Current status of VTE risk assessment and prevention using clinical decision support system: a cross-sectional survey from China

Current status of VTE risk assessment and prevention using clinical decision support system: a cross-sectional survey from China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Current status of VTE risk assessment and prevention using clinical decision support system: a cross-sectional survey from China Lei Xia, Kaiyuan Zhen, Zhaofei Chen, Rui Liang, Xiaomeng Zhang, and 27 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5008620/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aim: Venous thromboembolism (VTE) is an important cause of unexpected death in hospitalized patients. In recent years, Clinical Decision Support System (CDSS) has been increasingly adopted by hospitals worldwide. We conducted a survey with the aim of gaining a comprehensive understanding of the current state and future development direction of CDSS for VTE risk assessment and prevention(VTE-CDSS) in China. Methods: A network survey was conducted among hospitals in China. The investigation mainly included 39 questions, such as the implementation details of VTE-CDSS, the scale and the admission capacity of the hospitals. SPSS 20.0 software was used for statistical analysis. Results: A total of 587 hospitals responded to this survey, of which 194 (33.05%, 194/587) deployed VTE-CDSS, and less than a quarter (23.71%, 46/194) had Artificial intelligence(AI)-enabled VTE-CDSS. Among the 194 hospitals, the proportion of auxiliary decision support functions related to "risk assessment" was the highest (78.87%, 68.04%, 69.07%), followed by the auxiliary decision support functions related to "prophylaxis execution" (88.66%, 49.48%, 26.80%), and the proportion of auxiliary decision support functions related to "outcome event monitoring" was the lowest (46.39%, 22.68%). More than half of the respondents believed that the risk assessment rate and accuracy of the assessment had been significantly improved (56.19%, 109/194). However, only over one-third of respondents believed that the prevention rate and the standardization of prevention had been significantly improved (37.63%, 73/194). "The overall hospital information foundation is not perfect" was the primary hindrance factor in the implementation and application of VTE-CDSS (40.21%, 78/194). "System functions need to be further improved and more functional applications expanded" (78.35%, 152/194) is the most critical problem that VTE-CDSS needs to be further optimized and solved in the future. There were statistically significant differences between the two groups of hospitals that deployed VTE-CDSS with and without AI function (P<0.005) in the functional realization of various application details, the obstacles encountered in the implementation, and the problems to be further optimized and solved in the future. However, at the present stage in China, the efficiency and effectiveness of VTE-CDSS with AI function in risk assessment and implementation of prophylaxis measures are not significantly different from that of VTE-CDSS without AI function. (0.75<P<0.9). Conclusions: The information construction of VTE in China has developed rapidly. The implementation of VTE-CDSS achieved certain results, but there are still some obstacles and problems that need to be optimized in the future. Survey Venous thromboembolism (VTE) Clinical Decision Support System (CDSS) Artificial intelligence(AI) Introduction Venous thromboembolism (VTE), defined as deep vein thrombosis(DVT), pulmonary thromboembolism (PTE), or both, has become an important issue of patient safety worldwide. VTE is associated with higher morbidity and mortality among hospitalized patients in low-, middle- and high-income countries. Furthermore, VTE is still considered as one of the main economic burdens on healthcare systems. Without appropriate prophylaxis, up to 25–60% of patients who undergo surgery develop a DVT that is detectable by venography [ 1 ]; for patients undergoing orthopedic surgery, it is known that if appropriate prophylaxis is used, symptomatic DVT and PTE can be reduced by 55–60% [ 2 ]. In recent years, international authoritative academic institutions have issued a series of clinical practice guidelines on the risk assessment and prevention of VTE. These guidelines are available to help risk-stratify patients into low, moderate, or high risk for VTE and provide the corresponding prophylaxis measure, including drug prophylaxis, mechanical prophylaxis, or both [ 2 , 3 , 4 ]. Despite the widespread availability of evidence-based clinical practice guidelines on VTE prophylaxis, however, clinicians do not uniformly use existing risk-stratification tools and, when used, clinicians often use the tools incorrectly, producing an underestimation of a patient’s risk for VTE [ 5 , 6 ]. This underestimation leaves patients without adequate prophylaxis for VTE and significantly increases their risk for DVT or PTE. International multi-center research results show that the proportion of patients receiving appropriate VTE prevention in accordance with the guidelines is only 39.5–58.5% [ 7 ]. In China, only 9.0% of inpatients followed the guidelines for appropriate prevention, among which the rate of medical prevention was 6.0% and the rate of surgical prevention was 11.8% [ 8 ]. With the digital transformation of medicine and advances in health information technology, Clinical Decision Support Systems (CDSS) offer great promise to enhance efficiency and effectiveness of the prevention of VTE. A CDSS is rule or algorithm-based software that can analyze patient data and trigger timely, actionable, evidence based recommendations to healthcare teams to support clinical decisions [ 9 ]. Artificial intelligence (AI) technology has entered a phase of accelerated development in the 21st century, it has been used to solve medical problems in the fields of medical management, clinical decision support, patient monitoring, and health intervention. AI can be regarded as the emulation of human intelligence, which enables machines to learn from existing knowledge, adjust outputs, perform human-like tasks, and discover new knowledge. AI-based CDSS(AI + CDSS), which combines the knowledge reasoning technology of AI with the basic functional model of CDSS, has become a hot spot in the current medical field, and is considered to be an effective tool to improve the quality of medical care and enhance the work efficiency of clinicians [ 10 ]. In recent years, CDSS and AI + CDSS have been increasingly adopted by hospitals worldwide [ 11 – 13 ], including those in China [ 14 – 16 ], to support clinicians in VTE risk assessment and prevention. This implementation has yielded significant advancements in enhancing the accuracy and efficiency of risk assessment and prevention measures while concurrently reducing hospital-associated VTE incidents. In October 2018, the official launch of the National VTE Prevention Program in China marked a significant emphasis on the establishment of a comprehensive VTE prevention management system and the utilization of information technology for VTE risk assessment and prevention in patients. To date, approximately 1,500 hospitals nationwide have actively participated in this program. However, the specific application of CDSS and AI + CDSS for VTE risk assessment and prevention in various hospitals in China remains unknown, including system functionalities, utilization effectiveness, implementation barriers, and areas for optimization and resolution. Therefore, we conducted a survey with the aim of gaining a comprehensive understanding of the current state and future development direction of CDSS and AI + CDSS for VTE risk assessment and prevention in China. Our goal is to identify targeted solutions through this research. Materials and Methods Survey Questionnaire Development and Pretesting We consulted the existing literature on the application of CDSS for VTE risk assessment and prevention(VTE-CDSS), and integrate it with the current practical application situation in China. Collaborate with domestic experts in medical informatics (particularly those with research and practical experience in CDSS and AI), clinical specialists, healthcare management personnel, and information professionals to collectively develop the initial questionnaire. Select ten representative hospitals from central, eastern, western, and northeastern regions of China that include large tertiary hospitals, medium-sized tertiary hospitals, as well as grassroots secondary hospitals. After conducting a pilot survey on a small scale, revise and enhance the survey questionnaire based on feedback received to finalize the questionnaire. Contents of Survey Questionnaire The contents of the questionnaire are mainly application status and future implementation suggestions for VTE-CDSS, which are composed of the following five parts (see supplemental table for details): 1. Covering letter; 2. Basic information of the surveyed hospitals: including the name, level, category, form of ownership, location, etc.; 3. Application and implementation of VTE-CDSS: including the development of VTE risk assessment and prevention work in hospitals, the specific application and implementation details of VTE-CDSS, utilization effectiveness, implementation barriers, and areas for optimization and resolution; 4. Scale of the surveyed hospitals: including the number of beds, the number of doctors and the number of nurses; 5. Admission capacity of the surveyed hospitals: annual outpatient volume, annual discharge number, annual operation volume; 6. Basic information of the respondents: including age, gender, title, education, work department, etc. Survey Deployment From September to December 2023, a network survey (electronic questionnaire) was adopted and forwarded by the National VTE Prevention Program Office of China to each provincial VTE prevention regional alliance, and the investigation purpose, survey objects and survey time period were explained in detail. Then, the provincial VTE prevention regional alliance forwarded the local hospitals, and each hospital assigned a responsible person who was familiar with the VTE-CDSS of the hospital to fill in the questionnaire. Additionally, the National VTE Prevention Program Office of China, in collaboration with provincial VTE prevention regional alliances, sent at least 5 reminders through WeChat group announcements, emails, mobile phone text messages, and other communication channels to enhance the response rate of questionnaires. Survey Sample Size According to《Statistical Bulletin on the Development of Health Undertakings in China in 2022》[17]issued by the National Health Commission of the People's Republic of China, as of October 2023, there are a total of 14,668 hospitals in China, including 3,523 tertiary hospitals and 11,145 secondary hospitals. That is, the total N is 14,668. Based on the sample size calculation formula: n=u 2 α /2 π(1-π)/δ 2 , with a 95% confidence interval and α set at 0.05, u=1.96; According to previous literature, the proportion of hospitals in China implementing CDSS is approximately 15%, with an overall rate of π=0.15. If the allowable error is set at 0.03, then δ=0.03. The calculated sample size n is 544. The n/N ratio is below 0.05, which indicates that no correction to the sample size is necessary; therefore, the survey should include a minimum of 544 hospitals. Survey Quality Control Measures In the electronic questionnaire, we preseted a logical correlation between the questions and the specified mandatory responses. Prior to formal submission of the questionnaire, an automatic check for logical coherence and comprehensiveness was conducted. Before submission, respondents had the opportunity to review or modify their completed content. We cleaned and organized the collected survey questionnaires, and removed 13 test questionnaires. Considering the IP address and hospital name, 11 hospitals were screened for duplicate questionnaires, and after cleaning, only the most authoritative one was retained as a valid questionnaire for each hospital. The cleaning rules are as follows: 1. Delete those with shorter filling times, such as deleting those that are completed within 30 seconds and retaining those that are completed within 500 seconds; 2. Completely identical, only keep one copy; 3. If there is a significant difference in the contents of the duplicate answer sheet, confirm to retain one copy after contacting the respondents. Finally, a total of 611 questionnaires were collected in this survey. After excluding 13 test questionnaires and 11 repeated questionnaires, 587 effective questionnaires were obtained, with an effective rate of 96.07%. Calculations and Statistical Analysis Based on the survey results, we have summarized and statistically analyzed the demographic characteristics of the respondents (age, gender, professional title, education level, work department) and the basic information of the responding hospitals (economic region, grade, category and ownership); we calculated the proportion of responding hospitals performing VTE risk assessment and prevention, and the proportion of VTE-CDSS deployed in responding hospitals; we analyzed the functions and implementation details of VTE-CDSS in clinical practice; we also described the achievements, existing problems and obstacles of VTE-CDSS, and what needs to be further optimized. We used a Chi-square test to compare whether there were differences in hospital background variables (economic region, hospital grade and category, hospital scale, admission capacity, etc.) between the two groups of VTE-CDSS with or without AI capabilities. Wilcoxon rank sum test was used to compare whether the two groups of VTE-CDSS with or without AI function had differences in implementation details and system effectiveness. Statistical significance was determined as p <0.05. Statistical calculation was performed by SPSS20.0 (IBM Corp. Released 2011, IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.). Results Geographical and Demographics Characteristics The 587 valid questionnaires in this survey were sourced from 587 hospitals, of which 194 hospitals deployed VTE-CDSS (33.05%, 194/587). Therefore, the responses available for this survey were from 194 respondents in 194 hospitals across 27 provinces in China. The majority of respondents were aged from 31 to 40 years old (46.91%, 91/194), followed by 41 to 50 years old (31.96%, 62/194). The proportion of females (58.25%, 113/194) was slightly higher than that of males (41.75%, 81/194). The department they work in was mainly the medical affairs department (63.92%, 124/194), followed by the quality control department (15.46%, 30/194). The demographic characteristics of respondents included in this survey are detailed in Table 1 . Among the responding hospitals included in the analysis, a higher proportion (85.05%, 165/194) were tertiary hospitals compared to secondary hospitals, and general hospitals (95.36%, 185/194) outnumbered specialized hospitals. Furthermore, public hospitals (96.91%, 188/194) were more prevalent than private ones. Table 2 presents the geographical distribution and other fundamental characteristics of the participating hospitals. Among the 194 responding hospitals included in this analysis, less than a quarter (23.71%, 46/194) had AI-enabled VTE-CDSS. The basic characteristics of hospitals with and without AI function VTE-CDSS were statistically analyzed, and it was found that there were no statistically significant differences between the two groups of hospitals in terms of economic regional distribution, hospital category and ownership. However, there were significant statistical differences in hospital scale (grade, number of beds, number of doctors, number of nurses) and admission capacity (annual outpatient volume, annual discharge number, annual operation volume). The p-values are shown in Table 2 . Therefore, we can conclude that the larger the scale and stronger the admission capacity of hospitals, the higher the proportion of deploying AI-enabled VTE-CDSS. Table 1 Demographic characteristics of the respondents in this survey Gender N answers Proportion male 81 41.75% female 113 58.25% Age N answers Proportion 18 ~ 25 3 1.55% 26 ~ 30 22 11.34% 31 ~ 40 91 46.91% 41 ~ 50 62 31.96% 51 ~ 60 16 8.25% ≥ 60 0 0 Professional Title N answers Proportion senior 27 13.92% associate senior 56 28.87% intermediate 70 36.08% junior 41 21.13% Educational Background N answers Proportion bachelor 116 59.79% master 73 37.63% doctor 5 2.58% Work Department N answers Proportion neurosurgery 1 0.52% cardiology 3 1.55% orthopedics 3 1.55% information technology department 3 1.55% intensive care unit 1 0.52% department of interventional medicine 4 2.06% nursing department 8 4.12% vascular surgery 8 4.12% department of Pulmonary and Critical Care Medicine 9 4.64% quality control department 30 15.46% medical affairs department 124 63.92% Table 2 Characteristics of responding hospitals in this survey Characteristics Total (194) Hospitals with AI-enabled VTE-CDSS Hospitals with non-AI-enabled VTE-CDSS P-Value (46, 23.71%) (148, 76.29%) Economic region Eastern 94(48.45%) 26(56.52%) 68(45.95%) 0.25 ~ 0.75 Midlands 44(22.68%) 7(15.22%) 37(25.00%) Western 49(25.26%) 11(23.91%) 38(25.68%) Northeast 7(3.61%) 2(4.35%) 5(3.38%) Grade Secondary hospital 29(14.95%) 2(4.35%) 27(18.24%) 0.01 ~ 0.025 Tertiary hospital 165(85.05%) 44(95.65%) 121(81.76%) Category General hospital 185(95.36%) 43(93.48%) 142(95.95%) 0.25 ~ 0.75 Specialized hospital 9(4.64%) 3(6.52%) 6(4.05%) Ownership Public 188(96.91%) 45(97.83%) 143(96.62%) 0.25 ~ 0.75 Private 6(3.09%) 1(2.17%) 5(3.38%) Actual opening beds < 500 45(23.20%) 6(13.04%) 39(26.35%) 0.005 ~ 0.01 500 ~ 999 53(27.32%) 7(15.22%) 46(31.08%) 1000 ~ 1499 28(14.43%) 12(26.09%) 16(10.81%) 1500 ~ 1999 56(28.87%) 16(34.78%) 40(27.03%) ≥ 2000 12(6.19%) 5(10.87%) 7(4.73%) Number of doctors < 100 16(8.25%) 1(2.17%) 15(10.14%) < 0.005 100 ~ 199 32(16.49%) 5(10.87%) 27(18.24%) 200 ~ 499 76(39.18%) 10(21.74%) 66(44.59%) 500 ~ 999 25(12.89%) 8(17.39%) 17(11.49%) ≥ 1000 45(23.20%) 22(47.83%) 23(15.54%) Number of nurses < 500 30(15.46%) 5(10.87%) 25(16.89%) 0.005 ~ 0.01 500 ~ 999 75(38.66%) 9(19.57%) 66(44.59%) 1000 ~ 1499 36(18.56%) 14(30.43%) 22(14.86%) 1500 ~ 1999 30(15.46%) 11(23.91%) 19(12.84%) ≥ 2000 23(11.86%) 7(15.22%) 16(10.81%) Outpatient quantity per year < 100,000 48(24.74%) 7(15.22%) 41(27.70%) 0.025 ~ 0.05 100,000 to 500,000 51(26.29%) 8(17.39%) 43(29.05%) 500,000 to 1 million 24(12.37%) 10(21.74%) 14(9.46%) 1 million to 2 million 57(29.38%) 17(36.96%) 40(27.03%) ≥ 2 million 14(7.22%) 4(8.70%) 10(6.76%) Discharged patients’ quantity per year < 20,000 21(10.82%) 3(6.52%) 18(12.16%) < 0.005 20,000 to 50,000 32(16.49%) 6(13.04%) 26(17.57%) 50,000 to 100,000 27(13.92%) 8(17.39%) 19(12.84%) 100,000 to 200,000 72(37.11%) 9(19.57%) 63(42.57%) ≥ 200,000 42(21.65%) 20(43.48%) 22(14.86%) Surgery quantity per year < 10,000 50(25.77%) 8(17.39%) 42(28.38%) 0.005 ~ 0.01 10,000 to 20,000 53(27.32%) 6(13.04%) 47(31.76%) 20,000 to 50,000 57(29.38%) 18(39.13%) 39(26.35%) 50,000 to 100,000 24(12.37%) 10(21.74%) 14(9.46%) ≥ 100,000 10(5.15%) 4(8.70%) 6(4.05%) Legend: All answers are reported as N (proportion in %). VTE Venous thromboembolism, CDSS Clinical Decision Support System, AI Artificial intelligence. Implementation details of VTE-CDSS In order to understand the specific functions of VTE-CDSS in China, we conducted statistical analysis on the application details at three aspects: CDSS assisted risk assessment, CDSS assisted prophylaxis implementation, and CDSS assisted endpoint event monitoring, as shown in Table 3 . Among the 194 responding hospitals included in this analysis, the proportion of auxiliary decision support functions related to "risk assessment" was the highest (78.87%, 68.04%, 69.07%), followed by the auxiliary decision support functions related to "prophylaxis execution" (88.66%, 49.48%, 26.80%), and the proportion of auxiliary decision support functions related to "outcome event monitoring" was the lowest (46.39%, 22.68%). Among them, "the system can recommend prophylaxis measures based on VTE risk and bleeding risk" (88.66%, 172/194) achieved the highest proportion of VTE-CDSS in 194 hospitals, while "the system can automatically identify cases without adequate prophylaxis for VTE and give early warning " (26.80%, 52/194) and "the system can automatically screen bleeding events related to VTE prevention or treatment " (22.68%, 44/194) achieved a relatively low proportion in the VTE-CDSS of 194 hospitals. For hospitals that have deployed VTE-CDSS with and without AI function, statistical analysis was conducted on the implementation details of the above applications. Significant statistical differences were found between the two groups of hospitals, with p-values shown in Table 3 . Therefore, we can conclude that VTE-CDSS with AI function is superior to VTE-CDSS without AI function in terms of functional realization of various application details. Table 3 Implementation details of VTE-CDSS Implementation details of VTE-CDSS Total (194) Hospitals with AI-enabled VTE-CDSS Hospitals with non-AI-enabled VTE-CDSS P-Value (46, 23.71%) (148, 76.29%) Implementation details of CDSS assisted risk assessment < 0.005 The system can automatically assess the risk of VTE and give early warning to intermediate and high risk cases of VTE 153(78.87%) 45(97.83%) 108(72.97%) The system can automatically assess the bleeding risk and alert high-risk cases of bleeding 132(68.04%) 43(93.48%) 89(60.14%) The system can automatically identify key dynamic time points and provide warning prompts for unassessed cases 134(69.07%) 43(93.48%) 91(61.49%) Implementation details of CDSS assisted prophylaxis measures The system can recommend prophylaxis measures based on VTE risk and bleeding risk 172(88.66%) 43(93.48%) 129(87.16%) The system can automatically identify cases where prophylaxis measures have not been taken and provide warning prompts 96(49.48%) 28(60.87%) 68(45.95%) The system can automatically identify cases without adequate prophylaxis for VTE and give early warning 52(26.80%) 23(50.00%) 29(19.59%) Implementation details of CDSS assisted outcome event monitoring The system can automatically screen hospital-associated VTE events 90(46.39%) 31(67.39%) 59(39.86%) The system can automatically screen bleeding events related to VTE prevention or treatment 44(22.68%) 18(39.13%) 26(17.57%) Legend: All answers are reported as N (proportion in %). VTE Venous thromboembolism, CDSS Clinical Decision Support System, AI Artificial intelligence. Implementation status of VTE-CDSS assisted in monitoring quality indicators This survey conducted a statistical analysis of the VTE-CDSS assisted monitoring of three types of core quality indicators (including risk assessment indicators, prevention indicators, and outcome indicators), as shown in Table 4 . Among the 194 responding hospitals included in this analysis, the proportion of VTE-CDSS capable of auxiliary monitoring of risk assessment indicators was the highest (82.47%-96.39%), followed by prevention indicators (87.63%-91.24%), and the proportion of auxiliary monitoring of outcome indicators was the lowest (53.61%-86.08%). Among the three types of core quality indicators, the degree of auxiliary monitoring of "intrinsic quality indicators" is relatively low, such as the accuracy of risk assessment (34.02% of hospitals, 66/194), the standardization of prophylaxis (37.63% of hospitals, 73/194), and the standardized treatment rate of hospital-associated VTE (22.68% of hospitals, 73/194). There were significant statistical differences in the implementation of auxiliary monitoring functions for three core quality indicators between two groups of hospitals deployed with and without AI enabled VTE-CDSS, with p-values shown in Table 4 . Therefore, we can conclude that VTE-CDSS with AI function is superior to VTE-CDSS without AI function in achieving auxiliary monitoring functions of each core quality index. Table 4 Implementation status of VTE-CDSS assisted in monitoring quality indicators Implementation status of VTE-CDSS assisted in monitoring quality indicators Total (194) Hospitals with AI-enabled VTE-CDSS Hospitals with non-AI-enabled VTE-CDSS P-Value (46, 23.71%) (148, 76.29%) Risk assessment indicators which the VTE-CDSS can assisted in monitoring VTE risk assessment rate 187(96.39%) 46(100.00%) 141(95.27%) < 0.005 Proportion of moderate to high risk of VTE 183(94.33%) 46(100.00%) 137(92.57%) Bleeding risk assessment rate 184(94.85%) 46(100.00%) 138(93.24%) Proportion of high risk of bleeding 160(82.47%) 45(97.83%) 115(77.70%) Accuracy of risk assessment* 66(34.02%) 21(45.65%) 45(30.41%) Prevention indicators which the VTE-CDSS can assisted in monitoring Drug prophylaxis rate 177(91.24%) 46(100.00%) 131(88.51%) < 0.005 Mechanical prophylaxis rate 176(90.72%) 45(97.83%) 131(88.51%) Combined prophylaxis rate 170(87.63%) 45(97.83%) 125(84.46%) Standardization of prophylaxis * 73(37.63%) 23(50.00%) 50(33.78%) Outcome indicators which the VTE-CDSS can assisted in monitoring Incidence of hospital-associated VTE 167(86.08%) 43(93.48%) 124(83.78%) < 0.005 Standardized treatment rate of hospital-associated VTE * 44(22.68%) 31(67.39%) 13(8.78%) Incidence of bleeding events 104(53.61%) 33(71.74%) 71(47.97%) Mortality of hospital-associated VTE 164(84.54%) 45(97.83%) 119(80.41%) Legend: All answers are reported as N (proportion in %). VTE Venous thromboembolism, CDSS Clinical Decision Support System, AI Artificial intelligence. *: intrinsic quality indicators The efficiency and effectiveness of using VTE-CDSS In this survey, participants were consulted for their opinions and evaluations on the effectiveness of VTE-CDSS in clinical practice, as shown in Table 5 . Among the 194 respondents included in this analysis, nearly 3/4 of them believed that VTE-CDSS had "perfect system functions, and high enthusiasm for use by medical personnel" (73.71%, 143/194). Among responders with AI enabled VTE-CDSS deployed in their hospital, the highest proportion (82.61%, 38/46) believed that "perfect system functions, and high enthusiasm for use by medical personnel". However, hospitals that deploy AI-enabled VTE-CDSS had a higher demand for vendor intervention support compared to hospitals with non-AI-enabled VTE-CDSS (21.74% vs. 18.24%). More than half of the respondents believed that the risk assessment rate and accuracy of the assessment had been significantly improved (56.19%, 109/194). However, only over one-third of respondents believed that the prevention rate and the standardization of prevention had been significantly improved (37.63%, 73/194). There was no significant statistical difference in the efficiency and effectiveness of risk assessment and implementation of prophylaxis measures between two groups of hospitals deployed with and without AI enabled VTE-CDSS, and the P-values are shown in Table 5 . Therefore, we can conclude that at the present stage in China, the efficiency and effectiveness of VTE-CDSS with AI function in risk assessment and implementation of prophylaxis measures are not significantly different from that of VTE-CDSS without AI function. Although VTE-CDSS with AI function is more complete in system functionality and easier to accept and use by medical personnel than those without AI function, it may require more intervention and support from manufacturers in system debugging and integration. Table 5 The efficiency and effectiveness of using VTE-CDSS The efficiency and effectiveness of using VTE-CDSS Total (194) Hospitals with AI-enabled VTE-CDSS Hospitals with non-AI-enabled VTE-CDSS P-Value (46, 23.71%) (148, 76.29%) Evaluation of system function Perfect system functions, and high enthusiasm for use by medical personnel 143(73.71%) 38(82.61%) 105(70.95%) < 0.005 Perfect system functions, but the number of users is small 24(12.37%) 5(10.87%) 19(12.84%) Incomplete system functions, and the number of users is not large 13(6.70%) 2(4.35%) 11(7.43%) System administrator intervention is often required 48(24.74%) 8(17.39%) 40(27.03%) Vender intervention is often required 37(19.07%) 10(21.74%) 27(18.24%) Efficiency and effectiveness——Risk assessment The risk assessment rate and accuracy of the assessment have been significantly improved (the improvement rate is more than 80%) 109(56.19%) 27(58.70%) 82(55.41%) 0.75 ~ 0.9 The risk assessment rate and accuracy of the assessment have been significantly improved (the improvement rate is more than 30%) 59(30.41%) 12(26.09%) 47(31.76%) The assessment rate has improved, but the accuracy of the assessment has not 22(11.34%) 6(13.04%) 16(10.81%) Effect uncertainty 4(2.06%) 1(2.17%) 3(2.03%) Efficiency and effectiveness——Prophylaxis The prevention rate and the standardization of prevention have been significantly improved (the improvement rate is more than 80%) 73(37.63%) 20(43.48%) 53(35.81%) 0.75 ~ 0.9 The prevention rate and the standardization of prevention have been improved to some extent (the improvement rate is more than 30%) 87(44.85%) 19(41.30%) 68(45.95%) The prevention rate has improved, but the standardization of prevention has not 28(14.43%) 6(13.04%) 22(14.86%) Effect uncertainty 6(3.09%) 1(2.17%) 5(3.38%) Legend: All answers are reported as N (proportion in %). VTE Venous thromboembolism, CDSS Clinical Decision Support System, AI Artificial intelligence. Barriers in using VTE-CDSS This survey conducted a statistical analysis on the obstacles existing in the implementation of VTE-CDSS, as shown in Table 6 . Among them, "the overall hospital information foundation is not perfect" accounted for the highest proportion (40.21%, 78/194), followed by "VTE-CDSS lacks effective suggestions of prophylaxis measures to guide clinical practice" (34.02%, 66/194), "VTE-CDSS has poor compatibility" (30.93%, 60/194), "the informationization process between doctors and nurses is not smooth" (24.74%, 48/194), etc. It is worth noting that the following hindrance factors, such as "changing the work habits of doctors and nurses, increasing workload and affecting work efficiency", "excessive warning and prompt information interferes with normal clinical work", "doctors and nurses overly rely on VTE-CDSS, losing their independent thinking ability and clinical autonomy, inducing doctor-patient conflicts and affecting doctor-patient relationships", "warning and prompt information is useless or even incorrect", although the proportion is not high, it indicates that the system function of VTE-CDSS urgently needs to be closely adapted to clinical practice and further optimized. There was a significant statistical difference between the two groups of hospitals that deployed VTE-CDSS with and without AI function in the obstacles encountered in the implementation, and the P-values were shown in Table 6 . The proportion of most hindrance factors that occur in hospitals with AI-enabled function VTE-CDSS is higher than that in hospitals with non-AI-enabled VTE-CDSS. However, the proportion of these three obstacles, such as "the content of knowledge base is not updated in a timely manner and lacks authority", "doctors and nurses overly rely on VTE-CDSS, losing their independent thinking ability and clinical autonomy, inducing doctor-patient conflicts and affecting doctor-patient relationships", "warning and prompt information is useless or even incorrect", appearing in hospitals with AI-enabled VTE-CDSS is higher than that in hospitals with non-AI-enabled VTE-CDSS. Table 6 Barriers in using VTE-CDSS Barriers in using VTE-CDSS Total Hospitals with AI-enabled VTE-CDSS Hospitals with non-AI-enabled VTE-CDSS P-Value (194) (46, 23.71%) (148, 76.29%) The overall hospital information foundation is not perfect 78(40.21%) 14(30.43%) 64(43.24%) < 0.005 VTE-CDSS lacks effective suggestions of prophylaxis measures to guide clinical practice 66(34.02%) 14(30.43%) 52(35.14%) VTE-CDSS has poor compatibility 60(30.93%) 11(23.91%) 49(33.11%) The informationization process between doctors and nurses is not smooth 48(24.74%) 11(23.91%) 37(25.00%) The content of knowledge base is not updated in a timely manner and lacks authority 44(22.68%) 11(23.91%) 33(22.30%) VTE-CDSS costs too much 42(21.65%) 9(19.57%) 33(22.30%) Changing the work habits of doctors and nurses, increasing workload and affecting work efficiency 34(17.53%) 6(13.04%) 28(18.92%) Excessive warning and prompt information interferes with normal clinical work 31(15.98%) 7(15.22%) 24(16.22%) Doctors and nurses overly rely on VTE-CDSS, losing their independent thinking ability and clinical autonomy, inducing doctor-patient conflicts and affecting doctor-patient relationships 17(8.76%) 5(10.87%) 12(8.11%) Warning and prompt information is useless or even incorrect 8(4.12%) 2(4.35%) 6(4.05%) Legend: All answers are reported as N (proportion in %). VTE Venous thromboembolism, CDSS Clinical Decision Support System, AI Artificial intelligence. Problems to be further optimized and solved in the future in the implementation of VTE-CDSS This survey conducted a statistical analysis on the problems that VTE-CDSS needs to be further optimized and solved in the future, as shown in Table 7 . Among the 194 respondents included in this analysis, over 70% believed that "system functions need further improvement and more functional applications need to be expanded" (78.35%, 152/194), and "Clinical pathways for VTE risk assessment and prevention need to be smoothly integrated with CDSS" (73.20%, 142/194). Compared to hospitals with AI-enabled VTE-CDSS, hospitals with non-AI-enabled VTE-CDSS had more urgent needs for the above two items (81.08% vs 69.57%, 74.32% vs 69.57%). More than 50% of respondents believed that "data quality and availability need to be further improved", "VTE-CDSS construction standards and application management policies need to be developed", "AI algorithms, such as interpretability, transparency, algorithm adaptability, etc., need to be further improved", "unified VTE dataset standards need to be established and promoted nationwide", "more user-friendly human-computer interaction interfaces" are all issues that need to be further optimized and solved in the future. As for the problems that need to be further optimized and solved in the future, there was a significant statistical difference between two groups of hospitals that have deployed VTE-CDSS with and without AI function, and the P-values were shown in Table 7 . Table 7 Problems to be further optimized and solved in the future Problems to be further optimized and solved in the future Total Hospitals with AI-enabled VTE-CDSS Hospitals with non-AI-enabled VTE-CDSS P-Value (194) (46, 23.71%) (148, 76.29%) System functions need further improvement and more functional applications need to be expanded 152(78.35%) 32(69.57%) 120(81.08%) < 0.005 Clinical pathways for VTE risk assessment and prevention need to be smoothly integrated with CDSS 142(73.20%) 32(69.57%) 110(74.32%) Data quality and availability need to be further improved 114(58.76%) 29(63.04%) 84(56.76%) VTE-CDSS construction standards and application management policies need to be developed 108(55.67%) 28(60.87%) 80(54.05%) AI algorithms, such as interpretability, transparency, algorithm adaptability, etc., need to be further improved 106(54.64%) 30(65.22%) 76(51.35%) Unified VTE dataset standards need to be established and promoted nationwide 105(54.12%) 29(63.04%) 76(51.35%) More user-friendly human-computer interaction interfaces 99(51.03%) 29(63.04%) 70(47.30%) Increase objective data on user usage 81(41.75%) 21(45.65%) 60(40.54%) Reduce system implementation workload 72(37.11%) 18(39.13%) 54(36.49%) Legend: All answers are reported as N (proportion in %). VTE Venous thromboembolism, CDSS Clinical Decision Support System, AI Artificial intelligence. Discussion With the promotion of the National VTE Prevention Program in China, the information construction of VTE has developed rapidly in recent years. Especially since 2018, the number of hospitals deploying VTE-CDSS has increased significantly, and the system functions have been gradually shifted from basic risk assessment computerization to more in-depth and diverse functions such as process quality control and monitoring, and AI-assisted clinical decision-making. The implementation of VTE-CDSS in China has achieved certain results, but there are still some obstacles and problems that need to be optimized in the future. Improving system functionality to adapt to clinical pathways for VTE risk assessment and prevention The biggest challenge faced by VTE-CDSS in the implementation process of this survey is to further improve system functionality to align with the clinical pathway of VTE risk assessment and prevention. This is consistent with the issue raised by other investigations in China [ 18 ], which suggests that there may be a mismatch between VTE-CDSS system functionality and clinical business needs, making it difficult to translate user needs into system functionality design and closely align with clinical practice. This is also one of the main obstacles faced by VTE-CDSS in the process of system implementation. Therefore, it is recommended that throughout the entire lifecycle of VTE-CDSS (design, development, implementation, operation and optimization), medical professionals and R&D personnel jointly participate in the creation of CDSS system solutions[ 19 ], taking more consideration of the application scope and service scenarios of the system, combining with hospital reality and clinical pathways of VTE risk assessment and prevention, closely communicating with medical professionals, and integrating the true needs of users and the pain points and difficulties of clinical business into the development of the system, so as to improve the clinical fit of the system. On the basis of breaking through the technical barriers, the standardized VTE-CDSS computerized clinical path is promoted from top to bottom, truly improving medical quality and clinical efficiency. Deeply understand and implement the "Five Correct" principles of VTE-CDSS Although the application of VTE-CDSS in hospitals in China is not yet widespread (33.05%, 194/587), medical personnel have recognized and appreciated its potential benefits and possible challenges in the future. In this survey, the functional implementation of VTE-CDSS is mainly focused on assisting risk assessment and implementing prophylaxis measures. However, at the same time, we also concerned that the lack of effective prophylaxis measures to guide clinical practice is the second major obstacle to VTE-CDSS. A small number of respondents also mentioned that VTE-CDSS had "too much warning information, interfering with normal clinical work", and "warning information is useless or even incorrect". The implementation of CDSS should follow the "Five Corrects" principle [ 20 ]: providing the right information to the right people through the right channels, at the right time and in the right intervention mode in the diagnosis and treatment process. The clinical expertise involved in VTE-CDSS is relatively intensive, which requires the R&D team to deeply understand what is the right time, who is the right person, and what prophylaxis measure recommendations are correct and effective information in combination with the clinical business process of VTE risk assessment and prevention. More research and practice is still needed to seamlessly integrate CDSS into clinical business processes for VTE risk assessment and prevention, where, when and in what intervention mode, which can effectively improve the execution of prophylaxis measures, reduce unnecessary information interference, and minimize the burden of system response. While dynamically integrating a comprehensive and evidence-based medical knowledge base, VTE-CDSS needs to conduct medical logical training and conditional weight analysis training tailoring to the clinical characteristics of VTE risk assessment and prevention. This enables CDSS to truly understand medical records and VTE prophylaxis guidelines, accurately grasp the "Five Correct" implementation principles of CDSS, and empower medical personnel with a higher level of risk assessment and prevention capabilities. Promote the application of VTE standard datasets to improve data quality and availability "Data quality and availability", which has attracted more and more global attention, is also a key issue that all hospitals in this survey believe that VTE-CDSS needs to be further optimized and solved in the future. The risk assessment and prevention of VTE involve a wide range of patients, covering almost all inpatient departments in the hospital. However, VTE risk assessment and prevention need to comprehensively consider patient factors, underlying diseases, concomitant medications, blood coagulation function and invasive operations, etc. [ 21 ]. These key medical information are scattered throughout various information systems within the hospital. Currently, the information systems within a hospital in China are often developed by multiple different software vendors. Systems developed by different suppliers have large structural differences in their technical architecture and data, and hospitals generate massive amounts of operation data and disease data every day, resulting in the integrated application of VTE-CDSS, which requires a significant investment of manpower and financial resources for data cleaning, governance, and standardization. On the other hand, in order to achieve a high degree of accuracy in assisted decision-making, AI-enabled VTE-CDSS often needs to conduct cross-center and cross-regional data collection to obtain sufficient evidence-based medicine sample data to support machine learning, which also puts higher requirements on the standardization, structuring and unification of data models [ 22 ]. To change this situation, it is urgent to establish a VTE standard dataset based on VTE's own disease characteristics and combined with existing industry standards, so as to lay a technical foundation for future VTE clinical normative management, real-world research, and nationwide quality control. Based on this, the National VTE Prevention Program Office of China organized experts in relevant fields to integrate existing terminology norms, clinical practice guidelines and expert consensus from the perspective of data management, conducted a comprehensive and systematic collection of VTE-related data elements, and compiled and published the "Venous Thromboembolism Standard Dataset" in January 2023. We look forward to the nationwide promotion and application of the " Venous Thromboembolism Standard Dataset " to effectively promote the quality control of VTE and the integration and utilization of clinical data resources, standardize the management of VTE data in China, provide data support for the application of AI-based VTE-CDSS, and promote the standardized and homogeneous development of VTE prophylaxis and treatment system construction in China. Improve the transparency and interpretability of VTE-CDSS related AI algorithms In recent years, the application of AI-based CDSS in the VTE field has become increasingly active. In this survey, although the number of hospitals deploying AI-enabled VTE-CDSS was relatively small (7.84%, 46/587), more than half of the surveyed hospitals (54.64%, 106/194) are highly concerned about the interpretability and transparency of AI algorithms. This will be another important challenge for AI-based VTE-CDSS in the future. In healthcare, the interpretability and transparency of AI is particularly important. Both patients and medical staff are not solely satisfied with the medical outcomes of AI output, but are also eager to understand the reasons behind making clinical decisions. Healthcare workers' understanding of how AI algorithms work, AI-prompted risk assessment and prophylaxis decision information affect their trust in the system. However, many machine learning methods ("black boxes") lack transparency, which may undermine the trust of healthcare professionals in AI output results [23,24]. To solve these problems, the R&D team needs to consider how to open the "black box" of AI algorithms, such as developing interpretable AI algorithms or interactive human-computer dialogue software, so that medical personnel can understand the logic behind a clinical decision suggestion and judge its accuracy [ 19 , 25 ]. In addition, AI-based VTE-CDSS should proactively provide medical personnel with the details of risk assessment and prevention decisions, as well as relevant evidence-based medical evidence, such as source information support for patients' VTE risk factors and clinical evidence behind VTE prophylaxis measures recommendations, so that medical personnel can quickly understand the basis for clinical decision-making in a short time. This may be more favored by healthcare professionals than other software products with equal accuracy. Strengths and Potential Limitations In summary, this survey on the application of CDSS for VTE risk assessment and prevention in China has two advantages. Firstly, to our knowledge, this is the first study to investigate the system functionality, effectiveness, and barriers to implementation of VTE-CDSS, and to evaluate the differences between deploying VTE-CDSS with and without AI capabilities. Our investigation has received widespread response and acceptance in China. Secondly, 79.38% of the respondents in our survey were administrative personnel from the medical affairs department or quality control department, who were responsible for the overall deployment and specific implementation of VTE-CDSS in their hospitals, so they had a more comprehensive understanding of the application of VTE-CDSS in hospitals. This will help us to accurately grasp the current status and potential challenges of VTE-CDSS application and implementation in China. On the other hand, our online survey also has some potential limitations. Firstly, as we did not have a tracking system to distinguish responders from non-responders, we were unable to compare detailed information about those who were not included in the survey and their hospitals, so we did not analyze the impact of non-respondent bias. This non-response bias may have some adverse effects on the representativeness of survey results. Because the respondents were likely to have a special interest in VTE-CDSS, most respondents hold a positive attitude toward VTE-CDSS. Secondly, quantitative studies may not be sufficient to fully understand the current application status and implementation barriers of VTE-CDSS. In fact, focusing solely on technology while ignoring human factors may lead to further explanatory oversights. Considering the social attribute of this topic, a combination of qualitative and quantitative methods in research may provide additional understanding of this complex phenomenon, which we will consider in our future work. Conclusion The application of CDSS and AI + CDSS for VTE risk assessment and prevention, although not yet widespread in hospitals in China, has made certain progress and breakthroughs in the implementation details of auxiliary risk assessment and prophylaxis measures, auxiliary quality control and monitoring, etc. Its effectiveness in improving the risk assessment rate, the accuracy of risk assessment, the prevention rate and the standardization of prevention has also been recognized by most users. The popularization and promotion of VTE-CDSS in Chinese hospitals still need to overcome some external and internal obstacles, and further optimize and solve the key issues that users are concerned about. Although facing challenges in the future, there is still great potential and development space. Abbreviations AI Artificial intelligence AI+CDSS AI-based Clinical Decision Support Systems; AI-based CDSS CDSS Clinical Decision Support Systems DVT Deep vein thrombosis PTE Pulmonary thromboembolism VTE Venous thromboembolism VTE-CDSS CDSS for VTE risk assessment and prevention R&D Research and development Declarations Acknowledgements We thank all the professionals who offered precious suggestions to this study. We thank you survey participants for their valuable input and help in improving this study. We thank all the healthcare workers who contributed to the data collection and management. Authors’ contributions CJ and ZZ contributed to the research idea and study design. CS, YT, LJ, JX, YX, WW, LZ and WX developed and designed the initial questionnaire. JW, YY, ZL, YJ, JS, QY, GS, YG, NZ, ZC, LZ, and ZC participated in the pilot survey and provided critical questionnaire review. QG, MS and BL were involved in data acquisition. LX, KZ, ZC, RL and XZ performed the statistical analysis and data interpretation. XZ and DW directed the statistical methods. The first draft of the manuscript was written by LX and all authors commented on previous versions of the of the paper. CJ and ZZ had full access to all data in the study and verified the data and are responsible for the integrity and accuracy of the data and the decision to submit the manuscript. All authors revised the report and approved the final version before submission. Funding This study is supported by National High Level Hospital Clinical Research Funding (2022‑NHLHCRF‑LX‑01‑0108); the CAMS Innovation Fund for Medical Sciences (CIFMS) (2023-I2M-A-014); National Key Research and Development Program of China (2023YFC2507200). Availability of data and materials According to the Personal Information Protection Law of China, individual participant data in our study will not be made available publicly. The data from the National VTE Prevention Program in China, will be made available upon publication to members of the scientific and medical community for non-commercial use only, upon email request to [email protected] . Ethics approval and consent to participate This study was approved by the Clinical Research Ethical Committee of the science and technology center of China-Japan Friendship Hospital (approval number:2021-162-K120). All participants gave informed consent prior to taking the survey. Participation was voluntary and the data collected was nonidentifiable. Clinical trial number ： not applicable Competing interests The authors have no conflict of interest or financial relationships to disclose. No form of payment was given to anyone to produce the manuscript. References Geerts WH, Bergqvist D, Pineo GF, et al; American College of Chest Physicians. 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DOI:10.1001/jama.2023.22557. Silcox, C., Dentzer, S., Bates, D.W. AI-enabled clinical decision support software: A “trust and value checklist” for clinicians. NEJM Catalyst. 2020,1(6). DOI: https: //doi.org/10.1056/cat.20.0212. Additional Declarations No competing interests reported. Supplementary Files SupplementalTable.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5008620","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351523954,"identity":"e22f014c-ba89-4c33-a785-3b3ad5133034","order_by":0,"name":"Lei Xia","email":"","orcid":"","institution":"Medical Affairs Department of China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Xia","suffix":""},{"id":351523955,"identity":"fb3a415e-0dc6-487e-a9e7-c02a6f5fe5f8","order_by":1,"name":"Kaiyuan Zhen","email":"","orcid":"","institution":"Peking University China-Japan Friendship School of Clinical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kaiyuan","middleName":"","lastName":"Zhen","suffix":""},{"id":351523956,"identity":"2393fb67-eccc-4fd1-b88c-7e880d87d24e","order_by":2,"name":"Zhaofei Chen","email":"","orcid":"","institution":"Peking University China-Japan Friendship School of Clinical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhaofei","middleName":"","lastName":"Chen","suffix":""},{"id":351523957,"identity":"564af9c7-b23b-44bf-ac51-f37e66c0fd09","order_by":3,"name":"Rui Liang","email":"","orcid":"","institution":"Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Liang","suffix":""},{"id":351523958,"identity":"e9523fa4-c2ec-4bdb-a450-cb8463e9f861","order_by":4,"name":"Xiaomeng Zhang","email":"","orcid":"","institution":"Peking University China-Japan Friendship School of Clinical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaomeng","middleName":"","lastName":"Zhang","suffix":""},{"id":351523959,"identity":"80f091f6-0888-42d6-b714-62275d8f7c89","order_by":5,"name":"Qian Gao","email":"","orcid":"","institution":"The National VTE Prevention Program Office of China","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Gao","suffix":""},{"id":351523960,"identity":"5bad135e-0f59-4c3f-bdf4-fe2195066a0b","order_by":6,"name":"Mingwei Sheng","email":"","orcid":"","institution":"The National VTE Prevention Program Office of China","correspondingAuthor":false,"prefix":"","firstName":"Mingwei","middleName":"","lastName":"Sheng","suffix":""},{"id":351523961,"identity":"d104936b-45a6-48a2-a403-1528291187d8","order_by":7,"name":"Bing Liu","email":"","orcid":"","institution":"Hospital Office of China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Liu","suffix":""},{"id":351523962,"identity":"859912e6-8da8-49a3-93ed-b1e883683bc1","order_by":8,"name":"Jiefeng Xia","email":"","orcid":"","institution":"Department of information management, China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiefeng","middleName":"","lastName":"Xia","suffix":""},{"id":351523963,"identity":"a85515cb-7351-4056-9a63-cc930d0a3701","order_by":9,"name":"Chaozeng Si","email":"","orcid":"","institution":"Department of information management, China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chaozeng","middleName":"","lastName":"Si","suffix":""},{"id":351523964,"identity":"0dba5921-31b7-4441-9fa5-0b0cbfc8a431","order_by":10,"name":"Yanbi Tian","email":"","orcid":"","institution":"Department of information management, China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanbi","middleName":"","lastName":"Tian","suffix":""},{"id":351523965,"identity":"c8e43e93-fb04-46a0-a2ff-1a62b0ea9f31","order_by":11,"name":"Lurong Jia","email":"","orcid":"","institution":"Department of information management, China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lurong","middleName":"","lastName":"Jia","suffix":""},{"id":351523966,"identity":"82da641b-fdfe-4558-b7b2-e4ce6763e66c","order_by":12,"name":"Yaping Xu","email":"","orcid":"","institution":"Department of Nursing, China-Japan Friendship Hospital,","correspondingAuthor":false,"prefix":"","firstName":"Yaping","middleName":"","lastName":"Xu","suffix":""},{"id":351523967,"identity":"5845ad1e-021e-4e0c-9251-53ebc23506aa","order_by":13,"name":"Wei Wang","email":"","orcid":"","institution":"Department of Nursing, China-Japan Friendship Hospital,","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":351523968,"identity":"d42ce001-ca0c-48d8-bbd1-0994c4ae8455","order_by":14,"name":"Lintao Zhong","email":"","orcid":"","institution":"Medical Affairs Department of China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lintao","middleName":"","lastName":"Zhong","suffix":""},{"id":351523969,"identity":"8090a87c-3358-4b74-89cb-7bdc4a2cb9c4","order_by":15,"name":"Xianbo Zuo","email":"","orcid":"","institution":"Center of Science and technology, China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianbo","middleName":"","lastName":"Zuo","suffix":""},{"id":351523970,"identity":"b5e011cc-0b45-42db-979d-c7d3ce6abb17","order_by":16,"name":"Dingyi Wang","email":"","orcid":"","institution":"Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dingyi","middleName":"","lastName":"Wang","suffix":""},{"id":351523971,"identity":"5086b5df-34db-44db-8d65-c1d907b8e5ee","order_by":17,"name":"Wanmu Xie","email":"","orcid":"","institution":"Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wanmu","middleName":"","lastName":"Xie","suffix":""},{"id":351523972,"identity":"bdeabf37-d281-4638-93eb-6ccb5cac5281","order_by":18,"name":"Jun Wan","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Beijing Anzhen Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wan","suffix":""},{"id":351523973,"identity":"73404403-7812-4193-8d5b-8ab58a933691","order_by":19,"name":"Yuanhua Yang","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanhua","middleName":"","lastName":"Yang","suffix":""},{"id":351523974,"identity":"b5c5ab43-a3f1-4ee2-88e7-ce510052ad90","order_by":20,"name":"Zhihong Liu","email":"","orcid":"","institution":"Fuwai Hospital, Chinese Academy of Medical Science; National Center for Cardiovascular Diseases","correspondingAuthor":false,"prefix":"","firstName":"Zhihong","middleName":"","lastName":"Liu","suffix":""},{"id":351523975,"identity":"ef1f1607-1d28-4136-84f6-055969e930fa","order_by":21,"name":"Yingqun Ji","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital Affiliated by Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Yingqun","middleName":"","lastName":"Ji","suffix":""},{"id":351523976,"identity":"fb327d79-1ee3-49e6-8afc-b36d2431dadb","order_by":22,"name":"Juhong Shi","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Juhong","middleName":"","lastName":"Shi","suffix":""},{"id":351523977,"identity":"e2950565-1894-4d36-9458-40df1e99f7b7","order_by":23,"name":"Qun Yi","email":"","orcid":"","institution":"Sichuan Cancer Hospital, University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Qun","middleName":"","lastName":"Yi","suffix":""},{"id":351523978,"identity":"e29ffe51-aa67-4191-9f8b-b57cf7b2d985","order_by":24,"name":"Guochao Shi","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guochao","middleName":"","lastName":"Shi","suffix":""},{"id":351523979,"identity":"9c18a57c-fa5d-4948-a087-7f7c06f3d908","order_by":25,"name":"Yutao Guo","email":"","orcid":"","institution":"Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yutao","middleName":"","lastName":"Guo","suffix":""},{"id":351523980,"identity":"718ede2b-ced4-4fdc-96a9-41521b179eb9","order_by":26,"name":"Nuofu Zhang","email":"","orcid":"","institution":"National Center for Respiratory Medicine, Sleep Medicine Center, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nuofu","middleName":"","lastName":"Zhang","suffix":""},{"id":351523981,"identity":"d5e648ea-f6ed-44df-bb52-93660cb5c72d","order_by":27,"name":"Zhaozhong Cheng","email":"","orcid":"","institution":"Respiratory Department, The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Zhaozhong","middleName":"","lastName":"Cheng","suffix":""},{"id":351523982,"identity":"5916f9f9-1151-4697-97cd-8ba66d723cde","order_by":28,"name":"Ling Zhu","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Zhu","suffix":""},{"id":351523983,"identity":"5d19f657-3239-411b-a28d-288d287161c8","order_by":29,"name":"Zhe Cheng","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Cheng","suffix":""},{"id":351523984,"identity":"a3105e53-702d-4b05-a610-af22578dfeee","order_by":30,"name":"Cunbo Jia","email":"","orcid":"","institution":"Hospital Office of China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cunbo","middleName":"","lastName":"Jia","suffix":""},{"id":351523985,"identity":"7483bd67-955a-4101-af38-0f36237234a0","order_by":31,"name":"Zhenguo Zhai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie3Pv0sDMRTA8YSDuATnVwTjn/COLg7i3+GYEOgtUQouN0lLIS7ifKB4/4IiiOM7CnWpu+BSEZyv2xUEfxwiIty1o2C+UwLvQ/IYC4X+YsAY8UF95DONsCnWRrQyibDsb3fX5UQvJeyLiE5WpuYC3FarUGejp2Jxu3Og9ixtSITEM8dYld40En4+QepMe4fxQ093P8i+Z/fET6aPjSQCjRT7sbnKHNqa8FMdcd9MBCQlGf9Wk/Hnx0QksZVIcEiFJ5ODi4cZghZiCQFw/WLorbmUL5aVCLGXQhdtu6gsuZ4v/K7Jj+1dpV+PlMqfi1mVNpPvkH5cqGHo13ODlcZCoVDoP/YO9YFba+wHZl8AAAAASUVORK5CYII=","orcid":"","institution":"Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhenguo","middleName":"","lastName":"Zhai","suffix":""}],"badges":[],"createdAt":"2024-08-31 10:59:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5008620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5008620/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71162724,"identity":"4d57b51d-548b-4d9e-96d2-1da1033c4c9e","added_by":"auto","created_at":"2024-12-11 16:46:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1496280,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5008620/v1/45e8861e-e08d-464f-a4b5-3354bcd0b958.pdf"},{"id":66169841,"identity":"4d5ad7fc-1c13-429e-8b88-3765ca05be11","added_by":"auto","created_at":"2024-10-08 10:27:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24084,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-5008620/v1/313f2beaee1935607293cff3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Current status of VTE risk assessment and prevention using clinical decision support system: a cross-sectional survey from China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVenous thromboembolism (VTE), defined as deep vein thrombosis(DVT), pulmonary thromboembolism (PTE), or both, has become an important issue of patient safety worldwide. VTE is associated with higher morbidity and mortality among hospitalized patients in low-, middle- and high-income countries. Furthermore, VTE is still considered as one of the main economic burdens on healthcare systems. Without appropriate prophylaxis, up to 25\u0026ndash;60% of patients who undergo surgery develop a DVT that is detectable by venography [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]; for patients undergoing orthopedic surgery, it is known that if appropriate prophylaxis is used, symptomatic DVT and PTE can be reduced by 55\u0026ndash;60% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e In recent years, international authoritative academic institutions have issued a series of clinical practice guidelines on the risk assessment and prevention of VTE. These guidelines are available to help risk-stratify patients into low, moderate, or high risk for VTE and provide the corresponding prophylaxis measure, including drug prophylaxis, mechanical prophylaxis, or both [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite the widespread availability of evidence-based clinical practice guidelines on VTE prophylaxis, however, clinicians do not uniformly use existing risk-stratification tools and, when used, clinicians often use the tools incorrectly, producing an underestimation of a patient\u0026rsquo;s risk for VTE [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This underestimation leaves patients without adequate prophylaxis for VTE and significantly increases their risk for DVT or PTE. International multi-center research results show that the proportion of patients receiving appropriate VTE prevention in accordance with the guidelines is only 39.5\u0026ndash;58.5% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In China, only 9.0% of inpatients followed the guidelines for appropriate prevention, among which the rate of medical prevention was 6.0% and the rate of surgical prevention was 11.8% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith the digital transformation of medicine and advances in health information technology, Clinical Decision Support Systems (CDSS) offer great promise to enhance efficiency and effectiveness of the prevention of VTE. A CDSS is rule or algorithm-based software that can analyze patient data and trigger timely, actionable, evidence based recommendations to healthcare teams to support clinical decisions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Artificial intelligence (AI) technology has entered a phase of accelerated development in the 21st century, it has been used to solve medical problems in the fields of medical management, clinical decision support, patient monitoring, and health intervention. AI can be regarded as the emulation of human intelligence, which enables machines to learn from existing knowledge, adjust outputs, perform human-like tasks, and discover new knowledge. AI-based CDSS(AI\u0026thinsp;+\u0026thinsp;CDSS), which combines the knowledge reasoning technology of AI with the basic functional model of CDSS, has become a hot spot in the current medical field, and is considered to be an effective tool to improve the quality of medical care and enhance the work efficiency of clinicians [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, CDSS and AI\u0026thinsp;+\u0026thinsp;CDSS have been increasingly adopted by hospitals worldwide [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], including those in China [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], to support clinicians in VTE risk assessment and prevention. This implementation has yielded significant advancements in enhancing the accuracy and efficiency of risk assessment and prevention measures while concurrently reducing hospital-associated VTE incidents.\u003c/p\u003e \u003cp\u003eIn October 2018, the official launch of the National VTE Prevention Program in China marked a significant emphasis on the establishment of a comprehensive VTE prevention management system and the utilization of information technology for VTE risk assessment and prevention in patients. To date, approximately 1,500 hospitals nationwide have actively participated in this program. However, the specific application of CDSS and AI\u0026thinsp;+\u0026thinsp;CDSS for VTE risk assessment and prevention in various hospitals in China remains unknown, including system functionalities, utilization effectiveness, implementation barriers, and areas for optimization and resolution. Therefore, we conducted a survey with the aim of gaining a comprehensive understanding of the current state and future development direction of CDSS and AI\u0026thinsp;+\u0026thinsp;CDSS for VTE risk assessment and prevention in China. Our goal is to identify targeted solutions through this research.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eSurvey Questionnaire Development and Pretesting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe consulted the existing literature on the application of CDSS for VTE risk assessment and prevention(VTE-CDSS), and integrate it with the current practical application situation in China. Collaborate with domestic experts in medical informatics (particularly those with research and practical experience in CDSS and AI), clinical specialists, healthcare management personnel, and information professionals to collectively develop the initial questionnaire.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSelect ten representative hospitals from central, eastern, western, and northeastern regions of China that include large tertiary hospitals, medium-sized tertiary hospitals, as well as grassroots secondary hospitals. After conducting a pilot survey on a small scale, revise and enhance the survey questionnaire based on feedback received to finalize the questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContents of Survey Questionnaire\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe contents of the questionnaire are mainly application status and future implementation suggestions for VTE-CDSS, which are composed of the following five parts (see supplemental table for details):\u003c/p\u003e\n\u003cp\u003e1. Covering letter;\u003c/p\u003e\n\u003cp\u003e2. Basic information of the surveyed hospitals: including the name, level, category, form of ownership, location, etc.;\u003c/p\u003e\n\u003cp\u003e3. Application and implementation of VTE-CDSS: including the development of VTE risk assessment and prevention work in hospitals, the specific application and implementation details of VTE-CDSS,\u0026nbsp;utilization effectiveness, implementation barriers, and areas for optimization and resolution;\u003c/p\u003e\n\u003cp\u003e4. Scale of the surveyed hospitals: including the number of beds, the number of doctors and the number of nurses;\u003c/p\u003e\n\u003cp\u003e5. Admission capacity of the surveyed hospitals: annual outpatient volume, annual discharge number, annual operation volume;\u003c/p\u003e\n\u003cp\u003e6. Basic information of the respondents: including age, gender, title, education, work department, etc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvey Deployment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom September to December 2023, a network survey (electronic questionnaire) was adopted and forwarded by the National VTE Prevention Program Office of China to each provincial VTE prevention regional alliance, and the investigation purpose, survey objects and survey time period were explained in detail. Then, the provincial VTE prevention regional alliance forwarded the local hospitals, and each hospital assigned a responsible person who was familiar with the VTE-CDSS of the hospital to fill in the questionnaire.\u003c/p\u003e\n\u003cp\u003eAdditionally, the National VTE Prevention Program Office of China, in collaboration with provincial VTE prevention regional alliances, sent at least 5 reminders through WeChat group announcements, emails, mobile phone text messages, and other communication channels to enhance the response rate of questionnaires.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvey Sample Size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to《Statistical Bulletin on the Development of Health Undertakings in China in 2022》[17]issued by the National Health Commission of the People's Republic of China, as of October 2023, there are a total of 14,668 hospitals in China, including 3,523 tertiary hospitals and 11,145 secondary hospitals. That is, the total N is 14,668.\u003c/p\u003e\n\u003cp\u003eBased on the sample size calculation formula: n=u\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eα\u003c/sub\u003e\u003csub\u003e/2\u003c/sub\u003eπ(1-π)/δ\u003csup\u003e2\u003c/sup\u003e, with a 95% confidence interval and α set at 0.05, u=1.96; According to previous literature, the proportion of hospitals in China implementing CDSS is approximately 15%, with an overall rate of π=0.15. If the allowable error is set at 0.03, then δ=0.03. The calculated sample size n is 544.\u003c/p\u003e\n\u003cp\u003eThe n/N ratio is below 0.05, which indicates that no correction to the sample size is necessary; therefore, the survey should include a minimum of 544 hospitals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvey Quality Control Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the electronic questionnaire, we preseted a logical correlation between the questions and the specified mandatory responses. Prior to formal submission of the questionnaire, an automatic check for logical coherence and comprehensiveness was conducted. Before submission, respondents had the opportunity to review or modify their completed content.\u003c/p\u003e\n\u003cp\u003eWe cleaned and organized the collected survey questionnaires, and removed 13 test questionnaires. Considering the IP address and hospital name, 11 hospitals were screened for duplicate questionnaires, and after cleaning, only the most authoritative one was retained as a valid questionnaire for each hospital. The cleaning rules are as follows:\u003c/p\u003e\n\u003cp\u003e1. Delete those with shorter filling times, such as deleting those that are completed within 30 seconds and retaining those that are completed within 500 seconds;\u003c/p\u003e\n\u003cp\u003e2. Completely identical, only keep one copy;\u003c/p\u003e\n\u003cp\u003e3. If there is a significant difference in the contents of the duplicate answer sheet, confirm to retain one copy after contacting the respondents.\u003c/p\u003e\n\u003cp\u003eFinally, a total of 611 questionnaires were collected in this survey. After excluding 13 test questionnaires and 11 repeated questionnaires, 587 effective questionnaires were obtained, with an effective rate of 96.07%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculations and Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the survey results, we have summarized and statistically analyzed the demographic characteristics of the respondents (age, gender, professional title, education level, work department) and the basic information of the responding hospitals (economic region, grade, category and ownership); we calculated the proportion of responding hospitals performing VTE risk assessment and prevention, and the proportion of VTE-CDSS deployed in responding hospitals; we analyzed the functions and implementation details of VTE-CDSS in clinical practice; we also described the achievements, existing problems and obstacles of VTE-CDSS, and what needs to be further optimized.\u003c/p\u003e\n\u003cp\u003eWe used a Chi-square test to compare whether there were differences in hospital background variables (economic region, hospital grade and category, hospital scale, admission capacity, etc.) between the two groups of VTE-CDSS with or without AI capabilities. Wilcoxon rank sum test was used to compare whether the two groups of VTE-CDSS with or without AI function had differences in implementation details and system effectiveness. Statistical significance was determined as p \u0026lt;0.05. Statistical calculation was performed by SPSS20.0 (IBM Corp. Released 2011, IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGeographical and Demographics Characteristics\u003c/h2\u003e \u003cp\u003eThe 587 valid questionnaires in this survey were sourced from 587 hospitals, of which 194 hospitals deployed VTE-CDSS (33.05%, 194/587). Therefore, the responses available for this survey were from 194 respondents in 194 hospitals across 27 provinces in China. The majority of respondents were aged from 31 to 40 years old (46.91%, 91/194), followed by 41 to 50 years old (31.96%, 62/194). The proportion of females (58.25%, 113/194) was slightly higher than that of males (41.75%, 81/194). The department they work in was mainly the medical affairs department (63.92%, 124/194), followed by the quality control department (15.46%, 30/194). The demographic characteristics of respondents included in this survey are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAmong the responding hospitals included in the analysis, a higher proportion (85.05%, 165/194) were tertiary hospitals compared to secondary hospitals, and general hospitals (95.36%, 185/194) outnumbered specialized hospitals. Furthermore, public hospitals (96.91%, 188/194) were more prevalent than private ones. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the geographical distribution and other fundamental characteristics of the participating hospitals.\u003c/p\u003e \u003cp\u003eAmong the 194 responding hospitals included in this analysis, less than a quarter (23.71%, 46/194) had AI-enabled VTE-CDSS. The basic characteristics of hospitals with and without AI function VTE-CDSS were statistically analyzed, and it was found that there were no statistically significant differences between the two groups of hospitals in terms of economic regional distribution, hospital category and ownership. However, there were significant statistical differences in hospital scale (grade, number of beds, number of doctors, number of nurses) and admission capacity (annual outpatient volume, annual discharge number, annual operation volume). The p-values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTherefore, we can conclude that the larger the scale and stronger the admission capacity of hospitals, the higher the proportion of deploying AI-enabled VTE-CDSS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of the respondents in this survey\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN answers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN answers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eProportion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026thinsp;~\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026thinsp;~\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026thinsp;~\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026thinsp;~\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.96%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026thinsp;~\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfessional Title\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN answers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eProportion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eassociate senior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.87%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.08%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ejunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Background\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN answers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eProportion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWork Department\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN answers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eProportion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eneurosurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecardiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eorthopedics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einformation technology department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintensive care unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edepartment of interventional medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enursing department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evascular surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edepartment of Pulmonary and Critical Care Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003equality control department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emedical affairs department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of responding hospitals in this survey\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(194)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHospitals with AI-enabled VTE-CDSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHospitals with non-AI-enabled VTE-CDSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(46, 23.71%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(148, 76.29%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eEconomic region\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94(48.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(56.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68(45.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.25\u0026thinsp;~\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(22.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(25.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(25.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(23.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(25.68%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(3.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(4.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(3.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(14.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(4.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(18.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.01\u0026thinsp;~\u0026thinsp;0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165(85.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(95.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121(81.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCategory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185(95.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(93.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142(95.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.25\u0026thinsp;~\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialized hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(4.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(6.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(4.05%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOwnership\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188(96.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(97.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143(96.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.25\u0026thinsp;~\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(3.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(2.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(3.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eActual opening beds\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(23.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(13.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(26.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.005\u0026thinsp;~\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u0026thinsp;~\u0026thinsp;999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53(27.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46(31.08%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u0026thinsp;~\u0026thinsp;1499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(14.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(26.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(10.81%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1500\u0026thinsp;~\u0026thinsp;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(28.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(34.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(27.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(6.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(10.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(4.73%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of doctors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(8.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(2.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(10.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u0026thinsp;~\u0026thinsp;199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(16.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(10.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(18.24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u0026thinsp;~\u0026thinsp;499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76(39.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(21.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(44.59%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u0026thinsp;~\u0026thinsp;999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(12.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(17.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(11.49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(23.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(47.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(15.54%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of nurses\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(15.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(10.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(16.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.005\u0026thinsp;~\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u0026thinsp;~\u0026thinsp;999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(38.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(19.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(44.59%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u0026thinsp;~\u0026thinsp;1499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(18.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(30.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(14.86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1500\u0026thinsp;~\u0026thinsp;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(15.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(23.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(12.84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(11.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(10.81%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutpatient quantity per year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(24.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(27.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.025\u0026thinsp;~\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100,000 to 500,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(26.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(17.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(29.05%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500,000 to 1 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(12.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(21.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(9.46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026nbsp;million to 2 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(29.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(36.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(27.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(7.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(8.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(6.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDischarged patients\u0026rsquo; quantity per year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(10.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(6.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(12.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20,000 to 50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(16.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(13.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(17.57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50,000 to 100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(13.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(17.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(12.84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100,000 to 200,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(37.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(19.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63(42.57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;200,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(21.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(43.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(14.86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery quantity per year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(25.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(17.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42(28.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.005\u0026thinsp;~\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10,000 to 20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53(27.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(13.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(31.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20,000 to 50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(29.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(39.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(26.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50,000 to 100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(12.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(21.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(9.46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(5.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(8.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(4.05%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: All answers are reported as N (proportion in %). \u003cem\u003eVTE\u003c/em\u003e Venous thromboembolism, \u003cem\u003eCDSS\u003c/em\u003e Clinical Decision Support System, \u003cem\u003eAI\u003c/em\u003e Artificial intelligence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImplementation details of VTE-CDSS\u003c/h2\u003e \u003cp\u003eIn order to understand the specific functions of VTE-CDSS in China, we conducted statistical analysis on the application details at three aspects: CDSS assisted risk assessment, CDSS assisted prophylaxis implementation, and CDSS assisted endpoint event monitoring, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAmong the 194 responding hospitals included in this analysis, the proportion of auxiliary decision support functions related to \"risk assessment\" was the highest (78.87%, 68.04%, 69.07%), followed by the auxiliary decision support functions related to \"prophylaxis execution\" (88.66%, 49.48%, 26.80%), and the proportion of auxiliary decision support functions related to \"outcome event monitoring\" was the lowest (46.39%, 22.68%). Among them, \"the system can recommend prophylaxis measures based on VTE risk and bleeding risk\" (88.66%, 172/194) achieved the highest proportion of VTE-CDSS in 194 hospitals, while \"the system can automatically identify cases without adequate prophylaxis for VTE and give early warning \" (26.80%, 52/194) and \"the system can automatically screen bleeding events related to VTE prevention or treatment \" (22.68%, 44/194) achieved a relatively low proportion in the VTE-CDSS of 194 hospitals.\u003c/p\u003e \u003cp\u003eFor hospitals that have deployed VTE-CDSS with and without AI function, statistical analysis was conducted on the implementation details of the above applications. Significant statistical differences were found between the two groups of hospitals, with p-values shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Therefore, we can conclude that VTE-CDSS with AI function is superior to VTE-CDSS without AI function in terms of functional realization of various application details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImplementation details of VTE-CDSS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eImplementation details of VTE-CDSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHospitals with AI-enabled VTE-CDSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHospitals with non-AI-enabled VTE-CDSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(46, 23.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(148, 76.29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImplementation details of CDSS assisted risk assessment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe system can automatically assess the risk of VTE and give early warning to intermediate and high risk cases of VTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153(78.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(97.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108(72.97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe system can automatically assess the bleeding risk and alert high-risk cases of bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132(68.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(93.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89(60.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe system can automatically identify key dynamic time points and provide warning prompts for unassessed cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134(69.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(93.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91(61.49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImplementation details of CDSS assisted prophylaxis measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe system can recommend prophylaxis measures based on VTE risk and bleeding risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172(88.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(93.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129(87.16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe system can automatically identify cases where prophylaxis measures have not been taken and provide warning prompts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96(49.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(60.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68(45.95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe system can automatically identify cases without adequate prophylaxis for VTE and give early warning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52(26.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(50.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(19.59%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImplementation details of CDSS assisted outcome event monitoring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe system can automatically screen hospital-associated VTE events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(46.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(67.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59(39.86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe system can automatically screen bleeding events related to VTE prevention or treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(22.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(39.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(17.57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: All answers are reported as N (proportion in %). \u003cem\u003eVTE\u003c/em\u003e Venous thromboembolism, \u003cem\u003eCDSS\u003c/em\u003e Clinical Decision Support System, \u003cem\u003eAI\u003c/em\u003e Artificial intelligence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImplementation status of VTE-CDSS assisted in monitoring quality indicators\u003c/h2\u003e \u003cp\u003eThis survey conducted a statistical analysis of the VTE-CDSS assisted monitoring of three types of core quality indicators (including risk assessment indicators, prevention indicators, and outcome indicators), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAmong the 194 responding hospitals included in this analysis, the proportion of VTE-CDSS capable of auxiliary monitoring of risk assessment indicators was the highest (82.47%-96.39%), followed by prevention indicators (87.63%-91.24%), and the proportion of auxiliary monitoring of outcome indicators was the lowest (53.61%-86.08%). Among the three types of core quality indicators, the degree of auxiliary monitoring of \"intrinsic quality indicators\" is relatively low, such as the accuracy of risk assessment (34.02% of hospitals, 66/194), the standardization of prophylaxis (37.63% of hospitals, 73/194), and the standardized treatment rate of hospital-associated VTE (22.68% of hospitals, 73/194).\u003c/p\u003e \u003cp\u003eThere were significant statistical differences in the implementation of auxiliary monitoring functions for three core quality indicators between two groups of hospitals deployed with and without AI enabled VTE-CDSS, with p-values shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Therefore, we can conclude that VTE-CDSS with AI function is superior to VTE-CDSS without AI function in achieving auxiliary monitoring functions of each core quality index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImplementation status of VTE-CDSS assisted in monitoring quality indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eImplementation status of VTE-CDSS\u003c/p\u003e \u003cp\u003eassisted in monitoring quality indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(194)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHospitals with AI-enabled VTE-CDSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHospitals with non-AI-enabled VTE-CDSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(46, 23.71%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(148, 76.29%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRisk assessment indicators which the VTE-CDSS can assisted in monitoring\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVTE risk assessment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187(96.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141(95.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of moderate to high risk of VTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183(94.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137(92.57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBleeding risk assessment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184(94.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138(93.24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of high risk of bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160(82.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(97.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115(77.70%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccuracy of risk assessment*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e66(34.02%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e21(45.65%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e45(30.41%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevention indicators which the VTE-CDSS can assisted in monitoring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug prophylaxis rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177(91.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(88.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical prophylaxis rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176(90.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(97.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(88.51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined prophylaxis rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170(87.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(97.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125(84.46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandardization of prophylaxis *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e73(37.63%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e23(50.00%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e50(33.78%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome indicators which the VTE-CDSS can assisted in monitoring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence of hospital-associated VTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167(86.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(93.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124(83.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandardized treatment rate of hospital-associated VTE *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e44(22.68%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e31(67.39%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13(8.78%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence of bleeding events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(53.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(71.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71(47.97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality of hospital-associated VTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164(84.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(97.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119(80.41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: All answers are reported as N (proportion in %). \u003cem\u003eVTE\u003c/em\u003e Venous thromboembolism, \u003cem\u003eCDSS\u003c/em\u003e Clinical Decision Support System, \u003cem\u003eAI\u003c/em\u003e Artificial intelligence.\u003c/p\u003e \u003cp\u003e*: intrinsic quality indicators\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe efficiency and effectiveness of using VTE-CDSS\u003c/h2\u003e \u003cp\u003eIn this survey, participants were consulted for their opinions and evaluations on the effectiveness of VTE-CDSS in clinical practice, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAmong the 194 respondents included in this analysis, nearly 3/4 of them believed that VTE-CDSS had \"perfect system functions, and high enthusiasm for use by medical personnel\" (73.71%, 143/194). Among responders with AI enabled VTE-CDSS deployed in their hospital, the highest proportion (82.61%, 38/46) believed that \"perfect system functions, and high enthusiasm for use by medical personnel\". However, hospitals that deploy AI-enabled VTE-CDSS had a higher demand for vendor intervention support compared to hospitals with non-AI-enabled VTE-CDSS (21.74% vs. 18.24%).\u003c/p\u003e \u003cp\u003eMore than half of the respondents believed that the risk assessment rate and accuracy of the assessment had been significantly improved (56.19%, 109/194). However, only over one-third of respondents believed that the prevention rate and the standardization of prevention had been significantly improved (37.63%, 73/194). There was no significant statistical difference in the efficiency and effectiveness of risk assessment and implementation of prophylaxis measures between two groups of hospitals deployed with and without AI enabled VTE-CDSS, and the P-values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTherefore, we can conclude that at the present stage in China, the efficiency and effectiveness of VTE-CDSS with AI function in risk assessment and implementation of prophylaxis measures are not significantly different from that of VTE-CDSS without AI function. Although VTE-CDSS with AI function is more complete in system functionality and easier to accept and use by medical personnel than those without AI function, it may require more intervention and support from manufacturers in system debugging and integration.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe efficiency and effectiveness of using VTE-CDSS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThe efficiency and effectiveness of\u003c/p\u003e \u003cp\u003eusing VTE-CDSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(194)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHospitals with AI-enabled VTE-CDSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHospitals with non-AI-enabled VTE-CDSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(46, 23.71%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(148, 76.29%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation of system function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfect system functions, and high enthusiasm for use by medical personnel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143(73.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38(82.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105(70.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfect system functions, but the number of users is small\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24(12.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(10.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19(12.84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncomplete system functions, and the number of users is not large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13(6.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(4.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11(7.43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem administrator intervention is often required\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48(24.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8(17.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40(27.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVender intervention is often required\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37(19.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(21.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27(18.24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEfficiency and effectiveness\u0026mdash;\u0026mdash;Risk assessment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe risk assessment rate and accuracy of the assessment have been significantly improved (the improvement rate is more than 80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109(56.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27(58.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82(55.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.75\u0026thinsp;~\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe risk assessment rate and accuracy of the assessment have been significantly improved (the improvement rate is more than 30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59(30.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12(26.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47(31.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe assessment rate has improved, but the accuracy of the assessment has not\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22(11.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(13.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16(10.81%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect uncertainty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4(2.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(2.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(2.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEfficiency and effectiveness\u0026mdash;\u0026mdash;Prophylaxis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe prevention rate and the standardization of prevention have been significantly improved (the improvement rate is more than 80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73(37.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(43.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53(35.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.75\u0026thinsp;~\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe prevention rate and the standardization of prevention have been improved to some extent (the improvement rate is more than 30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87(44.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19(41.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68(45.95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe prevention rate has improved, but the standardization of prevention has not\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28(14.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(13.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22(14.86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect uncertainty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6(3.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(2.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(3.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: All answers are reported as N (proportion in %). \u003cem\u003eVTE\u003c/em\u003e Venous thromboembolism, \u003cem\u003eCDSS\u003c/em\u003e Clinical Decision Support System, \u003cem\u003eAI\u003c/em\u003e Artificial intelligence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBarriers in using VTE-CDSS\u003c/h2\u003e \u003cp\u003eThis survey conducted a statistical analysis on the obstacles existing in the implementation of VTE-CDSS, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Among them, \"the overall hospital information foundation is not perfect\" accounted for the highest proportion (40.21%, 78/194), followed by \"VTE-CDSS lacks effective suggestions of prophylaxis measures to guide clinical practice\" (34.02%, 66/194), \"VTE-CDSS has poor compatibility\" (30.93%, 60/194), \"the informationization process between doctors and nurses is not smooth\" (24.74%, 48/194), etc.\u003c/p\u003e \u003cp\u003eIt is worth noting that the following hindrance factors, such as \"changing the work habits of doctors and nurses, increasing workload and affecting work efficiency\", \"excessive warning and prompt information interferes with normal clinical work\", \"doctors and nurses overly rely on VTE-CDSS, losing their independent thinking ability and clinical autonomy, inducing doctor-patient conflicts and affecting doctor-patient relationships\", \"warning and prompt information is useless or even incorrect\", although the proportion is not high, it indicates that the system function of VTE-CDSS urgently needs to be closely adapted to clinical practice and further optimized.\u003c/p\u003e \u003cp\u003eThere was a significant statistical difference between the two groups of hospitals that deployed VTE-CDSS with and without AI function in the obstacles encountered in the implementation, and the P-values were shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The proportion of most hindrance factors that occur in hospitals with AI-enabled function VTE-CDSS is higher than that in hospitals with non-AI-enabled VTE-CDSS. However, the proportion of these three obstacles, such as \"the content of knowledge base is not updated in a timely manner and lacks authority\", \"doctors and nurses overly rely on VTE-CDSS, losing their independent thinking ability and clinical autonomy, inducing doctor-patient conflicts and affecting doctor-patient relationships\", \"warning and prompt information is useless or even incorrect\", appearing in hospitals with AI-enabled VTE-CDSS is higher than that in hospitals with non-AI-enabled VTE-CDSS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBarriers in using VTE-CDSS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBarriers in using VTE-CDSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHospitals with AI-enabled VTE-CDSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHospitals with non-AI-enabled VTE-CDSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(46, 23.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(148, 76.29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe overall hospital information foundation is not perfect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(40.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(30.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64(43.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVTE-CDSS lacks effective suggestions of prophylaxis measures to guide clinical practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(34.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(30.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(35.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVTE-CDSS has poor compatibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(30.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(23.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49(33.11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe informationization process between doctors and nurses is not smooth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(24.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(23.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(25.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe content of knowledge base is not updated in a timely manner and lacks authority\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e44(22.68%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11(23.91%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e33(22.30%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVTE-CDSS costs too much\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(21.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(19.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(22.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChanging the work habits of doctors and nurses, increasing workload and affecting work efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(17.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(13.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(18.92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive warning and prompt information interferes with normal clinical work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(15.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(16.22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDoctors and nurses overly rely on VTE-CDSS, losing their independent thinking ability and clinical autonomy, inducing doctor-patient conflicts and affecting doctor-patient relationships\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e17(8.76%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5(10.87%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e12(8.11%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWarning and prompt information is useless or even incorrect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e8(4.12%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2(4.35%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6(4.05%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: All answers are reported as N (proportion in %). \u003cem\u003eVTE\u003c/em\u003e Venous thromboembolism, \u003cem\u003eCDSS\u003c/em\u003e Clinical Decision Support System, \u003cem\u003eAI\u003c/em\u003e Artificial intelligence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eProblems to be further optimized and solved in the future in the implementation of VTE-CDSS\u003c/h2\u003e \u003cp\u003eThis survey conducted a statistical analysis on the problems that VTE-CDSS needs to be further optimized and solved in the future, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAmong the 194 respondents included in this analysis, over 70% believed that \"system functions need further improvement and more functional applications need to be expanded\" (78.35%, 152/194), and \"Clinical pathways for VTE risk assessment and prevention need to be smoothly integrated with CDSS\" (73.20%, 142/194). Compared to hospitals with AI-enabled VTE-CDSS, hospitals with non-AI-enabled VTE-CDSS had more urgent needs for the above two items (81.08% vs 69.57%, 74.32% vs 69.57%).\u003c/p\u003e \u003cp\u003eMore than 50% of respondents believed that \"data quality and availability need to be further improved\", \"VTE-CDSS construction standards and application management policies need to be developed\", \"AI algorithms, such as interpretability, transparency, algorithm adaptability, etc., need to be further improved\", \"unified VTE dataset standards need to be established and promoted nationwide\", \"more user-friendly human-computer interaction interfaces\" are all issues that need to be further optimized and solved in the future.\u003c/p\u003e \u003cp\u003eAs for the problems that need to be further optimized and solved in the future, there was a significant statistical difference between two groups of hospitals that have deployed VTE-CDSS with and without AI function, and the P-values were shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProblems to be further optimized and solved in the future\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProblems to be further optimized and solved in the future\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHospitals with AI-enabled VTE-CDSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHospitals with non-AI-enabled VTE-CDSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(46, 23.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(148, 76.29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystem functions need further improvement and more functional applications need to be expanded\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e152(78.35%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e32(69.57%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e120(81.08%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical pathways for VTE risk assessment and prevention need to be smoothly integrated with CDSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e142(73.20%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e32(69.57%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e110(74.32%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData quality and availability need to be further improved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114(58.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(63.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84(56.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVTE-CDSS construction standards and application management policies need to be developed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108(55.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(60.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80(54.05%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI algorithms, such as interpretability, transparency, algorithm adaptability, etc., need to be further improved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106(54.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(65.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76(51.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnified VTE dataset standards need to be established and promoted nationwide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105(54.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(63.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76(51.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore user-friendly human-computer interaction interfaces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(51.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(63.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70(47.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncrease objective data on user usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81(41.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(45.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(40.54%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduce system implementation workload\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(37.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(39.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(36.49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: All answers are reported as N (proportion in %). \u003cem\u003eVTE\u003c/em\u003e Venous thromboembolism, \u003cem\u003eCDSS\u003c/em\u003e Clinical Decision Support System, \u003cem\u003eAI\u003c/em\u003e Artificial intelligence.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith the promotion of the National VTE Prevention Program in China, the information construction of VTE has developed rapidly in recent years. Especially since 2018, the number of hospitals deploying VTE-CDSS has increased significantly, and the system functions have been gradually shifted from basic risk assessment computerization to more in-depth and diverse functions such as process quality control and monitoring, and AI-assisted clinical decision-making. The implementation of VTE-CDSS in China has achieved certain results, but there are still some obstacles and problems that need to be optimized in the future.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImproving system functionality to adapt to clinical pathways for VTE risk assessment and prevention\u003c/h2\u003e \u003cp\u003eThe biggest challenge faced by VTE-CDSS in the implementation process of this survey is to further improve system functionality to align with the clinical pathway of VTE risk assessment and prevention. This is consistent with the issue raised by other investigations in China [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which suggests that there may be a mismatch between VTE-CDSS system functionality and clinical business needs, making it difficult to translate user needs into system functionality design and closely align with clinical practice. This is also one of the main obstacles faced by VTE-CDSS in the process of system implementation.\u003c/p\u003e \u003cp\u003eTherefore, it is recommended that throughout the entire lifecycle of VTE-CDSS (design, development, implementation, operation and optimization), medical professionals and R\u0026amp;D personnel jointly participate in the creation of CDSS system solutions[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], taking more consideration of the application scope and service scenarios of the system, combining with hospital reality and clinical pathways of VTE risk assessment and prevention, closely communicating with medical professionals, and integrating the true needs of users and the pain points and difficulties of clinical business into the development of the system, so as to improve the clinical fit of the system. On the basis of breaking through the technical barriers, the standardized VTE-CDSS computerized clinical path is promoted from top to bottom, truly improving medical quality and clinical efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDeeply understand and implement the \"Five Correct\" principles of VTE-CDSS\u003c/h2\u003e \u003cp\u003eAlthough the application of VTE-CDSS in hospitals in China is not yet widespread (33.05%, 194/587), medical personnel have recognized and appreciated its potential benefits and possible challenges in the future.\u003c/p\u003e \u003cp\u003eIn this survey, the functional implementation of VTE-CDSS is mainly focused on assisting risk assessment and implementing prophylaxis measures. However, at the same time, we also concerned that the lack of effective prophylaxis measures to guide clinical practice is the second major obstacle to VTE-CDSS. A small number of respondents also mentioned that VTE-CDSS had \"too much warning information, interfering with normal clinical work\", and \"warning information is useless or even incorrect\".\u003c/p\u003e \u003cp\u003eThe implementation of CDSS should follow the \"Five Corrects\" principle [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]: providing the right information to the right people through the right channels, at the right time and in the right intervention mode in the diagnosis and treatment process. The clinical expertise involved in VTE-CDSS is relatively intensive, which requires the R\u0026amp;D team to deeply understand what is the right time, who is the right person, and what prophylaxis measure recommendations are correct and effective information in combination with the clinical business process of VTE risk assessment and prevention. More research and practice is still needed to seamlessly integrate CDSS into clinical business processes for VTE risk assessment and prevention, where, when and in what intervention mode, which can effectively improve the execution of prophylaxis measures, reduce unnecessary information interference, and minimize the burden of system response.\u003c/p\u003e \u003cp\u003eWhile dynamically integrating a comprehensive and evidence-based medical knowledge base, VTE-CDSS needs to conduct medical logical training and conditional weight analysis training tailoring to the clinical characteristics of VTE risk assessment and prevention. This enables CDSS to truly understand medical records and VTE prophylaxis guidelines, accurately grasp the \"Five Correct\" implementation principles of CDSS, and empower medical personnel with a higher level of risk assessment and prevention capabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePromote the application of VTE standard datasets to improve data quality and availability\u003c/h2\u003e \u003cp\u003e\"Data quality and availability\", which has attracted more and more global attention, is also a key issue that all hospitals in this survey believe that VTE-CDSS needs to be further optimized and solved in the future.\u003c/p\u003e \u003cp\u003eThe risk assessment and prevention of VTE involve a wide range of patients, covering almost all inpatient departments in the hospital. However, VTE risk assessment and prevention need to comprehensively consider patient factors, underlying diseases, concomitant medications, blood coagulation function and invasive operations, etc. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These key medical information are scattered throughout various information systems within the hospital. Currently, the information systems within a hospital in China are often developed by multiple different software vendors. Systems developed by different suppliers have large structural differences in their technical architecture and data, and hospitals generate massive amounts of operation data and disease data every day, resulting in the integrated application of VTE-CDSS, which requires a significant investment of manpower and financial resources for data cleaning, governance, and standardization. On the other hand, in order to achieve a high degree of accuracy in assisted decision-making, AI-enabled VTE-CDSS often needs to conduct cross-center and cross-regional data collection to obtain sufficient evidence-based medicine sample data to support machine learning, which also puts higher requirements on the standardization, structuring and unification of data models [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo change this situation, it is urgent to establish a VTE standard dataset based on VTE's own disease characteristics and combined with existing industry standards, so as to lay a technical foundation for future VTE clinical normative management, real-world research, and nationwide quality control. Based on this, the National VTE Prevention Program Office of China organized experts in relevant fields to integrate existing terminology norms, clinical practice guidelines and expert consensus from the perspective of data management, conducted a comprehensive and systematic collection of VTE-related data elements, and compiled and published the \"Venous Thromboembolism Standard Dataset\" in January 2023.\u003c/p\u003e \u003cp\u003eWe look forward to the nationwide promotion and application of the \" Venous Thromboembolism Standard Dataset \" to effectively promote the quality control of VTE and the integration and utilization of clinical data resources, standardize the management of VTE data in China, provide data support for the application of AI-based VTE-CDSS, and promote the standardized and homogeneous development of VTE prophylaxis and treatment system construction in China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImprove the transparency and interpretability of VTE-CDSS related AI algorithms\u003c/h2\u003e \u003cp\u003eIn recent years, the application of AI-based CDSS in the VTE field has become increasingly active. In this survey, although the number of hospitals deploying AI-enabled VTE-CDSS was relatively small (7.84%, 46/587), more than half of the surveyed hospitals (54.64%, 106/194) are highly concerned about the interpretability and transparency of AI algorithms. This will be another important challenge for AI-based VTE-CDSS in the future.\u003c/p\u003e \u003cp\u003eIn healthcare, the interpretability and transparency of AI is particularly important. Both patients and medical staff are not solely satisfied with the medical outcomes of AI output, but are also eager to understand the reasons behind making clinical decisions. Healthcare workers' understanding of how AI algorithms work, AI-prompted risk assessment and prophylaxis decision information affect their trust in the system. However, many machine learning methods (\"black boxes\") lack transparency, which may undermine the trust of healthcare professionals in AI output results [23,24].\u003c/p\u003e \u003cp\u003eTo solve these problems, the R\u0026amp;D team needs to consider how to open the \"black box\" of AI algorithms, such as developing interpretable AI algorithms or interactive human-computer dialogue software, so that medical personnel can understand the logic behind a clinical decision suggestion and judge its accuracy [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition, AI-based VTE-CDSS should proactively provide medical personnel with the details of risk assessment and prevention decisions, as well as relevant evidence-based medical evidence, such as source information support for patients' VTE risk factors and clinical evidence behind VTE prophylaxis measures recommendations, so that medical personnel can quickly understand the basis for clinical decision-making in a short time. This may be more favored by healthcare professionals than other software products with equal accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Potential Limitations\u003c/h2\u003e \u003cp\u003eIn summary, this survey on the application of CDSS for VTE risk assessment and prevention in China has two advantages. Firstly, to our knowledge, this is the first study to investigate the system functionality, effectiveness, and barriers to implementation of VTE-CDSS, and to evaluate the differences between deploying VTE-CDSS with and without AI capabilities. Our investigation has received widespread response and acceptance in China. Secondly, 79.38% of the respondents in our survey were administrative personnel from the medical affairs department or quality control department, who were responsible for the overall deployment and specific implementation of VTE-CDSS in their hospitals, so they had a more comprehensive understanding of the application of VTE-CDSS in hospitals. This will help us to accurately grasp the current status and potential challenges of VTE-CDSS application and implementation in China.\u003c/p\u003e \u003cp\u003eOn the other hand, our online survey also has some potential limitations. Firstly, as we did not have a tracking system to distinguish responders from non-responders, we were unable to compare detailed information about those who were not included in the survey and their hospitals, so we did not analyze the impact of non-respondent bias. This non-response bias may have some adverse effects on the representativeness of survey results. Because the respondents were likely to have a special interest in VTE-CDSS, most respondents hold a positive attitude toward VTE-CDSS. Secondly, quantitative studies may not be sufficient to fully understand the current application status and implementation barriers of VTE-CDSS. In fact, focusing solely on technology while ignoring human factors may lead to further explanatory oversights. Considering the social attribute of this topic, a combination of qualitative and quantitative methods in research may provide additional understanding of this complex phenomenon, which we will consider in our future work.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe application of CDSS and AI\u0026thinsp;+\u0026thinsp;CDSS for VTE risk assessment and prevention, although not yet widespread in hospitals in China, has made certain progress and breakthroughs in the implementation details of auxiliary risk assessment and prophylaxis measures, auxiliary quality control and monitoring, etc. Its effectiveness in improving the risk assessment rate, the accuracy of risk assessment, the prevention rate and the standardization of prevention has also been recognized by most users. The popularization and promotion of VTE-CDSS in Chinese hospitals still need to overcome some external and internal obstacles, and further optimize and solve the key issues that users are concerned about. Although facing challenges in the future, there is still great potential and development space.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"530\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7925%;\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.2075%;\"\u003e\n \u003cp\u003eArtificial intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7925%;\"\u003e\n \u003cp\u003eAI+CDSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.2075%;\"\u003e\n \u003cp\u003eAI-based Clinical Decision Support Systems; AI-based CDSS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7925%;\"\u003e\n \u003cp\u003eCDSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.2075%;\"\u003e\n \u003cp\u003eClinical Decision Support Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7925%;\"\u003e\n \u003cp\u003eDVT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.2075%;\"\u003e\n \u003cp\u003eDeep vein thrombosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7925%;\"\u003e\n \u003cp\u003ePTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.2075%;\"\u003e\n \u003cp\u003ePulmonary thromboembolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7925%;\"\u003e\n \u003cp\u003eVTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.2075%;\"\u003e\n \u003cp\u003eVenous thromboembolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7925%;\"\u003e\n \u003cp\u003eVTE-CDSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.2075%;\"\u003e\n \u003cp\u003eCDSS for VTE risk assessment and prevention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7925%;\"\u003e\n \u003cp\u003eR\u0026amp;D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.2075%;\"\u003e\n \u003cp\u003eResearch and development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the professionals who offered precious suggestions to this study. We thank you survey participants for their valuable input and help in improving this study. We thank all the healthcare workers who contributed to the data collection and management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCJ and ZZ contributed to the research idea and study design. CS, YT, LJ, JX, YX, WW, LZ and WX developed and designed the initial questionnaire. JW, YY, ZL, YJ, JS, QY, GS, YG, NZ, ZC, LZ, and ZC participated in the pilot survey and provided critical questionnaire review. QG, MS and BL were involved in data acquisition. LX, KZ, ZC, RL and XZ performed the statistical analysis and data interpretation. XZ and DW directed the statistical methods. The first draft of the manuscript was written by LX and all authors commented on previous versions of the of the paper. CJ and ZZ had full access to all data in the study and verified the data and are responsible for the integrity and accuracy of the data and the decision to submit the manuscript. All authors revised the report and approved the final version before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by National High Level Hospital Clinical Research Funding (2022‑NHLHCRF‑LX‑01‑0108); the CAMS Innovation Fund for Medical Sciences (CIFMS) (2023-I2M-A-014); National Key Research and Development Program of China (2023YFC2507200).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the Personal Information Protection Law of China, individual participant data in our study will not be made available publicly. The data from the National VTE Prevention Program in China, will be made available upon publication to members of the scientific and medical community for non-commercial use only, upon email request to [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Clinical Research Ethical Committee of the science and technology center of China-Japan Friendship Hospital (approval number:2021-162-K120). All participants gave informed consent prior to taking the survey. Participation was voluntary and the data collected was nonidentifiable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest or financial relationships to disclose. No form of payment was given to anyone to produce the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGeerts WH, Bergqvist D, Pineo GF, et al; American College of Chest Physicians. Prevention of venous thromboembolism: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest. 2008;133(6) (suppl):381S-453S.\u003c/li\u003e\n\u003cli\u003eFalck-Ytter Y, Francis CW, Johanson NA, et al; American College of Chest Physicians. Prevention of VTE in orthopedic surgery patients: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest. 2012;141(2) (suppl): e278S-e325S.\u003c/li\u003e\n\u003cli\u003eGould MK, Garcia DA, Wren SM, et al; American College of Chest Physicians. Prevention of VTE in nonorthopedic surgical patients: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest. 2012;141(2) (suppl): e227S-e277S.\u003c/li\u003e\n\u003cli\u003eCaprini JA, Tapson VF, Hyers TM, et al; NABOR Steering Committee. Treatment of venous thromboembolism: adherence to guidelines and impact of physician knowledge, attitudes, and beliefs. J Vasc Surg. 2005;42(4):726-733.\u003c/li\u003e\n\u003cli\u003eBeck MJ, Haidet P, Todoric K, Lehman E, Sciamanna C. Reliability of a point-based VTE risk assessment tool in the hands of medical residents. J Hosp Med. 2011;6(4):195-201.\u003c/li\u003e\n\u003cli\u003ePannucci CJ, Obi A, Alvarez R, et al. Inadequate venous thromboembolism risk stratification predicts venous thromboembolic events in surgical intensive care unit patients. J Am Coll Surg. 2014; 218(5):898-904.\u003c/li\u003e\n\u003cli\u003eCohen AT, Tapson VF, Bergmann JF, et al; ENDORSE Investigators. Venous thromboembolism risk and prophylaxis in the acute hospital care setting (ENDORSE study): a multinational cross-sectional study. Lancet. 2008;371(9610):387-394.\u003c/li\u003e\n\u003cli\u003eZhai Z, Kan Q, Li W et al. VTE Risk Profiles and Prophylaxis in Medical and Surgical Inpatients: The Identification of Chinese Hospitalized Patients\u0026apos; Risk Profile for Venous Thromboembolism (DissolVE-2)-A Cross-sectional StudyJ. . Chest,2019,155(1): 114-122.DOI: 10.1016/j.chest.2018.09.020.\u003c/li\u003e\n\u003cli\u003eMoja L, Kwag KH, Lytras T, et al. Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. Am J Public Health 2014;104: e12-22. DOI:10.2105/AJPH.2014.302164.\u003c/li\u003e\n\u003cli\u003eNakamura T, Sasano T. Artificial intelligence and cardiology: current status and perspectiveJ. . J Cardiol 2022; 79:326\u0026ndash;33. https://doi.org/10.1016/j.jjcc.2021.11.017.\u003c/li\u003e\n\u003cli\u003eTiti MA, Alotair HA, Fayed A, et al. Effects of Computerised Clinical Decision Support on Adherence to VTE Prophylaxis Clinical Practice Guidelines among Hospitalised Patients. Int J Qual Health Care. 2021;33(1): mzab034. DOI:10.1093/intqhc/mzab034.\u003c/li\u003e\n\u003cli\u003eBorab ZM, Lanni MA, Tecce MG, Pannucci CJ, Fischer JP. Use of Computerized Clinical Decision Support Systems to Prevent Venous Thromboembolism in Surgical Patients: A Systematic Review and Meta-analysis. JAMA Surg. 2017;152(7):638-645. DOI:10.1001/jamasurg.2017.0131.\u003c/li\u003e\n\u003cli\u003eHaut ER, Owodunni OP, Wang J, et al. Alert-Triggered Patient Education Versus Nurse Feedback for Nonadministered Venous Thromboembolism Prophylaxis Doses: A Cluster-Randomized Controlled Trial. J Am Heart Assoc. 2022;11(18): e027119. DOI:10.1161/JAHA.122.027119.\u003c/li\u003e\n\u003cli\u003eHuang X, Zhou S, Ma X, et al. Effectiveness of an artificial intelligence clinical assistant decision support system to improve the incidence of hospital-associated venous thromboembolism: a prospective, randomised controlled study. BMJ Open Qual. 2023;12(4): e002267. DOI:10.1136/bmjoq-2023-002267.\u003c/li\u003e\n\u003cli\u003eGao Q, Zhen K, Xia L, et al. Assessment of the Effect on Thromboprophylaxis with Multifaceted Quality Improvement Intervention based on Clinical Decision Support System in Hospitalized Patients: A Pilot Study. J Clin Med. 2022;11(17):4997. Published 2022 Aug 25. DOI:10.3390/jcm11174997.\u003c/li\u003e\n\u003cli\u003eJin ZG, Zhang H, Tai MH, Yang Y, Yao Y, Guo YT. Natural Language Processing in a Clinical Decision Support System for the Identification of Venous Thromboembolism: Algorithm Development and Validation. J Med Internet Res. 2023;25: e43153. Published 2023 Apr 24. DOI:10.2196/43153.\u003c/li\u003e\n\u003cli\u003eThe National Health Commission of the People\u0026apos;s Republic of China. Statistical Bulletin on the Development of Health Undertakings in China in 2022. EB/OL. . (2023-10-12) 2023-10-12. . http://www.nhc.gov.cn/guihuaxxs/s3586s/202310/5d9a6423f2b74587ac9ca41ab0a75f66.shtml.\u003c/li\u003e\n\u003cli\u003eJi M, Chen X, Georgi Z Genchev, et al. Status of AI-Enabled Clinical Decision Support Systems Implementations in China. Methods Inf Med. 2021;60(5-06):123-132. DOI:10.1055/s-0041-1736461.\u003c/li\u003e\n\u003cli\u003eLiu S, McCoy AB, Peterson JF, et al. Leveraging explainable artificial intelligence to optimize clinical decision support. J Am Med Inform Assoc. Published online February 22, 2024. DOI:10.1093/jamia/ocae019.\u003c/li\u003e\n\u003cli\u003eAgency for Healthcare Research and Quality. A Guide for Effective Quality Improvement: Preventing Hospital-Acquired Venous Thromboembolism. 2nd EdEB/OL. . (2016-08-01) 2022-06-01. . https://www.ahrq.gov/patient-safety/resources/vtguide/index.html.\u003c/li\u003e\n\u003cli\u003eHenke PK, Kahn SR, Pannucci CJ, et al. Call to Action to Prevent Venous Thromboembolism in Hospitalized Patients: A Policy Statement From the American Heart Association. Circulation. 2020;141(24): e914-e931. DOI:10.1161/CIR.0000000000000769\u003c/li\u003e\n\u003cli\u003eLu Y, Melnick ER, Krumholz HM. Clinical decision support in cardiovascular medicine. BMJ. 2022 May 25;377: e059818. DOI: 10.1136/bmj-2020-059818.\u003c/li\u003e\n\u003cli\u003eFlorien S van Royen, Folkert W Asselbergs, Fernando Alfonso, et al. Five critical quality criteria for artificial intelligence-based prediction modelsJ. . European Heart Journal, 2023, ehad727, https://doi.org/10.1093/eurheartj/ehad727.\u003c/li\u003e\n\u003cli\u003eKhera R, Simon MA, Ross JS. Automation Bias and Assistive AI: Risk of Harm from AI-Driven Clinical Decision Support. JAMA. 2023,330(23):2255-2257. DOI:10.1001/jama.2023.22557.\u003c/li\u003e\n\u003cli\u003eSilcox, C., Dentzer, S., Bates, D.W. AI-enabled clinical decision support software: A \u0026ldquo;trust and value checklist\u0026rdquo; for clinicians. NEJM Catalyst. 2020,1(6). DOI: https: //doi.org/10.1056/cat.20.0212.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Survey, Venous thromboembolism (VTE), Clinical Decision Support System (CDSS), Artificial intelligence(AI)","lastPublishedDoi":"10.21203/rs.3.rs-5008620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5008620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Aim:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenous thromboembolism (VTE) is an important cause of unexpected death in hospitalized patients. In recent years, Clinical Decision Support System (CDSS) has been increasingly adopted by hospitals worldwide. We conducted a survey with the aim of gaining a comprehensive understanding of the current state and future development direction of CDSS for VTE risk assessment and prevention(VTE-CDSS) in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA network survey was conducted among hospitals in China. The investigation mainly included 39 questions, such as the implementation details of VTE-CDSS, the scale and the admission capacity of the hospitals. SPSS 20.0 software was used for statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 587 hospitals responded to this survey, of which 194 (33.05%, 194/587) deployed VTE-CDSS, and less than a quarter (23.71%, 46/194) had Artificial intelligence(AI)-enabled VTE-CDSS. Among the 194 hospitals, the proportion of auxiliary decision support functions related to \"risk assessment\" was the highest (78.87%, 68.04%, 69.07%), followed by the auxiliary decision support functions related to \"prophylaxis execution\" (88.66%, 49.48%, 26.80%), and the proportion of auxiliary decision support functions related to \"outcome event monitoring\" was the lowest (46.39%, 22.68%). More than half of the respondents believed that the risk assessment rate and accuracy of the assessment had been significantly improved (56.19%, 109/194). However, only over one-third of respondents believed that the prevention rate and the standardization of prevention had been significantly improved (37.63%, 73/194). \"The overall hospital information foundation is not perfect\" was the primary hindrance factor in the implementation and application of VTE-CDSS (40.21%, 78/194). \"System functions need to be further improved and more functional applications expanded\" (78.35%, 152/194) is the most critical problem that VTE-CDSS needs to be further optimized and solved in the future. There were statistically significant differences between the two groups of hospitals that deployed VTE-CDSS with and without AI function (P\u0026lt;0.005) in the functional realization of various application details, the obstacles encountered in the implementation, and the problems to be further optimized and solved in the future. However, at the present stage in China, the efficiency and effectiveness of VTE-CDSS with AI function in risk assessment and implementation of prophylaxis measures are not significantly different from that of VTE-CDSS without AI function. (0.75\u0026lt;P\u0026lt;0.9).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe information construction of VTE in China has developed rapidly. The implementation of VTE-CDSS achieved certain results, but there are still some obstacles and problems that need to be optimized in the future.\u003c/p\u003e","manuscriptTitle":"Current status of VTE risk assessment and prevention using clinical decision support system: a cross-sectional survey from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 10:27:51","doi":"10.21203/rs.3.rs-5008620/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"322f260d-6c1a-43c1-8c5b-2b9812257523","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-11T16:38:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-08 10:27:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5008620","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5008620","identity":"rs-5008620","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}