Enhancing the Timeliness of EMR Documentation in Resident Doctors: The Role of PDCA Cycle Management

Enhancing the Timeliness of EMR Documentation in Resident Doctors: The Role of PDCA Cycle Management | 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 Enhancing the Timeliness of EMR Documentation in Resident Doctors: The Role of PDCA Cycle Management Jiaoting Chen, Qiongwen Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3881618/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2024 Read the published version in BMC Medical Education → Version 1 posted 12 You are reading this latest preprint version Abstract Background The role of the Plan-Do-Check-Act (PDCA) cycle in managing the timeliness of electronic medical records (EMRs) remains unclear. Therefore, this study aimed to evaluate the effect of PDCA management in improving the timeliness of EMR for resident doctors. Method This study had a before and after design. The resident doctors rotating in the Head and Neck Oncology Department of West China Hospital, Sichuan University from November 2021 to August 2022 were classified as the control group, which was managed by the current department practice. The resident doctors from September 2022 to June 2023 were included in the PDCA group, which was managed by the PDCA cycle. The incidences of late EMRs and unqualified EMRs were compared between the two groups and the influencing factors of the occurrence of late EMRs and unqualified EMRs were explored. Results A total of 314 resident doctors were included, with 162 doctors in the PDCA group and 152 doctors in the control group. The incidences of late EMRs (5.40% vs. 2.56%, P = 0.005) and unqualified EMRs (1.05% vs. 0.00%, P < 0.001) in the PDCA group were significantly lower than those in the control group. The timeliness of the first disease course records (0.24% vs. 0.00%, P = 0.023) and the first-ward-round records (0.36% vs. 0.00%, P = 0.035) were also improved significantly. After incorporating confounding factors, including age, sex, academic degree, working hours, and major, PDCA management still significantly reduced the occurrence of unqualified EMRs (P < 0.001) with an adjusted OR of 0.166 (95% CI 0.067–0.416) and a probability of 83.4% (0.166–1 = − 0.834). Conclusion This study successfully developed PDCA management and revealed that it is beneficial to enhance the timeliness of EMR while concurrently reducing the incidence of unqualified or delayed entries among resident doctors. electronic medical records Plan-Do-Check-Act cycle timeliness resident doctors Figures Figure 1 Figure 2 Introduction Electronic medical records (EMRs) are ingrained in medical practice today and are effective working instruments for doctors ( 1 ). EMRs form a rich repository of information that enables the use of better clinical prognostic tools ( 2 ), surgical scheduling tools ( 3 ), decision support systems ( 4 ), public health surveillance ( 1 , 5 ), and so on. However, in the systems of employing resident doctors, work hours restrictions and schedule variations interrupt continuity between patients and providers, which enhances the importance of prompt recording ( 6 – 8 ). Few articles have been published on approaches to improving the timeliness of EMRs, including ameliorating EMR application ( 9 ), conducting assessments and public displays ( 10 ), and designing new structured data-entry forms ( 6 ). As the current rate of timely EMR entries has remained unsatisfactory, more new improvement measures are required. The Plan-Do-Check-Act (PDCA) cycle, also known as the “quality loop,” is a management model for continuous improvement( 11 ) and has demonstrated its effectiveness in multiple medical scenarios, including managing hyperglycemia( 12 ), managing nutrition ( 13 ), and controlling the infection rate in operating rooms( 14 ). Recently, the PDCA cycle was also applied in modern hospital quality management and demonstrated its efficiency ( 15 – 17 ). Considering the wide application of EMRs and the lack of effective management modes for reducing delayed EMRs, this study aimed to explore whether the implementation of PDCA management in residents can improve the timeliness of EMRs compared to traditional management methods. Materials and Methods Study population The present study employed a before and after design. We enrolled the resident doctors rotating in the Head and Neck Oncology Department of West China Hospital, Sichuan University. Resident doctors rotating between November 2021 and August 2022 were classified as the control group and were managed according to current department practices. The resident doctors from September 2022 to June 2023 were included in the PDCA group, which was managed according to the PDCA cycle for quality management. Doctors with missing information were not eligible for inclusion. Study variables (1) Basic information of resident doctors was recorded, including age, sex, postgraduate year (PGY), major, and weekly working hours. (2) The late EMRs were defined as those that were not written in a timely manner. The timely issues of EMRs were categorized as follows: (a) for patients with stable conditions, the completion time of disease course records exceeded 3 days; (b) the first disease course records were not completed within 8 hours after admission; (c) the shift records were not completed before the succession records were written; (d) the completion time of stage summaries exceeded 30 days during the hospitalization; (e) the first-ward-round records by the superior physicians were not completed within 48 hours after the patient was admitted; (f) the surgical records were not completed within 24 hours after surgery; (g) important examination results were not recorded in the disease course records; (h) the EMR homepages were not completed within 24 hours after discharge; Among them, EMRs with severe timeliness problems [(b) to (f)] were classified as unqualified EMRs. All these timeliness issues can be identified by the medical records system of our hospital. Intervention According to the PDCA cycle, the management approach was conducted in the PDCA group and the specific steps were as follows (Figure 1): Plan Formulate the training plan: determine the training content and schedule. Strengthening the standardized training and the entry training of EMRs for resident doctors can enhance their awareness and ability to standardize the writing of EMRs. Establish an EMR review team: the review team was composed of EMR quality controllers and medical team leaders. The EMR quality controller needed to strengthen the verification of each EMR based on the evaluation results of the medical records system. Determine the key verification contents: based on the previous causes of unqualified EMRs, we summarized the key verification contents, focusing on the correctness of surgical procedure on the homepage, diagnosis, coding, and admission/discharge situation and the integrity of the homepage and disease course records. Determine immediate feedback methods: after finding unqualified EMRs, the medical record quality controller was responsible for reminding the medical teams in a timely manner to promote learning and rectifications. Do Conduct regular training: the training aimed to introduce the content and criteria of the review to the doctors, and enable them to master the writing norms of EMRs, including the requirements of timeliness, correctness, and integrity, to reduce the number of unqualified EMRs. Training methods: the training starts with online training (i.e., remote meetings, online lectures, and online Q&As) and offline training (i.e., morning meetings and sessions to share relative information). The training was arranged twice a week. Use social media to establish communication groups: First, each medical team leader reviewed the timeliness, correctness, and integrity of EMRs, and promptly reported the review results in the communication group on the WeChat platform. Unqualified EMRs should be reported and modified promptly. The medical records quality controller conducted a second round of review. If any unqualified medical record was found, timely feedback was given to each medical team in the communication group, and the responsible medical team was required to strengthen learning and rectification. The communication method was expected to contribute to mutual supervision and promotion among medical teams. Check The quality and timeliness rates of EMRs were evaluated by the three-level supervision every month. The three-level supervision was carried out by the medical team leaders, the EMR quality controller, and the special department of the hospital to obtain objective evaluation results based on the evaluation of the medical records system. The evaluation of the PDCA management effect involved the implementation of the plan in the training phase, the quality and timeliness of EMRs, and the reduction of unqualified EMRs. We analyzed the causes of unqualified EMRs and adjusted the focus of training and review. Act A doctor who excelled in timely, complete, and correct EMRs was selected and given a 5% performance reward and praised in the department. Hold talks with doctors who have multiple unqualified EMRs and strengthen their training and supervision. Discussions on unqualified EMRs were organized to help doctors learn from mistakes. Finally, the medical records review team modified the management plan based on the problems encountered during the process of implementation and management. Evaluation indicators The department's timeliness rate of EMR documentation was evaluated by the proportion of EMRs with different timeliness issues each month. The effectiveness of PDCA management was examined by comparing the proportions of EMRs with different timeliness issues between the two groups. Statistical analysis For continuous variables, tests of normality distribution were used. Normally distributed continuous variables were presented as mean ± standard deviation, and non-normally distributed continuous variables were represented by the median and range [25th-75th percentile]. Categorical variables were presented as counts and percentages. We used independent t-test and Mann-Whitney U test for continuous variables, as appropriate. The chi-square test and Fisher’s exact test were applied for categorical variables. Univariate and multivariate binary logistic regression were performed to explore the independent factors influencing the occurrence of EMRs with timeliness issues. The results were presented as odds ratios (ORs). The results were considered statistically significant at P < .05 and all tests were 2-tailed. The data were analyzed with the Statistical Package for the Social Sciences (SPSS) software (version 26.0, SPSS Inc., Chicago, IL, USA). Results Between November 2021 and June 2023, we included a total of 314 resident doctors, and no one was excluded due to incomplete information. The control group included 152 doctors who underwent rotation or further education from November 2021 to August 2022 and 162 doctors from September 2022 to June 2023 were allocated to the PDCA group. The PDCA group adopted the PDCA management of the timeliness of EMR documentation. The basic characteristics of all the doctors are presented in Table 1. The majority of the participants were under 30 years old (n = 204, 65.0%), had a bachelor's degree (n = 197, 62.7%), and worked 40 to 60 hours per week (n = 264, 84.1%). There was no significant difference in most of the basic information between the two groups, while the doctors in the PDCA group had a significantly shorter PGY compared to the control group (P < 0.001). The effectiveness of PDCA management There was no significant difference in the total number of EMRs between the two groups. As indicated in Table 2, the PDCA cycle management mode significantly reduced the percentage of late EMRs (5.40% vs. 2.56%, P = 0.005) and unqualified EMRs (1.05% vs. 0.00%, P < 0.001). Moreover, the percentage of EMRs with first disease course records that weren't completed within 8 hours after the admission of patients in the PDCA group was significantly lower than that in the control group (0.24% vs. 0.00%, P = 0.023). In addition, the timeliness of the 3-day disease course records (2.93% vs. 0.61%, P = 0.001) and the first ward round records (0.36% vs. 0.00%, P = 0.035) also improved significantly due to the implementation of the PDCA cycle. The numbers and percentages of EMRs with different timeliness issues were significantly reduced after employing PDCA management (Figure 2). Factors influencing unqualified medical records To further investigate the effectiveness of the PDCA cycle management model, we identified the influencing factors of the occurrence of unqualified EMRs and eliminated their interference using univariate and multivariate binary logistic regression. There were six potential confounding factors, including age, sex, PGY, academic degree, working hours, and major, of which only age significantly affected the occurrence of unqualified EMRs (P = 0.010). Compared with doctors who are older than 30 years old, those who are younger than 30 years old have a higher occurrence of unqualified EMRs, with an OR of 3.622 (95% CI 1.363–9.621) and a relative probability of 262.2% (3.622 – 1 = 2.622). All ORs of the influencing factors can be found in Table 3. Incorporating age and PDCA management into a multivariate regression analysis, the results showed that PDCA cycle management can still significantly reduce the occurrence of unqualified EMRs (P < 0.001) with an adjusted OR of 0.166 (95% CI 0.067–0.416) and a probability of 83.4% (0.166 – 1 = –0.834) (Table 4). Factors influencing late medical records According to the univariate regression analysis, age (P = 0.028), sex (P = 0.044), PGY, and major were determined to be confounding factors (Table 5) and were incorporated into the multiple regression analysis. The results showed that the PDCA cycle management mode could still effectively lower the occurrence of late EMRs (P < 0.001) with an adjusted OR of 0.318 (95% CI 0.181–0.557) and a probability of 68.2% (0.318 – 1 = –0.682). Age may be a potential predictive factor for the occurrence of late EMR documentation (P = 0.055). The impact of sex on the occurrence of late EMRs was not statistically significant (P = 0.155). Compared to doctors who have graduated for 4 years or more, doctors who have graduated for 2 years have a higher incidence of late EMR (P = 0.008), with an OR of 1.027 (95% CI 1.301–5.994) and a probability of 30.1% (1.301 – 1 = 0.301). Furthermore, the incidence of late EMRs among doctors in internal medicine and other majors was significantly lower than that of doctors in head and neck tumors (P = 0.021), with an OR of 2.537 (95% CI 1.161–5.543) and a probability of 153.7% (2.537 – 1 = 1.537) (Table 6). Discussion The ability of resident doctors to accurately document and efficiently share patient information through EMRs has emerged as a key area of focus for medical educators and policymakers. This focus is justified by the crucial role that timely and accurate EMR entries play in effective patient care and coordination among healthcare professionals. Timely EMR updates are critical not just for emergency scenarios, where they can be life-saving, but also for reducing the risk of medical errors across all areas of healthcare delivery( 18 – 20 ). However, the implementation of EMRs has introduced unintended challenges, including increased work hours, time constraints, and potential miscommunication between patients and healthcare providers( 21 ). Therefore, the application of scientific management tools and the provision of EMR training are indispensable components in the training of resident doctors, ensuring they are equipped to navigate these complexities effectively and maintain the highest standards of patient care. In response to these challenges, our study explored the Plan-Do-Check-Act (PDCA) cycle as a strategic intervention to increase the timeliness of EMR documentation among resident doctors. The PDCA cycle, a structured quality management method, involved establishing a medical records review team and formulating a comprehensive training plan in the planning phase( 22 ). During the implementation phase, training was delivered according to predefined education standards. Subsequently, in the evaluation phase, we identified challenges, analyzed underlying causes, and incorporated improvement measures into subsequent PDCA cycles, fostering a continuous enhancement environment. Notably, practical measures such as network meetings and real-time communication were employed for immediate response to delays in EMR documentation. Our findings indicate a significant increase in the proportion of timely EMR entries following the PDCA implementation, underscoring its effectiveness in improving EMR management practices among residents. This suggests that structured, cyclic approaches to process management can profoundly impact EMR documentation quality. Additionally, our analysis identified key factors contributing to delayed or suboptimal medical record entries. We noted correlations between delayed EMR entries and variables like the age of resident doctors, years since graduation, and medical specialization. For instance, residents in their second post-graduation year often face higher patient loads than in their first year, potentially leading to increased workload and time constraints, which can impede timely documentation. Despite these variables, the implementation of the PDCA cycle was the most significant factor in reducing delayed entries, highlighting its potential to transform EMR practices. This study has several limitations that warrant discussion. The application of the PDCA cycle in this study was restricted to resident doctors, and its effectiveness in enhancing EMR timeliness among more seasoned physicians warrants further exploration. Moreover, our study's findings, derived from a single Chinese hospital, may not be universally applicable due to variations in medical records management practices across different healthcare systems. To overcome these limitations, future research should extend to multi-center studies in varied healthcare settings and include larger sample sizes. Such studies could provide a more comprehensive evaluation of the PDCA cycle's efficacy across diverse medical environments. Conclusions In conclusion, this study successfully developed and implemented a PDCA cycle tailored for managing the timeliness of EMR among resident doctors. Our findings demonstrate that the application of the PDCA management method significantly enhances the timeliness of EMR documentation while concurrently reducing the incidence of unqualified or delayed entries. These encouraging results underscore the potential of PDCA cycle management as a robust framework not only for improving EMR management but also as a versatile tool in the broader context of resident training and skill development. Abbreviations Plan-Do-Check-Act (PDCA) Electronic medical records (EMRs) postgraduate year (PGY) odds ratios (ORs) the Statistical Package for the Social Sciences (SPSS) Declarations Ethics approval and consent to participate Written informed consent was obtained from the individual participants or their guardians. The study was approved by the medical ethics committee of West China Hospital, Sichuan University, China. All procedures involving human participants performed in this study were in accordance with the ethical standards of the institutional research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Consent for publication Not applicable. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The author(s) reported there is no funding associated with the work featured in this article. Author Contributions QZ designed this work. JC collected materials and wrote this manuscript. QZ edited and revised the manuscript. Both authors have read the manuscript and approved the final version. Acknowledgement None. References Abid M, Schneider AB. Clinical Informatics and the Electronic Medical Record. The Surgical clinics of North America (2023) 103(2):247-58. Epub 2023/03/23. doi: 10.1016/j.suc.2022.11.005. Escobar GJ, Soltesz L, Schuler A, Niki H, Malenica I, Lee C. Prediction of Obstetrical and Fetal Complications Using Automated Electronic Health Record Data. American journal of obstetrics and gynecology (2021) 224(2):137-47.e7. 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(%) 0.179 bachelor's degree 100 (65.8) 97 (59.9) 197 (62.7) Master's degree 31 (20.4) 31 (19.1) 62 (19.7) doctor's degree 21 (13.8) 34 (21.0) 55 (17.5) Major — no. (%) 0.205 hematological malignancy 5 (3.3) 5 (3.1) 10 (3.2) Thoracic tumors 29 (19.1) 32 (19.8) 61 (19.4) Abdominal tumors 32 (21.1) 38 (23.5) 70 (22.3) Internal medicine and others 52 (34.2) 37 (22.8) 89 (28.3) Head and neck tumors 34 (22.4) 50 (30.9) 84 (26.8) Working hours — no. (%) 0.765 60 h per week 10 (6.6) 23 (14.2) 33 (10.5) PGY — no. (%) <0.001 1 25 (16.4) 56 (34.6) 81 (25.8) 2 45 (29.6) 56 (34.6) 101 (32.2) 3 41 (27.0) 28 (17.3) 69 (22.0) 4 * 41 (27.0) 22 (13.6) 63 (20.1) PGY: post graduate year *: more than 3 years Table2. Comparison of the medical records with different timeliness issues between two groups Items Control group PDCA group Overall p-Value The total number of medical records 391 ± 61.53 344.50 ± 80.034 367 ± 73.51 0.160 The number of late medical records 24.00 (13.00-34.75) 9.00 (6.00-10.50) 11.00 (9.00-25.00) <0.001 The percentage of late medical records 5.40% (2.99%-8.47%) 2.56% (1.42%-3.58%) 3.35% (2.44%-5.46%) 0.005 The number of unqualified medical records 4.50 (2.75-6.25) 0.00 (0.00-1.00) 1.5 (0.00-4.75) <0.001 The percentage of unqualified medical records 1.05% (0.69%-1.49%) 0.00% (0.00%-0.32%) 0.49% (0.00%-1.14%) <0.001 The percentage of medical records with disease course records completion time exceeding 3 days for patients in stable conditions 2.93% (1.34%-5.76%) 0.61% (0.43%-1.25%) 1.30% (0.60%-2.97%) 0.001 The percentage of medical records with the first disease records completion time exceeding 8 hours after admission 0.24% (0.00%-0.43%) 0.00% (0.00%-0.00%) 0.00% (0.00%-0.29%) 0.023 The percentage of medical records with shift records that were not completed timely 0.21% (0.00%-0.55%) 0.00% (0.00%-0.06%) 0.00% (0.00%-0.28%) 0.063 The percentage of medical records with stage summaries completion time exceeding 30 days 0.00% (0.00%-0.05%) 0.00% (0.00%-0.00%) 0.00% (0.00%-0.00%) 0.481 The percentage of first-ward-round records by the superior physicians with the completion time exceeding 48 hours after admission 0.36% (0.00%-0.54%) 0.00% (0.00%-0.08%) 0.00% (0.00%-0.41%) 0.035 The percentage of medical records with surgical records completion time exceeding 24 hours after surgery 0.00% (0.00%-0.05%) 0.00% (0.00%-0.00%) 0.00% (0.00%-0.00%) 0.481 The percentage of medical records that didn't record the important results of examinations 0.00% (0.00%-0.35%) 0.00% (0.00%-0.25%) 0.00% (0.00%-0.27%) 0.912 The percentage of medical records with the homepage completion time exceeding 24 hours after discharge 1.05% ± 0.42% 1.56% ± 0.893% 1.31%±0.73% 0.645 PDCA: Plan-Do-Check-Act Data are Mean±SD or median (IQR). Table 3. Univariate logistic analysis of influencing factors for the occurrence of unqualified medical records Variable Units β Odds Ratio 95%CI p-Value Age <30 1.287 3.622 1.363; 9.621 0.010 ≥30 Ref Sex Male 0.350 1.419 0.673; 2.994 0.358 Female Ref PDCA cycle Yes -1.813 0.163 0.066; 0.405 <0.001 No Ref PGY 1 -0.878 0.416 0.116; 1.488 0.177 2 0.253 1.288 0.489; 3.387 0.609 3 0.304 1.355 0.483; 3.809 0.563 4* Ref Academic degree bachelor's degree -0.148 0.862 0.348; 2.138 0.180 Master's degree -0.308 0.735 0.231; 2.336 0.181 doctor's degree Ref Working hours 60h per week Ref Major Hematological malignancy 0.615 0.474 0.343; 9.969 0.474 Thoracic tumors 0.372 1.451 0.563; 3.738 0.441 Abdominal tumors -0.046 0.505 0.355; 2.567 0.927 Internal medicine and others -0.820 0.570 0.144; 1.347 0.151 Head and neck tumors Ref PDCA: Plan-Do-Check-Act PGY: post graduate year *: more than 3 years Table 4. Multivariate logistic analysis of influencing factors for the occurrence of unqualified medical records Variable Units β Odds Ratio 95%CI p-Value Age <30 1.254 2.923 1.296; 9.470 0.013 ≥30 Ref PDCA cycle Yes -1.794 0.166 0.067; 0.416 <0.001 No Ref PDCA: Plan-Do-Check-Act Table 5. Univariate logistic analysis of influencing factors for the occurrence of late medical records Variable Units β Odds Ratio 95%CI p-Value Age <30 0.596 1.815 1.067; 3.086 0.028 ≥30 Ref Sex Male 0.535 1.707 1.015; 2.872 0.044 Female Ref PDCA cycle Yes -0.825 0.438 0.268; 0.717 0.001 No Ref PGY 1 -0.038 0.963 0.451; 2.058 0.923 2 0.738 2.092 1.047; 4.176 0.037 3 0.036 1.037 0.475; 2.265 0.928 4* Ref Academic degree bachelor's degree -0.469 0.626 0.337; 1.164 0.139 Master's degree -0.651 0.522 0.238; 1.143 0.104 doctor's degree Ref Working hours 60h per week Ref Major Hematological malignancy 1.046 2.845 1.048; 4.128 0.127 Thoracic tumors -0.231 0.794 1.508; 23.210 0.524 Abdominal tumors 0.052 1.054 0.776; 3.514 0.877 Internal medicine and others -0.732 0.481 1.076; 4.465 0.036 Head and neck tumors Ref PDCA: Plan-Do-Check-Act PGY: post graduate year *: more than 3 years Table 6. Multivariate logistic analysis of influencing factors for the occurrence of late medical records Variable Units β Odds Ratio 95%CI p-Value Age <30 0.611 1.842 0.987; 3.439 0.055 ≥30 Ref Sex Male 0.421 1.523 0.853; 2.723 0.155 Female Ref PDCA cycle Yes -1.147 0.318 0.181; 0.557 <0.001 No Ref PGY 1 0.591 1.806 0.753; 4.326 0.185 2 1.027 2.793 1.301; 5.994 0.008 3 0.031 1.031 0.453; 2.351 0.940 4* Ref Major Hematological malignancy 0.888 2.416 1.145; 5.100 0.291 Thoracic tumors -0.242 5.877 1.374; 25.125 0.538 Abdominal tumors 0.049 1.896 0.831; 4.335 0.894 Internal medicine and others -0.882 2.537 1.161; 5.543 0.021 Head and neck tumors Ref PDCA: Plan-Do-Check-Act PGY: post graduate year *: more than 3 years Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2024 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 27 Jun, 2024 Reviews received at journal 26 Jun, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviews received at journal 28 Mar, 2024 Reviewers agreed at journal 25 Mar, 2024 Reviewers agreed at journal 22 Mar, 2024 Reviewers invited by journal 14 Feb, 2024 Editor assigned by journal 14 Feb, 2024 Editor invited by journal 20 Jan, 2024 Submission checks completed at journal 20 Jan, 2024 First submitted to journal 20 Jan, 2024 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. 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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-3881618","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268559260,"identity":"267c6798-d9f6-41f7-8d0c-b5046ee20231","order_by":0,"name":"Jiaoting Chen","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jiaoting","middleName":"","lastName":"Chen","suffix":""},{"id":268559261,"identity":"ef39189e-a831-46d7-bbe9-95bfe29578e1","order_by":1,"name":"Qiongwen Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYDACZjiLB0TYILGJ1JJGhBYEACs7TFgL33HmYw+/ttnkMfCfPfi54Nd5e/4ZCYwP3rYxyJvj0CJ5mC3dWLYtrZiB4Vyy9My+24kzbiQwG85tYzDc2YBdi8FhHjNpyW2HExsYewykeXtuJxhIJLBJ87YxJBgcwKvlf2IDM4/xb96ec/ZALey/CWmR/LjtQGIDG1Avz48DjBuAtjDj0wL0S5o047/kxDYeHjNr3obkxBlnHjZLzjknYbgBhxa+84ePSf44Y5fYz3/G+DbPHzt7/vbkgx/elNnI47KFASjODIoFNhCHsQ1MNgAJCRzqIVoYf8B5f3ArHAWjYBSMgpELAOFHVg8o4wrXAAAAAElFTkSuQmCC","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Qiongwen","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-01-20 13:15:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3881618/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3881618/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12909-024-06134-2","type":"published","date":"2024-11-26T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50008763,"identity":"460c33ae-2440-494c-8a8a-e3ef0b9b3dc1","added_by":"auto","created_at":"2024-01-23 04:13:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":576921,"visible":true,"origin":"","legend":"\u003cp\u003eThe specific implementation diagram of the PDCA for enhancing the timeliness of electronic medical records.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3881618/v1/89137d11fdac2e36654625e3.png"},{"id":50008762,"identity":"34132750-2b97-407e-8f29-d13fd1defe06","added_by":"auto","created_at":"2024-01-23 04:13:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":321012,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The total number of medical records between two groups. There was no significant difference in the total number of medical records between the control group and the PDCA group. (B) The percentage of unqualified medical records between two groups. PDCA management can significantly reduce the number of unqualified medical records significantly. (C) Timeliness issues in control group and PDCA group. The number and percentage of medical records with different timeliness issues can be reduced after the implementation of the PDCA.\u003c/p\u003e\n\u003cp\u003ePDCA, Plan-Do-Check-Act.\u003c/p\u003e\n\u003cp\u003e*P \u0026lt; 0.05, ***P \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3881618/v1/0e05c6fc81b4f3a23f28ec9d.png"},{"id":70391140,"identity":"5dbca62a-aea3-4707-96f9-dee17d2cfaed","added_by":"auto","created_at":"2024-12-02 17:30:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1743838,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3881618/v1/f2dd28c6-0de8-4e21-b1ec-3d2f6b38e404.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing the Timeliness of EMR Documentation in Resident Doctors: The Role of PDCA Cycle Management","fulltext":[{"header":"Introduction","content":"\u003cp\u003eElectronic medical records (EMRs) are ingrained in medical practice today and are effective working instruments for doctors (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). EMRs form a rich repository of information that enables the use of better clinical prognostic tools (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), surgical scheduling tools (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), decision support systems (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), public health surveillance (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and so on. However, in the systems of employing resident doctors, work hours restrictions and schedule variations interrupt continuity between patients and providers, which enhances the importance of prompt recording (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Few articles have been published on approaches to improving the timeliness of EMRs, including ameliorating EMR application (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), conducting assessments and public displays (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and designing new structured data-entry forms (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). As the current rate of timely EMR entries has remained unsatisfactory, more new improvement measures are required.\u003c/p\u003e \u003cp\u003eThe Plan-Do-Check-Act (PDCA) cycle, also known as the \u0026ldquo;quality loop,\u0026rdquo; is a management model for continuous improvement(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and has demonstrated its effectiveness in multiple medical scenarios, including managing hyperglycemia(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), managing nutrition (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and controlling the infection rate in operating rooms(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Recently, the PDCA cycle was also applied in modern hospital quality management and demonstrated its efficiency (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Considering the wide application of EMRs and the lack of effective management modes for reducing delayed EMRs, this study aimed to explore whether the implementation of PDCA management in residents can improve the timeliness of EMRs compared to traditional management methods.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study employed a before and after design. We enrolled the resident doctors rotating in the Head and Neck Oncology Department of West China Hospital, Sichuan University. Resident doctors rotating between November 2021 and August 2022 were classified as the control group and were managed according to current department practices. The resident doctors from September 2022 to June 2023 were included in the PDCA group, which was managed according to the PDCA cycle for quality management. Doctors with missing information were not eligible for inclusion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Basic information of resident doctors was recorded, including age, sex, postgraduate year (PGY), major, and weekly working hours.\u003c/p\u003e\n\u003cp\u003e(2) The late EMRs were defined as those that were not written in a timely manner. The timely issues of EMRs were categorized as follows: (a) for patients with stable conditions, the completion time of disease course records exceeded 3 days; (b) the first disease course records were not completed within 8 hours after admission; (c) the shift records were not completed before the succession records were written; (d) the completion time of stage summaries exceeded 30 days during the hospitalization; (e) the first-ward-round records by the superior physicians were not completed within 48 hours after the patient was admitted; (f) the surgical records were not completed within 24 hours after surgery; (g) important examination results were not recorded in the disease course records; (h) the EMR homepages were not completed within 24 hours after discharge; Among them, EMRs with severe timeliness problems [(b) to (f)] were classified as unqualified EMRs. All these timeliness issues can be identified by the medical records system of our hospital.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the PDCA cycle, the management approach was conducted in the PDCA group and the specific steps were as follows (Figure 1):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePlan\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eFormulate the training plan: determine the training content and schedule. Strengthening the standardized training and the entry training of EMRs for resident doctors can enhance their awareness and ability to standardize the writing of EMRs.\u003c/li\u003e\n \u003cli\u003eEstablish an EMR review team: the review team was composed of EMR quality controllers and medical team leaders. The EMR quality controller needed to strengthen the verification of each EMR based on the evaluation results of the medical records system.\u003c/li\u003e\n \u003cli\u003eDetermine the key verification contents: based on the previous causes of unqualified EMRs, we summarized the key verification contents, focusing on the correctness of surgical procedure on the homepage, diagnosis, coding, and admission/discharge situation and the integrity of the homepage and disease course records.\u003c/li\u003e\n \u003cli\u003eDetermine immediate feedback methods: after finding unqualified EMRs, the medical record quality controller was responsible for reminding the medical teams in a timely manner to promote learning and rectifications.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eDo\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eConduct regular training: the training aimed to introduce the content and criteria of the review to the doctors, and enable them to master the writing norms of EMRs, including the requirements of timeliness, correctness, and integrity, to reduce the number of unqualified EMRs.\u003c/li\u003e\n \u003cli\u003eTraining methods: the training starts with online training (i.e., remote meetings, online lectures, and online Q\u0026amp;As) and offline training (i.e., morning meetings and sessions to share relative information). The training was arranged twice a week.\u003c/li\u003e\n \u003cli\u003eUse social media to establish communication groups: First, each medical team leader reviewed the timeliness, correctness, and integrity of EMRs, and promptly reported the review results in the communication group on the WeChat platform. Unqualified EMRs should be reported and modified promptly. The medical records quality controller conducted a second round of review. If any unqualified medical record was found, timely feedback was given to each medical team in the communication group, and the responsible medical team was required to strengthen learning and rectification. The communication method was expected to contribute to mutual supervision and promotion among medical teams.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCheck\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eThe quality and timeliness rates of EMRs were evaluated by the three-level supervision every month. The three-level supervision was carried out by the medical team leaders, the EMR quality controller, and the special department of the hospital to obtain objective evaluation results based on the evaluation of the medical records system.\u003c/li\u003e\n \u003cli\u003eThe evaluation of the PDCA management effect involved the implementation of the plan in the training phase, the quality and timeliness of EMRs, and the reduction of unqualified EMRs.\u003c/li\u003e\n \u003cli\u003eWe analyzed the causes of unqualified EMRs and adjusted the focus of training and review.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAct\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eA doctor who excelled in timely, complete, and correct EMRs was selected and given a 5% performance reward and praised in the department. Hold talks with doctors who have multiple unqualified EMRs and strengthen their training and supervision.\u003c/li\u003e\n \u003cli\u003eDiscussions on unqualified EMRs were organized to help doctors learn from mistakes.\u003c/li\u003e\n \u003cli\u003eFinally, the medical records review team modified the management plan based on the problems encountered during the process of implementation and management.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe department\u0026apos;s timeliness rate of EMR documentation was evaluated by the proportion of EMRs with different timeliness issues each month. The effectiveness of PDCA management was examined by comparing the proportions of EMRs with different timeliness issues between the two groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor continuous variables, tests of normality distribution were used. Normally distributed continuous variables were presented as mean \u0026plusmn; standard deviation, and non-normally distributed continuous variables were represented by the median and range [25th-75th percentile]. Categorical variables were presented as counts and percentages. We used independent t-test and Mann-Whitney U test for continuous variables, as appropriate. The chi-square test and Fisher\u0026rsquo;s exact test were applied for categorical variables. Univariate and multivariate binary logistic regression were performed to explore the independent factors influencing the occurrence of EMRs with timeliness issues. The results were presented as odds ratios (ORs). The results were considered statistically significant at P \u0026lt; .05 and all tests were 2-tailed. The data were analyzed with the Statistical Package for the Social Sciences (SPSS) software (version 26.0, SPSS Inc., Chicago, IL, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween November 2021 and June 2023, we included a total of 314 resident doctors, and no one was excluded due to incomplete information. The control group included 152 doctors who underwent rotation or further education from November 2021 to August 2022 and 162 doctors from September 2022 to June 2023 were allocated to the PDCA group. The PDCA group adopted the PDCA management of the timeliness of EMR documentation. The basic characteristics of all the doctors are presented in Table 1. The majority of the participants were under 30 years old (n = 204, 65.0%), had a bachelor\u0026apos;s degree (n = 197, 62.7%), and worked 40 to 60 hours per week (n = 264, 84.1%). There was no significant difference in most of the basic information between the two groups, while the doctors in the PDCA group had a significantly shorter PGY compared to the control group (P \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe effectiveness of PDCA management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no significant difference in the total number of EMRs between the two groups. As indicated in Table 2, the PDCA cycle management mode significantly reduced the percentage of late EMRs (5.40% vs. 2.56%, P = 0.005) and unqualified EMRs (1.05% vs. 0.00%, P \u0026lt; 0.001). Moreover, the percentage of EMRs with first disease course records that weren\u0026apos;t completed within 8 hours after the admission of patients in the PDCA group was significantly lower than that in the control group (0.24% vs. 0.00%, P = 0.023). In addition, the timeliness of the 3-day disease course records (2.93% vs. 0.61%, P = 0.001) and the first ward round records (0.36% vs. 0.00%, P = 0.035) also improved significantly due to the implementation of the PDCA cycle. The numbers and percentages of EMRs with different timeliness issues were significantly reduced after employing PDCA management (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors influencing unqualified medical records\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the effectiveness of the PDCA cycle management model, we identified the influencing factors of the occurrence of unqualified EMRs and eliminated their interference using univariate and multivariate binary logistic regression. There were six potential confounding factors, including age, sex, PGY, academic degree, working hours, and major, of which only age significantly affected the occurrence of unqualified EMRs (P = 0.010). Compared with doctors who are older than 30 years old, those who are younger than 30 years old have a higher occurrence of unqualified EMRs, with an OR of 3.622 (95% CI 1.363\u0026ndash;9.621) and a relative probability of 262.2% (3.622 \u0026ndash; 1 = 2.622). All ORs of the influencing factors can be found in Table 3. Incorporating age and PDCA management into a multivariate regression analysis, the results showed that PDCA cycle management can still significantly reduce the occurrence of unqualified EMRs (P \u0026lt; 0.001) with an adjusted OR of 0.166 (95% CI 0.067\u0026ndash;0.416) and a probability of 83.4% (0.166 \u0026ndash; 1 = \u0026ndash;0.834) (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors influencing late medical records\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the univariate regression analysis, age (P = 0.028), sex (P = 0.044), PGY, and major were determined to be confounding factors (Table 5) and were incorporated into the multiple regression analysis. The results showed that the PDCA cycle management mode could still effectively lower the occurrence of late EMRs (P \u0026lt; 0.001) with an adjusted OR of 0.318 (95% CI 0.181\u0026ndash;0.557) and a probability of 68.2% (0.318 \u0026ndash; 1 = \u0026ndash;0.682). Age may be a potential predictive factor for the occurrence of late EMR documentation (P = 0.055). The impact of sex on the occurrence of late EMRs was not statistically significant (P = 0.155). Compared to doctors who have graduated for 4 years or more, doctors who have graduated for 2 years have a higher incidence of late EMR (P = 0.008), with an OR of 1.027 (95% CI 1.301\u0026ndash;5.994) and a probability of 30.1% (1.301 \u0026ndash; 1 = 0.301). Furthermore, the incidence of late EMRs among doctors in internal medicine and other majors was significantly lower than that of doctors in head and neck tumors (P = 0.021), with an OR of 2.537 (95% CI 1.161\u0026ndash;5.543) and a probability of 153.7% (2.537 \u0026ndash; 1 = 1.537) (Table 6).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe ability of resident doctors to accurately document and efficiently share patient information through EMRs has emerged as a key area of focus for medical educators and policymakers. This focus is justified by the crucial role that timely and accurate EMR entries play in effective patient care and coordination among healthcare professionals. Timely EMR updates are critical not just for emergency scenarios, where they can be life-saving, but also for reducing the risk of medical errors across all areas of healthcare delivery(\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, the implementation of EMRs has introduced unintended challenges, including increased work hours, time constraints, and potential miscommunication between patients and healthcare providers(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Therefore, the application of scientific management tools and the provision of EMR training are indispensable components in the training of resident doctors, ensuring they are equipped to navigate these complexities effectively and maintain the highest standards of patient care.\u003c/p\u003e \u003cp\u003eIn response to these challenges, our study explored the Plan-Do-Check-Act (PDCA) cycle as a strategic intervention to increase the timeliness of EMR documentation among resident doctors. The PDCA cycle, a structured quality management method, involved establishing a medical records review team and formulating a comprehensive training plan in the planning phase(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). During the implementation phase, training was delivered according to predefined education standards. Subsequently, in the evaluation phase, we identified challenges, analyzed underlying causes, and incorporated improvement measures into subsequent PDCA cycles, fostering a continuous enhancement environment. Notably, practical measures such as network meetings and real-time communication were employed for immediate response to delays in EMR documentation. Our findings indicate a significant increase in the proportion of timely EMR entries following the PDCA implementation, underscoring its effectiveness in improving EMR management practices among residents. This suggests that structured, cyclic approaches to process management can profoundly impact EMR documentation quality.\u003c/p\u003e \u003cp\u003eAdditionally, our analysis identified key factors contributing to delayed or suboptimal medical record entries. We noted correlations between delayed EMR entries and variables like the age of resident doctors, years since graduation, and medical specialization. For instance, residents in their second post-graduation year often face higher patient loads than in their first year, potentially leading to increased workload and time constraints, which can impede timely documentation. Despite these variables, the implementation of the PDCA cycle was the most significant factor in reducing delayed entries, highlighting its potential to transform EMR practices.\u003c/p\u003e \u003cp\u003eThis study has several limitations that warrant discussion. The application of the PDCA cycle in this study was restricted to resident doctors, and its effectiveness in enhancing EMR timeliness among more seasoned physicians warrants further exploration. Moreover, our study's findings, derived from a single Chinese hospital, may not be universally applicable due to variations in medical records management practices across different healthcare systems. To overcome these limitations, future research should extend to multi-center studies in varied healthcare settings and include larger sample sizes. Such studies could provide a more comprehensive evaluation of the PDCA cycle's efficacy across diverse medical environments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study successfully developed and implemented a PDCA cycle tailored for managing the timeliness of EMR among resident doctors. Our findings demonstrate that the application of the PDCA management method significantly enhances the timeliness of EMR documentation while concurrently reducing the incidence of unqualified or delayed entries. These encouraging results underscore the potential of PDCA cycle management as a robust framework not only for improving EMR management but also as a versatile tool in the broader context of resident training and skill development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePlan-Do-Check-Act (PDCA)\u003c/p\u003e\n\u003cp\u003eElectronic medical records (EMRs)\u003c/p\u003e\n\u003cp\u003epostgraduate year (PGY)\u003c/p\u003e\n\u003cp\u003eodds ratios (ORs)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethe Statistical Package for the Social Sciences (SPSS)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from the individual participants or their guardians. The study was approved by the medical ethics committee of West China Hospital, Sichuan University, China. All procedures involving human participants performed in this study were in accordance with the ethical standards of the institutional research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) reported there is no funding associated with the work featured in this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQZ designed this work. JC collected materials and wrote this manuscript. QZ edited and revised the manuscript. Both authors have read the manuscript and approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbid M, Schneider AB. Clinical Informatics and the Electronic Medical Record. \u003cem\u003eThe Surgical clinics of North America\u003c/em\u003e (2023) 103(2):247-58. Epub 2023/03/23. doi: 10.1016/j.suc.2022.11.005.\u003c/li\u003e\n\u003cli\u003eEscobar GJ, Soltesz L, Schuler A, Niki H, Malenica I, Lee C. Prediction of Obstetrical and Fetal Complications Using Automated Electronic Health Record Data. \u003cem\u003eAmerican journal of obstetrics and gynecology\u003c/em\u003e (2021) 224(2):137-47.e7. 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Epub 2008/07/10. doi: 10.1371/journal.pone.0002626.\u003c/li\u003e\n\u003cli\u003eCarlson KL, McFadden SE, Barkin S. Improving Documentation Timeliness: A \u0026quot;Brighter Future\u0026quot; for the Electronic Medical Record in Resident Clinics. \u003cem\u003eAcademic medicine : journal of the Association of American Medical Colleges\u003c/em\u003e (2015) 90(12):1641-5. Epub 2015/06/25. doi: 10.1097/acm.0000000000000792.\u003c/li\u003e\n\u003cli\u003eSun AJ, Wang L, Go M, Eggers Z, Deng R, Maggio P, et al. Night-Time Communication at Stanford University Hospital: Perceptions, Reality and Solutions. \u003cem\u003eBMJ quality \u0026amp; safety\u003c/em\u003e (2018) 27(2):156-62. Epub 2017/10/23. doi: 10.1136/bmjqs-2017-006727.\u003c/li\u003e\n\u003cli\u003eMacgregor JM, Sticca R. General Surgery Residents\u0026apos; Views on Work Hours Regulations. \u003cem\u003eJournal of surgical education\u003c/em\u003e (2010) 67(6):376-80. Epub 2010/12/16. doi: 10.1016/j.jsurg.2010.07.008.\u003c/li\u003e\n\u003cli\u003eBennett KJ, Steen C. Electronic Medical Record Customization and the Impact Upon Chart Completion Rates. \u003cem\u003eFamily medicine\u003c/em\u003e (2010) 42(5):338-42. Epub 2010/05/13.\u003c/li\u003e\n\u003cli\u003eBarbour JR, Brothers TE, Wetherholt SF. Stimulation of Interest in Resident Completion of Necessary Administrative Work: Implementation and Evaluation of a Publicly Displayed \u0026quot;Administrative Grade\u0026quot;. \u003cem\u003eJournal of surgical education\u003c/em\u003e (2010) 67(1):48-51. Epub 2010/04/28. doi: 10.1016/j.jsurg.2009.10.006.\u003c/li\u003e\n\u003cli\u003eZhong X, Wu X, Xie X, Zhou Q, Xu R, Wang J, et al. A Descriptive Study on Clinical Department Managers\u0026apos; Cognition of the Plan-Do-Check-Act Cycle and Factors Influencing Their Cognition. \u003cem\u003eBMC medical education\u003c/em\u003e (2023) 23(1):294. Epub 2023/05/02. doi: 10.1186/s12909-023-04293-2.\u003c/li\u003e\n\u003cli\u003eChen J, Cai W, Lin F, Chen X, Chen R, Ruan Z. Application of the Pdca Cycle for Managing Hyperglycemia in Critically Ill Patients. \u003cem\u003eDiabetes therapy : research, treatment and education of diabetes and related disorders\u003c/em\u003e (2023) 14(2):293-301. Epub 2022/11/25. doi: 10.1007/s13300-022-01334-9.\u003c/li\u003e\n\u003cli\u003eZeng X, Huang X, Wang P, Liao J, Wu L, Liu J, et al. The Application of the Pdca Cycle in the Nutritional Management of Patients with Nasopharyngeal Carcinoma. \u003cem\u003eSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer\u003c/em\u003e (2023) 31(5):251. Epub 2023/04/11. doi: 10.1007/s00520-023-07724-4.\u003c/li\u003e\n\u003cli\u003eChen H, Wang P, Ji Q. 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Epub 2019/03/01. doi: 10.1016/j.vaccine.2019.02.031.\u003c/li\u003e\n\u003cli\u003eMinard JP, Turcotte SE, Lougheed MD. Asthma Electronic Medical Records in Primary Care: An Integrative Review. \u003cem\u003eThe Journal of asthma : official journal of the Association for the Care of Asthma\u003c/em\u003e (2010) 47(8):895-912. Epub 2010/09/22. doi: 10.3109/02770903.2010.4911411.\u003c/li\u003e\n\u003cli\u003eMoy AJ, Schwartz JM, Chen R, Sadri S, Lucas E, Cato KD, et al. Measurement of Clinical Documentation Burden among Physicians and Nurses Using Electronic Health Records: A Scoping Review. \u003cem\u003eJournal of the American Medical Informatics Association : JAMIA\u003c/em\u003e (2021) 28(5):998-1008. Epub 2021/01/13. doi: 10.1093/jamia/ocaa325.\u003c/li\u003e\n\u003cli\u003eNicolay CR, Purkayastha S, Greenhalgh A, Benn J, Chaturvedi S, Phillips N, et al. Systematic Review of the Application of Quality Improvement Methodologies from the Manufacturing Industry to Surgical Healthcare. \u003cem\u003eThe British journal of surgery\u003c/em\u003e (2012) 99(3):324-35. Epub 2011/11/22. doi: 10.1002/bjs.7803.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"90.80779944289694%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Baseline Characteristics of two groups before management.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl group (n=152)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDCA group (n=162)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (n=314)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex \u0026mdash; no. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e41 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e46 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e87 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e111 (73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e116 (71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e227 (72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge \u0026mdash; yr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e＜30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e103 (67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e101 (62.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e204 (65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e49 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e61 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e110 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic degree \u0026mdash; no. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003ebachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e100 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e97 (59.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e197 (62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e31 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e31 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e62 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003edoctor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e21 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e34 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e55 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor \u0026mdash; no. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003ehematological malignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e5 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e5 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e10 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003eThoracic tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e29 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e32 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e61 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003eAbdominal tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e32 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e38 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e70 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003eInternal medicine and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e52 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e37 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e89 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003eHead and neck tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e34 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e50 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e84 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking hours \u0026mdash; no. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.765\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;40 h per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e3 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e14 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e17 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e40-60 h per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e139 (91.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e125 (77.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e264 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026gt;60 h per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e10 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e23 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e33 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePGY \u0026mdash; no. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e25 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e56 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e81 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e45 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e56 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e101 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e41 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e28 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e69 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" valign=\"bottom\"\u003e\n \u003cp\u003e41 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" valign=\"bottom\"\u003e\n \u003cp\u003e22 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.13091922005571%\" valign=\"bottom\"\u003e\n \u003cp\u003e63 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\" valign=\"bottom\" colspan=\"5\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePGY: post graduate year\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e*: more than 3 years\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"766\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable2. Comparison of the medical records with different timeliness issues between two groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDCA group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe total number of medical records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e391 \u0026plusmn; 61.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e344.50 \u0026plusmn; 80.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e367 \u0026plusmn; 73.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.160\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe number of late medical records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.00 (13.00-34.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.00 (6.00-10.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.00 (9.00-25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe percentage of late medical records\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.40% (2.99%-8.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.56%\u0026nbsp;(1.42%-3.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.35% (2.44%-5.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe number of unqualified medical records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.50 (2.75-6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00 (0.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.5 (0.00-4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe percentage of unqualified medical records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.05%\u0026nbsp;(0.69%-1.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00%\u0026nbsp;(0.00%-0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.49% (0.00%-1.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe percentage of medical records with disease course records completion time exceeding 3 days for patients in stable conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.93%\u0026nbsp;(1.34%-5.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.61%\u0026nbsp;(0.43%-1.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.30% (0.60%-2.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe percentage of medical records with the first disease records completion time exceeding 8 hours after admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.24%\u0026nbsp;(0.00%-0.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.023\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe percentage of medical records with shift records that were not completed timely\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.21% (0.00%-0.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.063\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe percentage of medical records with stage summaries completion time exceeding 30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.481\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe percentage of first-ward-round records by the superior physicians with the completion time exceeding 48 hours after admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.36%\u0026nbsp;(0.00%-0.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00%\u0026nbsp;(0.00%-0.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.035\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe percentage of medical records with surgical records completion time exceeding 24 hours after surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.481\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe percentage of medical records that didn\u0026apos;t record the important results of examinations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00% (0.00%-0.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.912\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.41830065359477%\" valign=\"bottom\"\u003e\n \u003cp\u003eThe percentage of medical records with the homepage completion time exceeding 24 hours after discharge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.05% \u0026plusmn; 0.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.56% \u0026plusmn; 0.893%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.31%\u0026plusmn;0.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.366013071895425%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.645\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePDCA:\u0026nbsp;Plan-Do-Check-Act\u003c/p\u003e\n\u003cp\u003eData are Mean\u0026plusmn;SD or median (IQR).\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. Univariate logistic analysis of influencing factors for the occurrence of unqualified medical records\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.363; 9.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.673; 2.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDCA cycle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.066; 0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.116; 1.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.489; 3.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.483; 3.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003e4*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003ebachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.348; 2.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.231; 2.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\n \u003cp\u003edoctor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking hours\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\"\u003e\n \u003cp\u003e\u0026lt;40h per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.082; 11.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\"\u003e\n \u003cp\u003e40-60h per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.488; 9.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\"\u003e\n \u003cp\u003e\u0026gt;60h per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\"\u003e\n \u003cp\u003eHematological malignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.343; 9.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\"\u003e\n \u003cp\u003eThoracic tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.563; 3.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\"\u003e\n \u003cp\u003eAbdominal tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.355; 2.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\"\u003e\n \u003cp\u003eInternal medicine and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.144; 1.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.595645412130636%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\"\u003e\n \u003cp\u003eHead and neck tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618973561430794%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.52255054432349%\" colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003ePDCA:\u0026nbsp;Plan-Do-Check-Act\u003c/p\u003e\n \u003cp\u003ePGY: post graduate year\u003c/p\u003e\n \u003cp\u003e*: more than 3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4. Multivariate logistic analysis of influencing factors for the occurrence of unqualified medical records\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.529595015576323%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.485981308411215%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.08411214953271%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.529595015576323%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.485981308411215%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.08411214953271%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.529595015576323%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.485981308411215%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.296; 9.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.08411214953271%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.529595015576323%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.485981308411215%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.08411214953271%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDCA cycle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.529595015576323%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.485981308411215%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.08411214953271%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.529595015576323%\" valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.485981308411215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.067; 0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.08411214953271%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.529595015576323%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.485981308411215%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.08411214953271%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePDCA:\u0026nbsp;Plan-Do-Check-Act\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"96.72897196261682%\" colspan=\"7\" valign=\"bottom\" style=\"width: 99.8445%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5. Univariate logistic analysis of influencing factors for the occurrence of late medical records\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.067; 3.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.015; 2.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDCA cycle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.268; 0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.451; 2.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.047; 4.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.475; 2.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003e4*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003ebachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.337; 1.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.238; 1.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\n \u003cp\u003edoctor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking hours\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\"\u003e\n \u003cp\u003e\u0026lt;40h per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.143; 2.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\"\u003e\n \u003cp\u003e40-60h per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.357; 1.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\"\u003e\n \u003cp\u003e\u0026gt;60h per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\"\u003e\n \u003cp\u003eHematological malignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.048; 4.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\"\u003e\n \u003cp\u003eThoracic tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.508; 23.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\"\u003e\n \u003cp\u003eAbdominal tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.776; 3.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\"\u003e\n \u003cp\u003eInternal medicine and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.076; 4.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.626168224299064%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.373831775700936%\"\u003e\n \u003cp\u003eHead and neck tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.1993769470405%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.280373831775702%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" colspan=\"7\" style=\"width: 99.8445%;\"\u003e\n \u003cp\u003ePDCA:\u0026nbsp;Plan-Do-Check-Act\u003c/p\u003e\n \u003cp\u003ePGY: post graduate year\u003c/p\u003e\n \u003cp\u003e*: more than 3 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6. Multivariate logistic analysis of influencing factors for the occurrence of late medical records\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.987; 3.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.853; 2.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\n \u003cp\u003ePDCA cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.181; 0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.753; 4.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.301; 5.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.453; 2.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\" valign=\"bottom\"\u003e\n \u003cp\u003e4*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\n \u003cp\u003eMajor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\"\u003e\n \u003cp\u003eHematological malignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.145; 5.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\"\u003e\n \u003cp\u003eThoracic tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.374; 25.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\"\u003e\n \u003cp\u003eAbdominal tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.831; 4.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\"\u003e\n \u003cp\u003eInternal medicine and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.161; 5.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.594855305466238%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.11897106109325%\"\u003e\n \u003cp\u003eHead and neck tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.967845659163988%\" valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.14790996784566%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.128617363344052%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.631239935587764%\" colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003ePDCA:\u0026nbsp;Plan-Do-Check-Act\u003c/p\u003e\n \u003cp\u003ePGY: post graduate year\u003c/p\u003e\n \u003cp\u003e*: more than 3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"electronic medical records, Plan-Do-Check-Act cycle, timeliness, resident doctors","lastPublishedDoi":"10.21203/rs.3.rs-3881618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3881618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe role of the Plan-Do-Check-Act (PDCA) cycle in managing the timeliness of electronic medical records (EMRs) remains unclear. Therefore, this study aimed to evaluate the effect of PDCA management in improving the timeliness of EMR for resident doctors.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study had a before and after design. The resident doctors rotating in the Head and Neck Oncology Department of West China Hospital, Sichuan University from November 2021 to August 2022 were classified as the control group, which was managed by the current department practice. The resident doctors from September 2022 to June 2023 were included in the PDCA group, which was managed by the PDCA cycle. The incidences of late EMRs and unqualified EMRs were compared between the two groups and the influencing factors of the occurrence of late EMRs and unqualified EMRs were explored.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 314 resident doctors were included, with 162 doctors in the PDCA group and 152 doctors in the control group. The incidences of late EMRs (5.40% vs. 2.56%, P\u0026thinsp;=\u0026thinsp;0.005) and unqualified EMRs (1.05% vs. 0.00%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the PDCA group were significantly lower than those in the control group. The timeliness of the first disease course records (0.24% vs. 0.00%, P\u0026thinsp;=\u0026thinsp;0.023) and the first-ward-round records (0.36% vs. 0.00%, P\u0026thinsp;=\u0026thinsp;0.035) were also improved significantly. After incorporating confounding factors, including age, sex, academic degree, working hours, and major, PDCA management still significantly reduced the occurrence of unqualified EMRs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with an adjusted OR of 0.166 (95% CI 0.067\u0026ndash;0.416) and a probability of 83.4% (0.166\u0026ndash;1 = \u0026minus;\u0026thinsp;0.834).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study successfully developed PDCA management and revealed that it is beneficial to enhance the timeliness of EMR while concurrently reducing the incidence of unqualified or delayed entries among resident doctors.\u003c/p\u003e","manuscriptTitle":"Enhancing the Timeliness of EMR Documentation in Resident Doctors: The Role of PDCA Cycle Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 04:13:09","doi":"10.21203/rs.3.rs-3881618/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-27T04:17:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-26T14:08:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1990588829614601748086006236872842525","date":"2024-06-20T14:28:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33866070530526984247128831794545197448","date":"2024-06-19T16:27:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-28T13:23:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e852bb19-cfba-4eb3-b341-ff7ad4f5bcf3","date":"2024-03-25T15:38:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5baf10c4-2464-4b2e-b07d-ed0d99979496","date":"2024-03-22T16:15:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-14T18:13:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-14T18:00:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-20T13:29:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-20T13:27:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2024-01-20T12:59:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b0577a87-9e33-4bab-851b-38953345478d","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T17:28:03+00:00","versionOfRecord":{"articleIdentity":"rs-3881618","link":"https://doi.org/10.1186/s12909-024-06134-2","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2024-11-26 15:57:07","publishedOnDateReadable":"November 26th, 2024"},"versionCreatedAt":"2024-01-23 04:13:09","video":"","vorDoi":"10.1186/s12909-024-06134-2","vorDoiUrl":"https://doi.org/10.1186/s12909-024-06134-2","workflowStages":[]},"version":"v1","identity":"rs-3881618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3881618","identity":"rs-3881618","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}