Information Management Requirements of Interventional Surgery Based on the Perioperative Patient-Focused Model: A Qualitative Study

Information Management Requirements of Interventional Surgery Based on the Perioperative Patient-Focused Model: A Qualitative Study | 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 Information Management Requirements of Interventional Surgery Based on the Perioperative Patient-Focused Model: A Qualitative Study Yingying Jiang, Yahui Gao, Zhanao Liu, Qin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6289391/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: In recent years, information management systems (IMS) have been increasingly adopted by healthcare institutions to standardize workflows, reduce human error, and improve decision-making efficiency. Although IMS support documentation automation, equipment tracking, and vital sign monitoring in interventional surgery, existing systems emphasize operational efficiency over holistic patient-centered care. The perioperative patient-focused model provides a theoretical framework encompassing patient safety, physiological responses, behavioral responses, and healthcare system environment, along with highlighting the need for solutions for patient-centered care. However, only a few studies have explored whether IMS can be adapted to these dimensions in interventional surgery, particularly in resource-limited settings. Therefore, this study aims to analyze the information management requirements of interventional surgery from the perspective of the perioperative patient-focused model, serving as a reference for constructing a scientific and standardized management system. Methods: Healthcare professionals in interventional surgery from a tertiary hospital in Suzhou were selected for semi-structured interviews utilizing purposive sampling. The themes from the interview data were analyzed, summarized, and extracted by applying Braun and Clarke’s thematic analysis method. Results: The following four themes were identified: (1) optimization needs of the function of patient safety assurance, (2) improvement requirements of perioperative physiological monitoring and intervention, (3) need for the assessment and support of patient behavioral responses, and (4) improvement requirements for the management of healthcare system environment. Conclusions: The information management requirements of perioperative patients are multidimensional and diverse. Healthcare professionals should prioritize assessment and implement targeted interventions addressing patient safety, physiological responses, behavioral patterns, and healthcare system environments to enhance perioperative management and ensure patient safety and health. Perioperative patients Patient-focused model Interventional surgery Information management Qualitative research Introduction The continuous development of information technology has led to the gradual adoption of information management systems (IMS) in nursing work to improve the quality of nursing and work efficiency [ 1 ], especially in high-risk interventional surgery [ 2 ]. These systems aid in ensuring patient safety, streamlining care delivery, and increasing clinical productivity [ 3 ]. IMS can serve as a clinical care decision-making center and not just a functional system [ 4 ] for data collection. Moreover, a previous study found a 28% reduction in perioperative complication rates in hospital patients following the implementation of this system, further supporting its role in improving care quality [ 5 ]. Current mainstream IMS for surgery patients primarily consist of the following three functional modules: (1) Preoperative assessment systems that integrate electronic health records, picture archiving and communication systems, and laboratory information systems to automate surgical risk evaluation [ 6 ]. (2) Intraoperative navigation systems that employ augmented reality technology to overlay three-dimensional reconstructed imaging with real-time physiological monitoring data, providing surgeons with multidimensional operational guidance [ 7 ]. (3) Postoperative follow-up modules that utilize Internet of things(IoT)devices to continuously monitor vital signs and machine learning algorithms to predict complication risks [ 8 ]. However, current clinical systems are inefficient in perioperative care management, particularly in integrating patients' physiological, psychological, and environmental parameters to optimize system performance and care delivery [ 9 , 10 ]. The perioperative patient-focused model offers a theoretical framework comprising patient safety, physiological responses, behavioral responses, and healthcare system environment, as well as highlights the necessity of solutions for patient-centered care [ 11 ]. This study aims to investigate the requirements, existing limitations, and optimization strategies of IMS in interventional surgery through qualitative research grounded in the perioperative patient-focused model. The findings from this study will provide a theoretical foundation and practical guidance for refining these information systems to optimize healthcare management. Methods This study employed a qualitative research design involving individual interviews with healthcare professionals specializing in interventional procedures at a tertiary hospital in Suzhou between November 2024 and December 2024. This research adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) [ 12 ]. The study protocol received ethical approval from the Ethics Committee of Suzhou Municipal Hospital (approval no.: K-2024-176-K01). Study setting and participants This study implemented a purposeful sampling method with the following inclusion criteria: professional experience of ≥ 5 years in interventional surgical practice or nursing; education level of bachelor's degree or higher; technological proficiency as demonstrated by experience using IMS; and signed informed consent for voluntary participation. Data collection An interview outline was developed based on the perioperative patient-focused model, literature review [ 13 – 14 ], and discussions within the research team (see Table 1 ). Two experienced qualitative researchers conducted one-on-one semi-structured interviews in a quiet setting. Each interview lasted 30–60 min and was recorded and transcribed verbatim. Audio recordings were transcribed within 24 h and analyzed using NVivo 12. All data were independently coded by two researchers. The entire study process strictly followed qualitative research norms, and the content was not evaluated or implied during the interview process to establish the authenticity and integrity of data collection. One qualitative research expert guided this study to confirm its scientific nature and rigor. Finally, the participants validated the results to ensure credibility. Analysis Braun and Clarke’s thematic analysis [ 15 ] was applied to assess the data. Based on the perioperative patient-focused model, core issues were identified through open coding and categorized into the following four domains: patient safety, physiological responses, behavioral responses, and healthcare system environment. Furthermore, the study adhered to the qualitative research standards. Interviewers avoided leading questions or biases. A qualitative research expert supervised the process to ensure rigor. Table 1 Interview guide Dimensions Questions Follow ups Patient safety 1.What critical patient safety issues require attention in daily work? Which safety management processes should be integrated into the system? 2.How does the current system handle near-miss event reporting (e.g., medication errors, equipment misconnections)? Are these incidents systematically analyzed for root causes? 3.What non-technical skills (e.g., team communication, situational awareness) should the system support to enhance safety protocols? Physiological responses 4.How effective is the current system in monitoring perioperative physiological data (e.g., blood pressure, heart rate, SpO₂)? What monitoring functions are clinically valuable? 5.Are multi-parameter early warning scores (e.g., NEWS, qSOFA) integrated into the system? How actionable are these alerts? 6.What real-time visualization tools (e.g., hemodynamic trend graphs, organ perfusion maps) would improve intraoperative decision-making? 7.Should the system incorporate closed-loop control (e.g., automated vasopressor titration based on blood pressure)? Why or why not? Behavioral responses 8.Can the system assess preoperative psychological states and postoperative behavioral reactions (e.g., rehabilitation compliance)? What challenges exist in managing psychological/behavioral responses? 9.Should the system provide personalized behavioral nudges (e.g., gamified rehabilitation exercises, mindfulness prompts)? Healthcare environment 10.What environmental factors most impact perioperative management? What are the shortcomings in resource management (e.g., equipment, supplies) and environmental monitoring (e.g., temperature, air quality)? 11.How does the system currently track equipment utilization rates (e.g., fluoroscopy machines, stent inventory)? What metrics are missing? 12.Could predictive maintenance algorithms reduce device downtime? What data inputs would be needed? Results This study included 12 interventional healthcare professionals (five males and seven females), including three interventional surgeons, seven interventional nurses, and two interventional technicians. The age range of the participants was 29–50 years (median: 39.00 years). In terms of educational background, eight participants had a bachelor’s degree, three had a master’s degree, and one had a doctoral degree. In the case of professional titles, three participants were charge nurses, two were attending physicians, six were associate chief nurses, and one was an associate chief physician. The years of experience of the participants in interventional surgery ranged from 6 to 26 years (median: 16.00 years). Our analysis revealed themes in the following four categories: 1) optimization needs of the function of patient safety assurance, 2) improvement requirements of perioperative physiological response monitoring and intervention, 3) need for the assessment and support of patient behavioral response, and 4) improvement requirements for the management of healthcare system environment. Optimization Needs of the Function of Patient Safety Assurance All participants acknowledged that IMS enhance patient safety during preoperative preparation, intraoperative monitoring, and postoperative evaluation. However, current systems are weak in risk prediction and emergency response, particularly concerning early warning capabilities for intraoperative emergencies. Clinicians emphasized the necessity for the real-time intraoperative monitoring of vital signs with prompt alerts in the event of anomalies. "Sometimes, a patient's blood pressure drops suddenly. If the system warns us early, we can respond more quickly." (P#1: interventional surgeon, 26 years of experience). "We currently rely on manual risk assessment, which can be delayed. A system that generates risk scores based on patient conditions would be invaluable." (P#4: interventional nurse specialist, 22 years of experience). All stakeholders emphasized the critical need for real-time risk prediction functionality to improve patient safety throughout clinical workflows. Additionally, participants unanimously advocated for increasing the postoperative surveillance capabilities of IMS, particularly highlighting the following three critical needs: "Despite standardized protocols, our current system inadequately stratifies infection risks. For instance, patients with diabetes who are undergoing peripheral vascular interventions have a 23% higher rate of surgical site infection (SSI) compared to their non-diabetic cohorts. However, the IMS lacks dynamic risk scoring. We still manually flag high-risk cases based on inflammatory markers such as CRP levels." (P#12: senior interventional technician, 17 years of experience). "Some patients are discharged postoperatively, making monitoring their status in real-time difficult. A standardized follow-up and reminder mechanism in the system would significantly address this issue." (P#7: interventional nurse specialist, 12 years of experience). "If the postoperative system could automatically alert clinicians to critical care priorities, patient safety may improve substantially." (P#8: interventional nurse specialist, 9 years of experience). Improvement Requirements of Perioperative Physiological Response Monitoring and Intervention Participants stressed the need for improved data visualization (e.g., trend graphs) and real-time analysis. "Intraoperative data change rapidly. Trend analysis with actionable suggestions would reduce manual efforts." (P#1: interventional surgeon, 26 years of experience). "Rapid fluctuations in intraoperative data require real-time trend analysis and actionable recommendations to reduce manual interpretation and improve efficiency." (P#3: interventional surgeon, 15 years of experience). "Visual data representation, such as trend charts of blood pressure, would allow a quicker analysis compared to manually reviewing recorded values." (P#6: interventional nurse specialist, 14 years of experience). Moreover, the participants recommended using customizable alarm thresholds for individual patients to reduce false alerts. "Older patients may require adjustment in blood pressure ranges. Personalized settings would improve precision." (P#5: interventional nurse specialist, 16 years of experience). "Fixed alarm thresholds for postoperative patients with baseline abnormalities generate frequent false alerts, which require tedious manual adjustments." (P#11: senior interventional technician, 10 years of experience). Need for the Assessment and Support of Patient Behavioral Response Although preoperative psychological states significantly affect surgical outcomes, present systems lack assessment tools to evaluate this aspect. “Preoperative anxiety significantly affects surgical outcomes. A self-assessment tool with guided recommendations would improve preoperative preparation.” (P#2: interventional surgeon, 18 years of experience). “The delayed detection of patient anxiety is common. The proactive delivery of psychoeducational content via the system days before surgery could mitigate this limitation.” (P#10: interventional nurse specialist, 6 years of experience). Postoperative behavior management is pivotal in determining recovery outcomes. However, current healthcare information systems fail to provide comprehensive support in this domain. “Patients frequently request guidance on diet and exercise post-discharge. Automated rehabilitation recommendations would reduce staff workload.” (P#8: interventional nurse specialist, 9 years of experience). “Intelligent postoperative modules, such as reminders for follow-up or symptom tracking, would optimize recovery outcomes.” (P#11: senior interventional technician, 10 years of experience). Improvement Requirements for the Management of Healthcare System Environment Participants highlighted the following inefficiencies in resource tracking: “Errors in the usage of high-cost consumables (e.g., stents) are frequent. Automated intraoperative inventory tracking is critical.” (P#6: interventional nurse specialist, 14 years of experience). “Manual post-procedure documentation of consumables is time-consuming. Automated inventory and billing reports would streamline workflows.” (P#9: interventional nurse specialist, 24 years of experience). “Precisely tracking surgical supplies (e.g., gauze and instruments) through digital systems would minimize waste.” (P#10: interventional nurse specialist, 6 years of experience). Environmental factors were suggested to directly affect surgical safety in the following ways: “Although temperature and humidity are vital for aseptic conditions, these parameters are manually recorded. Automated monitoring with alerts is needed.” (P#3: interventional surgeon, 15 years of experience). “Environmental parameters should be adopted according to procedure types(e.g., neurointervention vs. vascular intervention) to optimize safety and efficiency.” (P#8: interventional nurse specialist, 9 years of experience). Discussion Optimization Needs of the Function of Patient Safety Assurance Patient safety is a core element of perioperative management. Although IMS play a vital role in this aspect [ 16 ], the existing systems have notable deficiencies in risk warning and postoperative complication management, with the intraoperative emergency warning function being especially weak. Consequently, the interviewees suggested enhancing the intelligent risk warning function. In particular, the system should be able to provide real-time alarms in the case of the Crucial Surgical Phases and patients with exceptional circumstances. Current systems can record vital signs; however, they cannot perform intelligent risk prediction and manage suggestions. Nevertheless, real-time risk warnings are essential for improving patient safety [ 17 – 18 ]. Systems can integrate patient medical records, real-time data of vital signs, and surgical process information to conduct anomaly trend analysis and issue alerts [ 19 – 20 ]. In the future, systems should focus on developing dynamic risk prediction models based on big data and artificial intelligence to enhance the accuracy and timeliness of risk warnings. A major shortcoming of current systems is that their postoperative complication management functionality is weak. This limitation is especially prominent in terms of risk reminders for high-risk patients and monitoring patient status after discharge, which remains largely dependent on the experience of healthcare personnel. Information systems can improve postoperative complication management by incorporating the function of postoperative follow-up, including modules such as infection risk prediction and the report of postoperative abnormal status. Simultaneously, self-management support can be provided via mobile terminal applications to enable the closed-loop management of postoperative care. Improvement Requirements of Intraoperative Physiological Response Monitoring and Intervention Dynamic monitoring and interventions to control physiological responses are crucial for perioperative patient management; however, existing systems fail to completely fulfill the needs of healthcare staff in the areas of data presentation and personalized alarm settings. Respondents suggested that the current data presentation mode of the present system is relatively simple and lacks a trend map and visual analysis tools, thereby increasing the burden of information interpretation and judgment on the medical staff. The introduction of data visualization technology (such as a trend analysis chart) will allow the system to present the changing trends of key physiological parameters in real time and assist the medical staff in quickly determining the patient status. Systems can also incorporate intelligent analysis algorithms to facilitate the management of suggestions for abnormal situations, thus reducing human judgment errors and improving efficiency [ 21 ]. The large number of "false alarms" caused by a fixed alarm threshold is another prominent issue [ 22 ]. Personalized index settings can adjust the monitoring range according to the individual differences of patients, leading to improved monitoring accuracy [ 23 ]. Furthermore, systems can integrate the patient's medical history, preoperative evaluation data, and postoperative recovery and dynamically adjust the ranges of monitoring indicators. This approach will not only help reduce false alarms but also provide healthcare professionals with more accurate decision-making support, ultimately enhancing monitoring effectiveness. Need for the Assessment and Support of Patient Behavioral Response Preoperative psychological state and postoperative behavior management directly affect patient safety and recovery outcomes during the perioperative period [ 24 ]. However, the current system pays limited attention to these aspects. In the case of the preoperative psychological state, the current system does not offer functionality for preoperative psychological assessment and intervention, which can have a critical impact on patient cooperation and surgical outcomes. Moreover, a psychological assessment module could aid patients in identifying preoperative anxiety and fear levels through questionnaires or self-assessment tools. Combining the information system with personalized psychological intervention suggestions (such as relaxation training and preoperative education video push) can effectively relieve patients' tension and enhance their preoperative preparation. The lack of postoperative behavioral management function increases the work burden on medical staff and can affect patient rehabilitation. Hence, information systems should introduce a postoperative rehabilitation module, encompassing dietary advice, activity guidance, medication reminders, and other personalized content. Along with the push function of the mobile terminal, the system can dynamically track patients' postoperative behavior and provide feedback that will help them standardize their behavior and augment their rehabilitation effect. Improvement Requirements for the Management of Healthcare System Environment The operating room environment directly influences the quality of perioperative management. Accordingly, respondents suggested that optimizing material management and the functionality of environmental parameter monitoring would improve the overall operational efficiency of the operating room. The absence of material management and deployment efficiency, particularly the statistical omissions and errors in the usage of high-value consumables, severely affects the quality of operating room management [ 25 ]. Thus, information systems should automatically track the surgical materials and record their use and location. Concurrently, systems could utilize historical surgical data to predict material demand, thereby assisting in optimizing resource allocation and reducing waste and omission. Manually recording environmental parameters (such as temperature, humidity, and air quality) is inefficient, potentially leading to inaccurate recordings or delays. Hence, systems should introduce automatic monitoring and early warning modules to collect environmental data in real time and provide prompt alerts of abnormal conditions. Information systems should also refine ecological settings (e.g., specific humidity levels or air filtration requirements) according to the procedure type to improve the reliability of aseptic procedures and mitigate postoperative infection risk. Conclusion This study conducted qualitative interviews and revealed the needs and optimization directions for an IMS in surgical intervention based on the perioperative patient-focused model. The study findings indicated that system optimization could aid in better meeting the needs of perioperative management by concentrating on the four key areas: patient safety, data monitoring, psychological support, and resource management. These research results suggest that the adaptability and practicability of the current information system can be improved by incorporating certain functionalities, including intelligent risk assessment, evaluation of the patient's psychological state, monitoring of intraoperative physiological response, postoperative behavioral support, and follow-up. Finally, future research should combine multi-center data to validate and optimize information systems in different hospitals and for various surgical types, contributing to the further improvement of IMS and providing more scientific and precise decision support for operating room management. Declarations Acknowledgements The authors would like to thank the study participants and declare no conflict of interest. Authors’contributions Yingying Jiang, Yahui Gao and Qin Zhang have all made substantial contributions to conception and design of the study, acquisition of data, analyses and interpretation of data.Yingying Jiang wrote the initial draft of the manuscript, and Yahui Gao and Zhanao Liu were involved in revising it critically for important intellectual content. All authors have given fnal approval of the version to be published, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Fundings This work was supported by the 2024 Hospital Management Innovation Research Project of Jiangsu Provincial Hospital Association (JSYGY-3-2024-536) and the 2024 Suzhou Municipal Hospital Cohort Project and Evidence-Based Nursing Practice Project (SZFCXKHL202405). Availability of data and materials The dataset supporting the conclusions of this article is included within the article and its additional file. Competing interests The authors declare no competing interests. Ethics approval and content to participate The study protocol received ethical approval from the Ethics Committee of Suzhou Municipal Hospital (Approval No. K-2024-176-K01). This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Consent for publication Not applicable. References Lu H, Guojiao T, Min X et al. Information management practice of operatingroom instruments based on whole process traceability.JNS.2023;38(24):52–5. Yaoyao X, Yi L, Xiaoyun X et al. Study on information needs and self-management of cardiac rehabilitation in patients undergoing percutaneous coronary intervention.CNJ.2023;58(04):398–405. Tsirintani M. Web Quality Assurance of Information in Healthcare. Stud Health Technol Inf. 2022;295:442–5. Topol EJ. 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These systems aid in ensuring patient safety, streamlining care delivery, and increasing clinical productivity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. IMS can serve as a clinical care decision-making center and not just a functional system [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] for data collection. Moreover, a previous study found a 28% reduction in perioperative complication rates in hospital patients following the implementation of this system, further supporting its role in improving care quality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent mainstream IMS for surgery patients primarily consist of the following three functional modules: (1) Preoperative assessment systems that integrate electronic health records, picture archiving and communication systems, and laboratory information systems to automate surgical risk evaluation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. (2) Intraoperative navigation systems that employ augmented reality technology to overlay three-dimensional reconstructed imaging with real-time physiological monitoring data, providing surgeons with multidimensional operational guidance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. (3) Postoperative follow-up modules that utilize Internet of things(IoT)devices to continuously monitor vital signs and machine learning algorithms to predict complication risks [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, current clinical systems are inefficient in perioperative care management, particularly in integrating patients' physiological, psychological, and environmental parameters to optimize system performance and care delivery [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The perioperative patient-focused model offers a theoretical framework comprising patient safety, physiological responses, behavioral responses, and healthcare system environment, as well as highlights the necessity of solutions for patient-centered care [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to investigate the requirements, existing limitations, and optimization strategies of IMS in interventional surgery through qualitative research grounded in the perioperative patient-focused model. The findings from this study will provide a theoretical foundation and practical guidance for refining these information systems to optimize healthcare management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study employed a qualitative research design involving individual interviews with healthcare professionals specializing in interventional procedures at a tertiary hospital in Suzhou between November 2024 and December 2024. This research adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The study protocol received ethical approval from the Ethics Committee of Suzhou Municipal Hospital (approval no.: K-2024-176-K01).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting and participants\u003c/h2\u003e \u003cp\u003eThis study implemented a purposeful sampling method with the following inclusion criteria: professional experience of \u0026ge;\u0026thinsp;5 years in interventional surgical practice or nursing; education level of bachelor's degree or higher; technological proficiency as demonstrated by experience using IMS; and signed informed consent for voluntary participation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eAn interview outline was developed based on the perioperative patient-focused model, literature review [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and discussions within the research team (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Two experienced qualitative researchers conducted one-on-one semi-structured interviews in a quiet setting. Each interview lasted 30\u0026ndash;60 min and was recorded and transcribed verbatim. Audio recordings were transcribed within 24 h and analyzed using NVivo 12. All data were independently coded by two researchers. The entire study process strictly followed qualitative research norms, and the content was not evaluated or implied during the interview process to establish the authenticity and integrity of data collection. One qualitative research expert guided this study to confirm its scientific nature and rigor. Finally, the participants validated the results to ensure credibility.\u003c/p\u003e\n\u003ch3\u003eAnalysis\u003c/h3\u003e\n\u003cp\u003eBraun and Clarke\u0026rsquo;s thematic analysis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] was applied to assess the data. Based on the perioperative patient-focused model, core issues were identified through open coding and categorized into the following four domains: patient safety, physiological responses, behavioral responses, and healthcare system environment. Furthermore, the study adhered to the qualitative research standards. Interviewers avoided leading questions or biases. A qualitative research expert supervised the process to ensure rigor.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterview guide\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuestions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFollow ups\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient safety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.What critical patient safety issues require attention in daily work?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich safety management processes should be integrated into the system?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.How does the current system handle near-miss event reporting (e.g., medication errors, equipment misconnections)?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAre these incidents systematically analyzed for root causes?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.What non-technical skills (e.g., team communication, situational awareness) should the system support to enhance safety protocols?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysiological responses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.How effective is the current system in monitoring perioperative physiological data (e.g., blood pressure, heart rate, SpO₂)?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhat monitoring functions are clinically valuable?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.Are multi-parameter early warning scores (e.g., NEWS, qSOFA) integrated into the system?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow actionable are these alerts?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.What real-time visualization tools (e.g., hemodynamic trend graphs, organ perfusion maps) would improve intraoperative decision-making?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.Should the system incorporate closed-loop control (e.g., automated vasopressor titration based on blood pressure)?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhy or why not?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral responses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.Can the system assess preoperative psychological states and postoperative behavioral reactions (e.g., rehabilitation compliance)?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhat challenges exist in managing psychological/behavioral responses?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.Should the system provide personalized behavioral nudges (e.g., gamified rehabilitation exercises, mindfulness prompts)?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.What environmental factors most impact perioperative management?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhat are the shortcomings in resource management (e.g., equipment, supplies) and environmental monitoring (e.g., temperature, air quality)?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.How does the system currently track equipment utilization rates (e.g., fluoroscopy machines, stent inventory)?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhat metrics are missing?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.Could predictive maintenance algorithms reduce device downtime? What data inputs would be needed?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 12 interventional healthcare professionals (five males and seven females), including three interventional surgeons, seven interventional nurses, and two interventional technicians. The age range of the participants was 29\u0026ndash;50 years (median: 39.00 years). In terms of educational background, eight participants had a bachelor\u0026rsquo;s degree, three had a master\u0026rsquo;s degree, and one had a doctoral degree. In the case of professional titles, three participants were charge nurses, two were attending physicians, six were associate chief nurses, and one was an associate chief physician. The years of experience of the participants in interventional surgery ranged from 6 to 26 years (median: 16.00 years). Our analysis revealed themes in the following four categories: 1) optimization needs of the function of patient safety assurance, 2) improvement requirements of perioperative physiological response monitoring and intervention, 3) need for the assessment and support of patient behavioral response, and 4) improvement requirements for the management of healthcare system environment.\u003c/p\u003e\n\u003ch3\u003eOptimization Needs of the Function of Patient Safety Assurance\u003c/h3\u003e\n\u003cp\u003eAll participants acknowledged that IMS enhance patient safety during preoperative preparation, intraoperative monitoring, and postoperative evaluation. However, current systems are weak in risk prediction and emergency response, particularly concerning early warning capabilities for intraoperative emergencies. Clinicians emphasized the necessity for the real-time intraoperative monitoring of vital signs with prompt alerts in the event of anomalies.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Sometimes, a patient's blood pressure drops suddenly. If the system warns us early, we can respond more quickly.\" (P#1: interventional surgeon, 26 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\"We currently rely on manual risk assessment, which can be delayed. A system that generates risk scores based on patient conditions would be invaluable.\" (P#4: interventional nurse specialist, 22 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAll stakeholders emphasized the critical need for real-time risk prediction functionality to improve patient safety throughout clinical workflows.\u003c/p\u003e \u003cp\u003eAdditionally, participants unanimously advocated for increasing the postoperative surveillance capabilities of IMS, particularly highlighting the following three critical needs:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Despite standardized protocols, our current system inadequately stratifies infection risks. For instance, patients with diabetes who are undergoing peripheral vascular interventions have a 23% higher rate of surgical site infection (SSI) compared to their non-diabetic cohorts. However, the IMS lacks dynamic risk scoring. We still manually flag high-risk cases based on inflammatory markers such as CRP levels.\" (P#12: senior interventional technician, 17 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Some patients are discharged postoperatively, making monitoring their status in real-time difficult. A standardized follow-up and reminder mechanism in the system would significantly address this issue.\" (P#7: interventional nurse specialist, 12 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\"If the postoperative system could automatically alert clinicians to critical care priorities, patient safety may improve substantially.\" (P#8: interventional nurse specialist, 9 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImprovement Requirements of Perioperative Physiological Response Monitoring and Intervention\u003c/h2\u003e \u003cp\u003eParticipants stressed the need for improved data visualization (e.g., trend graphs) and real-time analysis.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Intraoperative data change rapidly. Trend analysis with actionable suggestions would reduce manual efforts.\" (P#1: interventional surgeon, 26 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Rapid fluctuations in intraoperative data require real-time trend analysis and actionable recommendations to reduce manual interpretation and improve efficiency.\" (P#3: interventional surgeon, 15 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Visual data representation, such as trend charts of blood pressure, would allow a quicker analysis compared to manually reviewing recorded values.\" (P#6: interventional nurse specialist, 14 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMoreover, the participants recommended using customizable alarm thresholds for individual patients to reduce false alerts.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Older patients may require adjustment in blood pressure ranges. Personalized settings would improve precision.\" (P#5: interventional nurse specialist, 16 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Fixed alarm thresholds for postoperative patients with baseline abnormalities generate frequent false alerts, which require tedious manual adjustments.\" (P#11: senior interventional technician, 10 years of experience).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNeed for the Assessment and Support of Patient Behavioral Response\u003c/h3\u003e\n\u003cp\u003eAlthough preoperative psychological states significantly affect surgical outcomes, present systems lack assessment tools to evaluate this aspect.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Preoperative anxiety significantly affects surgical outcomes. A self-assessment tool with guided recommendations would improve preoperative preparation.\u0026rdquo; (P#2: interventional surgeon, 18 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The delayed detection of patient anxiety is common. The proactive delivery of psychoeducational content via the system days before surgery could mitigate this limitation.\u0026rdquo; (P#10: interventional nurse specialist, 6 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePostoperative behavior management is pivotal in determining recovery outcomes. However, current healthcare information systems fail to provide comprehensive support in this domain.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Patients frequently request guidance on diet and exercise post-discharge. Automated rehabilitation recommendations would reduce staff workload.\u0026rdquo; (P#8: interventional nurse specialist, 9 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Intelligent postoperative modules, such as reminders for follow-up or symptom tracking, would optimize recovery outcomes.\u0026rdquo; (P#11: senior interventional technician, 10 years of experience).\u003c/em\u003e \u003c/p\u003e\n\u003ch3\u003eImprovement Requirements for the Management of Healthcare System Environment\u003c/h3\u003e\n\u003cp\u003eParticipants highlighted the following inefficiencies in resource tracking:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Errors in the usage of high-cost consumables (e.g., stents) are frequent. Automated intraoperative inventory tracking is critical.\u0026rdquo; (P#6: interventional nurse specialist, 14 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Manual post-procedure documentation of consumables is time-consuming. Automated inventory and billing reports would streamline workflows.\u0026rdquo; (P#9: interventional nurse specialist, 24 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Precisely tracking surgical supplies (e.g., gauze and instruments) through digital systems would minimize waste.\u0026rdquo; (P#10: interventional nurse specialist, 6 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eEnvironmental factors were suggested to directly affect surgical safety in the following ways:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Although temperature and humidity are vital for aseptic conditions, these parameters are manually recorded. Automated monitoring with alerts is needed.\u0026rdquo; (P#3: interventional surgeon, 15 years of experience).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Environmental parameters should be adopted according to procedure types(e.g., neurointervention vs. vascular intervention) to optimize safety and efficiency.\u0026rdquo; (P#8: interventional nurse specialist, 9 years of experience).\u003c/em\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOptimization Needs of the Function of Patient Safety Assurance\u003c/h2\u003e \u003cp\u003ePatient safety is a core element of perioperative management. Although IMS play a vital role in this aspect [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the existing systems have notable deficiencies in risk warning and postoperative complication management, with the intraoperative emergency warning function being especially weak. Consequently, the interviewees suggested enhancing the intelligent risk warning function. In particular, the system should be able to provide real-time alarms in the case of the Crucial Surgical Phases and patients with exceptional circumstances. Current systems can record vital signs; however, they cannot perform intelligent risk prediction and manage suggestions. Nevertheless, real-time risk warnings are essential for improving patient safety [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Systems can integrate patient medical records, real-time data of vital signs, and surgical process information to conduct anomaly trend analysis and issue alerts [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the future, systems should focus on developing dynamic risk prediction models based on big data and artificial intelligence to enhance the accuracy and timeliness of risk warnings.\u003c/p\u003e \u003cp\u003eA major shortcoming of current systems is that their postoperative complication management functionality is weak. This limitation is especially prominent in terms of risk reminders for high-risk patients and monitoring patient status after discharge, which remains largely dependent on the experience of healthcare personnel. Information systems can improve postoperative complication management by incorporating the function of postoperative follow-up, including modules such as infection risk prediction and the report of postoperative abnormal status. Simultaneously, self-management support can be provided via mobile terminal applications to enable the closed-loop management of postoperative care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImprovement Requirements of Intraoperative Physiological Response Monitoring and Intervention\u003c/h2\u003e \u003cp\u003eDynamic monitoring and interventions to control physiological responses are crucial for perioperative patient management; however, existing systems fail to completely fulfill the needs of healthcare staff in the areas of data presentation and personalized alarm settings. Respondents suggested that the current data presentation mode of the present system is relatively simple and lacks a trend map and visual analysis tools, thereby increasing the burden of information interpretation and judgment on the medical staff. The introduction of data visualization technology (such as a trend analysis chart) will allow the system to present the changing trends of key physiological parameters in real time and assist the medical staff in quickly determining the patient status. Systems can also incorporate intelligent analysis algorithms to facilitate the management of suggestions for abnormal situations, thus reducing human judgment errors and improving efficiency [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The large number of \"false alarms\" caused by a fixed alarm threshold is another prominent issue [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Personalized index settings can adjust the monitoring range according to the individual differences of patients, leading to improved monitoring accuracy [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, systems can integrate the patient's medical history, preoperative evaluation data, and postoperative recovery and dynamically adjust the ranges of monitoring indicators. This approach will not only help reduce false alarms but also provide healthcare professionals with more accurate decision-making support, ultimately enhancing monitoring effectiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNeed for the Assessment and Support of Patient Behavioral Response\u003c/h2\u003e \u003cp\u003ePreoperative psychological state and postoperative behavior management directly affect patient safety and recovery outcomes during the perioperative period [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, the current system pays limited attention to these aspects. In the case of the preoperative psychological state, the current system does not offer functionality for preoperative psychological assessment and intervention, which can have a critical impact on patient cooperation and surgical outcomes. Moreover, a psychological assessment module could aid patients in identifying preoperative anxiety and fear levels through questionnaires or self-assessment tools. Combining the information system with personalized psychological intervention suggestions (such as relaxation training and preoperative education video push) can effectively relieve patients' tension and enhance their preoperative preparation. The lack of postoperative behavioral management function increases the work burden on medical staff and can affect patient rehabilitation. Hence, information systems should introduce a postoperative rehabilitation module, encompassing dietary advice, activity guidance, medication reminders, and other personalized content. Along with the push function of the mobile terminal, the system can dynamically track patients' postoperative behavior and provide feedback that will help them standardize their behavior and augment their rehabilitation effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImprovement Requirements for the Management of Healthcare System Environment\u003c/h2\u003e \u003cp\u003eThe operating room environment directly influences the quality of perioperative management. Accordingly, respondents suggested that optimizing material management and the functionality of environmental parameter monitoring would improve the overall operational efficiency of the operating room. The absence of material management and deployment efficiency, particularly the statistical omissions and errors in the usage of high-value consumables, severely affects the quality of operating room management [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, information systems should automatically track the surgical materials and record their use and location. Concurrently, systems could utilize historical surgical data to predict material demand, thereby assisting in optimizing resource allocation and reducing waste and omission. Manually recording environmental parameters (such as temperature, humidity, and air quality) is inefficient, potentially leading to inaccurate recordings or delays. Hence, systems should introduce automatic monitoring and early warning modules to collect environmental data in real time and provide prompt alerts of abnormal conditions. Information systems should also refine ecological settings (e.g., specific humidity levels or air filtration requirements) according to the procedure type to improve the reliability of aseptic procedures and mitigate postoperative infection risk.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study conducted qualitative interviews and revealed the needs and optimization directions for an IMS in surgical intervention based on the perioperative patient-focused model. The study findings indicated that system optimization could aid in better meeting the needs of perioperative management by concentrating on the four key areas: patient safety, data monitoring, psychological support, and resource management. These research results suggest that the adaptability and practicability of the current information system can be improved by incorporating certain functionalities, including intelligent risk assessment, evaluation of the patient's psychological state, monitoring of intraoperative physiological response, postoperative behavioral support, and follow-up. Finally, future research should combine multi-center data to validate and optimize information systems in different hospitals and for various surgical types, contributing to the further improvement of IMS and providing more scientific and precise decision support for operating room management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the study participants and declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYingying Jiang, Yahui Gao and Qin Zhang have all made substantial contributions to conception and design of the study, acquisition of data, analyses and interpretation of data.Yingying Jiang wrote the initial draft of the manuscript, and Yahui Gao and Zhanao Liu were involved in revising it critically for important intellectual content. All authors have given fnal approval of the version to be published, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the 2024 Hospital Management Innovation Research Project of Jiangsu Provincial Hospital Association (JSYGY-3-2024-536) and the 2024 Suzhou Municipal Hospital Cohort Project and Evidence-Based Nursing Practice Project (SZFCXKHL202405).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is included within the article and its additional file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and content to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol received ethical approval from the Ethics Committee of Suzhou Municipal Hospital (Approval No. K-2024-176-K01). This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLu H, Guojiao T, Min X et al. Information management practice of operatingroom instruments based on whole process traceability.JNS.2023;38(24):52\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaoyao X, Yi L, Xiaoyun X et al. Study on information needs and self-management of cardiac rehabilitation in patients undergoing percutaneous coronary intervention.CNJ.2023;58(04):398\u0026ndash;405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsirintani M. Web Quality Assurance of Information in Healthcare. 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Information technology based closedloop management in operating room consumables management.JNS.2022;37(22):54\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Perioperative patients, Patient-focused model, Interventional surgery, Information management, Qualitative research","lastPublishedDoi":"10.21203/rs.3.rs-6289391/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6289391/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: In recent years, information management systems (IMS) have been increasingly adopted by healthcare institutions to standardize workflows, reduce human error, and improve decision-making efficiency. Although IMS support documentation automation, equipment tracking, and vital sign monitoring in interventional surgery, existing systems emphasize operational efficiency over holistic patient-centered care. The perioperative patient-focused model provides a theoretical framework encompassing patient safety, physiological responses, behavioral responses, and healthcare system environment, along with highlighting the need for solutions for patient-centered care. However, only a few studies have explored whether IMS can be adapted to these dimensions in interventional surgery, particularly in resource-limited settings. Therefore, this study aims to analyze the information management requirements of interventional surgery from the perspective of the perioperative patient-focused model, serving as a reference for constructing a scientific and standardized management system.\u003c/p\u003e\n\u003cp\u003eMethods: Healthcare professionals in interventional surgery from a tertiary hospital in Suzhou were selected for semi-structured interviews utilizing purposive sampling. The themes from the interview data were analyzed, summarized, and extracted by applying Braun and Clarke’s thematic analysis method.\u003c/p\u003e\n\u003cp\u003eResults: The following four themes were identified: (1) optimization needs of the function of patient safety assurance, (2) improvement requirements of perioperative physiological monitoring and intervention, (3) need for the assessment and support of patient behavioral responses, and (4) improvement requirements for the management of healthcare system environment.\u003c/p\u003e\n\u003cp\u003eConclusions: The information management requirements of perioperative patients are multidimensional and diverse. Healthcare professionals should prioritize assessment and implement targeted interventions addressing patient safety, physiological responses, behavioral patterns, and healthcare system environments to enhance perioperative management and ensure patient safety and health.\u003c/p\u003e","manuscriptTitle":"Information Management Requirements of Interventional Surgery Based on the Perioperative Patient-Focused Model: A Qualitative Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 10:39:16","doi":"10.21203/rs.3.rs-6289391/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1916ab48-b373-4db6-b7fb-69995a6d3623","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T08:24:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 10:39:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6289391","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6289391","identity":"rs-6289391","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}