Isfahan University of Medical Sciences Health Information Management 1735-7853 23 1 2026 03 21 Reforming Iran’s Trauma Registry: Policy Strategies to Strengthen Data Systems and Reduce Injury Burden Reforming Iran’s Trauma Registry: Policy Strategies to Strengthen Data Systems and Reduce Injury Burden 33172 10.48305/him.2026.45915.1362 FA Mehdi Nasr Isfahani 1. گروه طب اورژانس، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران 0000-0002-0236-6546 Maryam Jahanbakhsh 3. گروه مدیریت و فناوری اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران 0000-0003-0876-5422 Neda Al-Sadat Fatemi 2. گروه سلامت در بلایا و فوریت‌ها، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران 0000-0002-7972-0793 Shahriar Zehtabchi 4. گروه طب اورژانس، دانشگاه علوم بهداشتی SUNY Downstate، بروکلین، نیویورک، ایالات متحده آمریکا Mohammad Reza Zafarghandi 5. گروه جراحی عروق، بیمارستان سینا، دانشگاه علوم پزشکی تهران، تهران، ایران. Journal Article 2025 11 19 The National Trauma Registry in Iran, as one of the fundamental pillars of the injury management system, faces major challenges in data quality. To address these challenges, six policy options are proposed, each targeting different aspects of improving the trauma registration system. The first option is the mandatory establishment of trauma registration centers in tertiary hospitals, integrated into the accreditation system with dedicated funding. This solution strengthens the long-term sustainability of the registration system by establishing legal foundations and ensuring the allocation of financial resources. However, its implementation may face resistance from hospital managers due to increased administrative burden and structural challenges in budget allocation. The second option, performing periodic data audits, is a short-term and low-cost solution that would identify systematic errors and improve data quality. The success of this option requires training a specialized workforce and managing resistance from centers to external inspections. The third option, integrating the National Trauma Registry (NTRI) with hospital systems, would improve the efficiency of the registry by reducing human errors and increasing data accuracy. However, it would require significant initial investment and extensive staff training. The fourth option, developing national standards based on international TR-DGU standards, would enable comparability of Iranian data with advanced global systems. This would require careful localization and retraining of staff due to structural differences between the Iranian and German health systems. The National Trauma Registry in Iran, as one of the fundamental pillars of the injury management system, faces major challenges in data quality. To address these challenges, six policy options are proposed, each targeting different aspects of improving the trauma registration system. The first option is the mandatory establishment of trauma registration centers in tertiary hospitals, integrated into the accreditation system with dedicated funding. This solution strengthens the long-term sustainability of the registration system by establishing legal foundations and ensuring the allocation of financial resources. However, its implementation may face resistance from hospital managers due to increased administrative burden and structural challenges in budget allocation. The second option, performing periodic data audits, is a short-term and low-cost solution that would identify systematic errors and improve data quality. The success of this option requires training a specialized workforce and managing resistance from centers to external inspections. The third option, integrating the National Trauma Registry (NTRI) with hospital systems, would improve the efficiency of the registry by reducing human errors and increasing data accuracy. However, it would require significant initial investment and extensive staff training. The fourth option, developing national standards based on international TR-DGU standards, would enable comparability of Iranian data with advanced global systems. This would require careful localization and retraining of staff due to structural differences between the Iranian and German health systems.

Isfahan University of Medical Sciences Health Information Management 1735-7853 23 1 2026 03 21 Operational Strategies for Developing a Deep Learning and MRI Radiomics Model to Predict Treatment Response and Survival Outcomes in Rectal Cancer Operational Strategies for Developing a Deep Learning and MRI Radiomics Model to Predict Treatment Response and Survival Outcomes in Rectal Cancer 33456 10.48305/him.2026.46726.1428 FA نامه Name Isfahan University of Medical Sciences 0000-0002-1904-0999 Hossein Taheri گروه فیزیک پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران MohammadBagher Tavakoli مرکزتحقیقات فیزیولوژی کاربردی، پژوهشکده قلب و عروق، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران Simin Hemati گروه رادیوآنکولوژی، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران Hossein Salehi گروه علوم تشریح و بیولوژی تولیدمثل، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران Hamed Taheri گروه علوم تشریح و بیولوژی تولیدمثل، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران Journal Article 2026 07 28 Rectal cancer is one of the most important diseases of the distal colon, challenging for healthcare professionals and requiring accurate and timely treatment decisions at the early stages of diagnosis. The current treatment approach, which often includes preoperative radiotherapy and chemotherapy, is associated with different treatment responses in patients due to the diversity and biological heterogeneity of tumors, and exposes some of them to severe treatment complications or disease recurrence without receiving adequate results. This policy brief was developed based on the findings of a radiogenomic modeling study that examined the potential of artificial intelligence models in predicting treatment response and survival outcome in patients with the participation of 200 patients. The results of the study showed that the integration of histological and structural features from pretreatment MRI images with pathology data and genomic markers such as KRAS and BRAF mutation status provides a high accuracy of about 85% in predicting disease-free survival and overall survival of patients. Deep learning-based models also provided more accurate prediction of treatment response. However, the implementation of these achievements in the clinical field required the development of operational policy options, which, according to the research findings and expert opinions, were proposed as follows: "Improving radiogenomic deep learning models in clinical decision-making systems of specialized hospitals, developing a national protocol for validation and multicenter data collection of rectal cancer, and developing new clinical guidelines for screening and predicting treatment response using combined biomarkers (MRI and genomics)." Rectal cancer is one of the most important diseases of the distal colon, challenging for healthcare professionals and requiring accurate and timely treatment decisions at the early stages of diagnosis. The current treatment approach, which often includes preoperative radiotherapy and chemotherapy, is associated with different treatment responses in patients due to the diversity and biological heterogeneity of tumors, and exposes some of them to severe treatment complications or disease recurrence without receiving adequate results. This policy brief was developed based on the findings of a radiogenomic modeling study that examined the potential of artificial intelligence models in predicting treatment response and survival outcome in patients with the participation of 200 patients. The results of the study showed that the integration of histological and structural features from pretreatment MRI images with pathology data and genomic markers such as KRAS and BRAF mutation status provides a high accuracy of about 85% in predicting disease-free survival and overall survival of patients. Deep learning-based models also provided more accurate prediction of treatment response. However, the implementation of these achievements in the clinical field required the development of operational policy options, which, according to the research findings and expert opinions, were proposed as follows: "Improving radiogenomic deep learning models in clinical decision-making systems of specialized hospitals, developing a national protocol for validation and multicenter data collection of rectal cancer, and developing new clinical guidelines for screening and predicting treatment response using combined biomarkers (MRI and genomics)."

https://him.mui.ac.ir/article_33456_4012e4ca51f4f19e7d001e568a7f4394.pdf

Isfahan University of Medical Sciences Health Information Management 1735-7853 23 1 2026 03 21 Modeling the Determinants of Medical Tourist Attraction in the Iranian Health System Modeling the Determinants of Medical Tourist Attraction in the Iranian Health System 33404 10.48305/him.2026.45954.1365 FA Mohammad Dehghani Mahmoodabadi 1- Department of Health Services Management, Science &amp;amp; Research Branch, Islamic Azad University, Tehran, Iran. 0000-0003-4111-2351 Iravan Masoudi Asl Department of Healthcare Services Management, School of Health Management & Information Sciences, Iran University of Medical Sciences, Tehran, Iran. 0000-0002-0389-9571 Soad Mahfoozpour Department of Health Services Management, Science & Research Branch, Islamic Azad University, Tehran, Iran. 0000-0003-0178-679X Somayeh Hesam Department of Health Services Management, Science & Research Branch, Islamic Azad University, Tehran, Iran. 0000-0001-6501-3687 Journal Article 2025 12 02 Introduction: Medical tourism, as a subcategory of the tourism industry and a significant potential economic driver, can play a substantial role in improving the economic conditions of countries. The aim of this study is to design a model of the factors influencing the attraction of medical tourists within Iran’s healthcare system. Methods: In terms of purpose, this research is developmental and, through an inductive approach, takes on an exploratory–analytical nature in the form of a quantitative study. The research was conducted in two stages. In the first stage, the factors affecting the attraction of medical tourists in Iran’s healthcare system were identified using exploratory and confirmatory factor analysis with the LISREL software. The statistical population in this stage consisted of graduate students in tourism management. In the second stage, in order to propose a model and categorize the influencing factors, the Interpretive Structural Modeling (ISM) method and expert opinions were employed. Results: The attraction of medical tourists depends on a coordinated and integrated combination of specialized, economic, marketing, supportive, infrastructural, political, cultural, and human resource-related factors. Marketing and communication factors, supportive and developmental factors, and the provision of specialized services were identified as the most affected variables. In addition, political and social factors, economic factors, and cultural factors were recognized as the most influential variables. Conclusion: Sustainable development of the medical industry in Iran is a multidimensional and coordinated requirement that requires attention to eight components including political and social factors, marketing and communication factors, support and development factors, infrastructure and management factors, provision of specialized services, human resources and experienced medical staff, economic factors, and cultural factors. Introduction: Medical tourism, as a subcategory of the tourism industry and a significant potential economic driver, can play a substantial role in improving the economic conditions of countries. The aim of this study is to design a model of the factors influencing the attraction of medical tourists within Iran’s healthcare system. Methods: In terms of purpose, this research is developmental and, through an inductive approach, takes on an exploratory–analytical nature in the form of a quantitative study. The research was conducted in two stages. In the first stage, the factors affecting the attraction of medical tourists in Iran’s healthcare system were identified using exploratory and confirmatory factor analysis with the LISREL software. The statistical population in this stage consisted of graduate students in tourism management. In the second stage, in order to propose a model and categorize the influencing factors, the Interpretive Structural Modeling (ISM) method and expert opinions were employed. Results: The attraction of medical tourists depends on a coordinated and integrated combination of specialized, economic, marketing, supportive, infrastructural, political, cultural, and human resource-related factors. Marketing and communication factors, supportive and developmental factors, and the provision of specialized services were identified as the most affected variables. In addition, political and social factors, economic factors, and cultural factors were recognized as the most influential variables. Conclusion: Sustainable development of the medical industry in Iran is a multidimensional and coordinated requirement that requires attention to eight components including political and social factors, marketing and communication factors, support and development factors, infrastructure and management factors, provision of specialized services, human resources and experienced medical staff, economic factors, and cultural factors.

Isfahan University of Medical Sciences Health Information Management 1735-7853 23 1 2026 03 21 Analyzing and Predicting Treatment Processes: A Process Oriented Data Science Approach Analyzing and Predicting Treatment Processes: A Process Oriented Data Science Approach 33453 10.48305/him.2026.45810.1350 FA Mohammad Amin Pirian Systems and Industrial Engineering Department, Tarbiat Modares University, Tehran, Iran 0009-0006-3484-596X Toktam Khatibi Associate Professor, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran 0000-0001-5824-9798 Journal Article 2025 10 16 Background: Healthcare processes are inherently complex and demand data-driven strategies for effective health information management. This study integrates process mining with deep learning to predict subsequent activities in infectious disease emergency care based on event logs, aiming to support more efficient management. Methods: The event log comprised 1,050 patients admitted to the emergency department with infection symptoms and suspected sepsis, for whom specific treatment pathways were expected. First, patient flows were mapped using process mining tools to visualize and clarify care trajectories. Next, process data were analyzed, and the proposed model—built on deep learning with a transformer-based architecture—was employed to predict each patient’s next step. Model performance was evaluated using metrics such as accuracy. Results: Process mining identified bottlenecks, including delays in diagnostic tests and congestion in blood test units. The transformer model achieved an average accuracy of 83.5% in next-activity prediction, representing a 13% improvement over existing approaches. It also reduced mean absolute error (MAE) to 0.93 days for next-event time and 5.43 days for remaining time. Conclusion: The proposed model offers a robust tool for analyzing and improving clinical processes. This approach can reduce patient waiting times, optimize resource allocation, and enhance data-driven decision-making in healthcare. The findings contribute to advancing health information management systems and improving treatment services. Background: Healthcare processes are inherently complex and demand data-driven strategies for effective health information management. This study integrates process mining with deep learning to predict subsequent activities in infectious disease emergency care based on event logs, aiming to support more efficient management. Methods: The event log comprised 1,050 patients admitted to the emergency department with infection symptoms and suspected sepsis, for whom specific treatment pathways were expected. First, patient flows were mapped using process mining tools to visualize and clarify care trajectories. Next, process data were analyzed, and the proposed model—built on deep learning with a transformer-based architecture—was employed to predict each patient’s next step. Model performance was evaluated using metrics such as accuracy. Results: Process mining identified bottlenecks, including delays in diagnostic tests and congestion in blood test units. The transformer model achieved an average accuracy of 83.5% in next-activity prediction, representing a 13% improvement over existing approaches. It also reduced mean absolute error (MAE) to 0.93 days for next-event time and 5.43 days for remaining time. Conclusion: The proposed model offers a robust tool for analyzing and improving clinical processes. This approach can reduce patient waiting times, optimize resource allocation, and enhance data-driven decision-making in healthcare. The findings contribute to advancing health information management systems and improving treatment services.

Isfahan University of Medical Sciences Health Information Management 1735-7853 23 1 2026 03 21 Identification of Health System Resilience Indicators from a Knowledge Management Perspective Identification of Health System Resilience Indicators from a Knowledge Management Perspective 33405 10.48305/him.2026.46252.1395 FA Zohreh Rezaei Student,Information Science and Knowledge , Department of Communications and Knowledge Science, Science and Research Branch, Islamic Azad University, Tehran, Iran. 0000-0001-8731-3162 Fahimeh Babalhavaeji Department of Communications and Knowledge Science, ,Science and Research Branch, Islamic Azad University, Tehran, Iran. 0000-0002-0247-6614 Najla Hariri Department of Communications and Knowledge Science, Science and Research Branch, Islamic Azad University, Tehran, Iran 0000-0002-0247-6614 Dariush Matlabi Faculty of Humanities, Yadegar-e- Imam Khomeini Shahr-e Ray Branch, Islamic Azad University, Tehran, Iran. 0000-0002-2503-6558 Journal Article 2026 04 25 Abstract Introduction: Health system resilience is essential for maintaining critical services during crises. Knowledge management plays a pivotal role in enhancing this resilience. This study aims to identify indicators of health system resilience from a knowledge management perspective. Methods: This the study was conducted using a meta-synthesis qualitative method and a systematic search of domestic and foreign databases. From 168 studies found in the years 1390 to 1404 and 2010 to 2025, 60 studies were selected and by analyzing their content, the final indicators were identified and extracted. The Kappa coefficient was used to validate the results. Results: By analyzing the studies, 402 initial codes were classified into 66 subcategories and 13 main categories after conceptual comparison and integration of similar items, including: knowledge governance and leadership, knowledge management and creation, organizational learning and institutional memory, knowledge and resilient human resources, information and technology infrastructure, knowledge management in crisis, knowledge economic financing and resilience, supply chain and resource management, inter-sectoral and network interactions, knowledge innovation and transformation, resilience monitoring, evaluation and measurement, knowledge and resilient organizational culture, and knowledge resilience of health services. Conclusion: Health system resilience is the outcome of the coordinated interaction of multiple factors. The sustainability and quality of health services can only be ensured when knowledge is institutionalized across all levels of the health system and utilized as a strategic asset in decision-making processes. Keywords: Resilience, Health System, Knowledge Management, Indicator Abstract Introduction: Health system resilience is essential for maintaining critical services during crises. Knowledge management plays a pivotal role in enhancing this resilience. This study aims to identify indicators of health system resilience from a knowledge management perspective. Methods: This the study was conducted using a meta-synthesis qualitative method and a systematic search of domestic and foreign databases. From 168 studies found in the years 1390 to 1404 and 2010 to 2025, 60 studies were selected and by analyzing their content, the final indicators were identified and extracted. The Kappa coefficient was used to validate the results. Results: By analyzing the studies, 402 initial codes were classified into 66 subcategories and 13 main categories after conceptual comparison and integration of similar items, including: knowledge governance and leadership, knowledge management and creation, organizational learning and institutional memory, knowledge and resilient human resources, information and technology infrastructure, knowledge management in crisis, knowledge economic financing and resilience, supply chain and resource management, inter-sectoral and network interactions, knowledge innovation and transformation, resilience monitoring, evaluation and measurement, knowledge and resilient organizational culture, and knowledge resilience of health services. Conclusion: Health system resilience is the outcome of the coordinated interaction of multiple factors. The sustainability and quality of health services can only be ensured when knowledge is institutionalized across all levels of the health system and utilized as a strategic asset in decision-making processes. Keywords: Resilience, Health System, Knowledge Management, Indicator

Isfahan University of Medical Sciences Health Information Management 1735-7853 23 1 2026 03 21 Factors Influencing and Management Strategies for Clinical Information Overload: An Inquiry into the Experiences of Specialists at Isfahan University of Medical Sciences Factors Influencing and Management Strategies for Clinical Information Overload: An Inquiry into the Experiences of Specialists at Isfahan University of Medical Sciences 33454 10.48305/him.2026.46103.1384 FA Laila Shahrzadi Isfahan University of Medical Sciences 0000-0003-3738-2543 Ali Mansouri University of Isfahan 0000-0003-4047-2697 Mosa Alavi Isfahan University of Medical Sciences Ahmad Shabani Department of Knowledge and Information Science, University of Isfahan 0000-0003-0466-6240 Journal Article 2026 01 26 Introduction: Clinical information overload is a multidimensional phenomenon that adversely affects the quality of clinical decision-making and patient safety. This study aimed to explore the contributing factors and to identify management strategies for this phenomenon from the perspectives of clinical specialists at Isfahan University of Medical Sciences. Methods: This study employed a sequential exploratory mixed-methods design conducted in two phases. In the qualitative phase, semi-structured interviews were conducted with 19 clinical specialists, and the data were analyzed using inductive content analysis. In the quantitative phase, a questionnaire developed based on the qualitative findings was distributed among 100 specialists, and the data were analyzed using descriptive statistics. Results: Data analysis led to the identification of seven main categories of contributing factors, including managerial–organizational factors (ranked as the highest priority), educational, informational, technological, documentation-related, individual, and task-related factors. Among the novel findings was the identification of documentation-related and educational factors as independent determinants of clinical information overload. Overall, 107 management strategies were proposed across seven domains. Key strategies included the utilization of health information professionals, implementation of an integrated electronic health record system, and the design of user-friendly information systems. Conclusion: Effective management of clinical information overload requires a systematic and integrated approach at multiple levels. The proposed conceptual model may serve as a suitable framework for policymaking, information system design, and educational planning aimed at improving the quality of healthcare delivery. Introduction: Clinical information overload is a multidimensional phenomenon that adversely affects the quality of clinical decision-making and patient safety. This study aimed to explore the contributing factors and to identify management strategies for this phenomenon from the perspectives of clinical specialists at Isfahan University of Medical Sciences. Methods: This study employed a sequential exploratory mixed-methods design conducted in two phases. In the qualitative phase, semi-structured interviews were conducted with 19 clinical specialists, and the data were analyzed using inductive content analysis. In the quantitative phase, a questionnaire developed based on the qualitative findings was distributed among 100 specialists, and the data were analyzed using descriptive statistics. Results: Data analysis led to the identification of seven main categories of contributing factors, including managerial–organizational factors (ranked as the highest priority), educational, informational, technological, documentation-related, individual, and task-related factors. Among the novel findings was the identification of documentation-related and educational factors as independent determinants of clinical information overload. Overall, 107 management strategies were proposed across seven domains. Key strategies included the utilization of health information professionals, implementation of an integrated electronic health record system, and the design of user-friendly information systems. Conclusion: Effective management of clinical information overload requires a systematic and integrated approach at multiple levels. The proposed conceptual model may serve as a suitable framework for policymaking, information system design, and educational planning aimed at improving the quality of healthcare delivery.

Isfahan University of Medical Sciences Health Information Management 1735-7853 23 1 2026 03 21 Exploring the Mediating Role of Trust in the Relationship Between Artificial Intelligence and Communication Dynamics Among Librarians Exploring the Mediating Role of Trust in the Relationship Between Artificial Intelligence and Communication Dynamics Among Librarians 33455 10.48305/him.2026.46195.1393 FA Seifallah Andayesh Assistant Professor, Knowledge and Information Science, Faculty of Literature and Humanities, Persian Gulf University, Bushehr, Iran 0000-0002-0095-4272 Zahra Kianrad PhD graduate, knowledge and information science, University of Tehran. Tehran. Iran 0000-0002-1474-0830 Journal Article 2026 02 22 Introduction: With the expansion of artificial intelligence (AI) applications in professional environments, medical science libraries have also undergone significant transformation. The effectiveness of these technologies largely depends on the level of trust in intelligent systems as well as interpersonal trust. This study was conducted to analyze the role of trust in the relationship between artificial intelligence and communication dynamics among medical librarians. Methods: This research is applied in terms of purpose and descriptive-survey in terms of data collection. The statistical population consisted of all librarians working in libraries of medical universities in Tehran, with a sample size of 137 participants. Valid and standardized instruments were used to measure the research variables. Convergent and discriminant validity were applied to assess the measurement tools. Data were analyzed using Structural Equation Modeling (SEM) with Smart PLS software. Findings: The results indicated that artificial intelligence has a positive and significant effect on communication dynamics (path coefficient = 0.616). Artificial intelligence also has a positive effect on trust (path coefficient = 0.798). Furthermore, trust has a positive and significant effect on communication dynamics (path coefficient = 0.512). Conclusion: The findings show that trust plays a partial mediating role in the relationship between artificial intelligence and communication dynamics. In other words, artificial intelligence influences communication dynamics not only directly but also indirectly through strengthening trust. These results highlight the importance of addressing both technological and human dimensions in implementing AI in medical libraries, as the success of this technology in improving communication depends on fostering and strengthening trust among its users. Introduction: With the expansion of artificial intelligence (AI) applications in professional environments, medical science libraries have also undergone significant transformation. The effectiveness of these technologies largely depends on the level of trust in intelligent systems as well as interpersonal trust. This study was conducted to analyze the role of trust in the relationship between artificial intelligence and communication dynamics among medical librarians. Methods: This research is applied in terms of purpose and descriptive-survey in terms of data collection. The statistical population consisted of all librarians working in libraries of medical universities in Tehran, with a sample size of 137 participants. Valid and standardized instruments were used to measure the research variables. Convergent and discriminant validity were applied to assess the measurement tools. Data were analyzed using Structural Equation Modeling (SEM) with Smart PLS software. Findings: The results indicated that artificial intelligence has a positive and significant effect on communication dynamics (path coefficient = 0.616). Artificial intelligence also has a positive effect on trust (path coefficient = 0.798). Furthermore, trust has a positive and significant effect on communication dynamics (path coefficient = 0.512). Conclusion: The findings show that trust plays a partial mediating role in the relationship between artificial intelligence and communication dynamics. In other words, artificial intelligence influences communication dynamics not only directly but also indirectly through strengthening trust. These results highlight the importance of addressing both technological and human dimensions in implementing AI in medical libraries, as the success of this technology in improving communication depends on fostering and strengthening trust among its users.

Isfahan University of Medical Sciences Health Information Management 1735-7853 23 1 2026 03 21 Process Mining in Healthcare Process Simulation: A Structured Narrative Review of Trends, Barriers, and Opportunities Process Mining in Healthcare Process Simulation: A Structured Narrative Review of Trends, Barriers, and Opportunities 33460 10.48305/him.2026.46060.1380 FA Mohsen Mohammadi Esfarayen University of Technology 0000-0003-0532-1236 Journal Article 2025 12 29 As healthcare systems become more complex, advanced data-driven methods are needed to improve efficiency, patient outcomes, and resource allocation. This study examines the integration of process mining and healthcare process simulation to support data-driven decision-making.This analytical review explores the basic concepts of process mining and process simulation, their synergistic integration, healthcare applications, implementation challenges, and future research opportunities.Process mining can enhance simulation models by discovering actual care pathways, identifying bottlenecks, assessing process conformance, and extracting empirical parameters from event logs. Simulation, in turn, enables the evaluation of improvement scenarios, prediction of operational outcomes, analysis of patient flow, and optimization of resources. Together, these approaches can support evidence-based decision-making and improve healthcare service delivery.Integrating process mining and simulation provides a valuable framework for analyzing and improving healthcare processes. However, challenges related to data quality, privacy, workflow complexity, technical limitations, and organizational collaboration remain. Future research should focus on standardized frameworks, high-quality data, predictive analytics, and practical implementation studies. As healthcare systems become more complex, advanced data-driven methods are needed to improve efficiency, patient outcomes, and resource allocation. This study examines the integration of process mining and healthcare process simulation to support data-driven decision-making.This analytical review explores the basic concepts of process mining and process simulation, their synergistic integration, healthcare applications, implementation challenges, and future research opportunities.Process mining can enhance simulation models by discovering actual care pathways, identifying bottlenecks, assessing process conformance, and extracting empirical parameters from event logs. Simulation, in turn, enables the evaluation of improvement scenarios, prediction of operational outcomes, analysis of patient flow, and optimization of resources. Together, these approaches can support evidence-based decision-making and improve healthcare service delivery.Integrating process mining and simulation provides a valuable framework for analyzing and improving healthcare processes. However, challenges related to data quality, privacy, workflow complexity, technical limitations, and organizational collaboration remain. Future research should focus on standardized frameworks, high-quality data, predictive analytics, and practical implementation studies.