{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T13:50:38Z","timestamp":1787925038418,"version":"build-2784847793"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2021A1515220186"],"award-info":[{"award-number":["2021A1515220186"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate evaluation of clinical departmental performance is essential for public hospital management. However, existing approaches primarily rely on static, retrospective annual assessments and lack interpretability, limiting their ability to support early intervention and informed decision-making. To address these gaps, the study aimed to present a dynamic framework for predicting annual departmental performance score based on real-world hospital data using explainable machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Performance data between January 2023 to December 2024 was collected from the Hospital Information System (HIS). Six machine learning models, namely Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting, XGBoost, CatBoost, were trained to predict annual performance scores across three cumulative time windows (January\u2013March, January\u2013June, January\u2013September) with traning set of 2023 and testing set of 2024. Model Evaluation was using metrics such as R\n                      <jats:sup>2<\/jats:sup>\n                      , Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). To comprehensively enhance interpretability of dynamic model, SHapley Additive exPlanations (SHAP) analysis was applied to reveal key performance indicators across different time windows in predicting annual departmental performance. The web prediction System was developed using streamlit based on best-performing model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 648 records covering 24\u00a0months was obtained from 27 clinical departments. Compared with among models, LR model achieved the highest R\n                      <jats:sup>2<\/jats:sup>\n                      values (0.8964, 0.9314, 0.9640) and the lowest RMSE (0.0257, 0.0209, 0.0151), MAE (0.0190, 0.0162, 0.0116), and MSE (0.0007, 0.0004, 0.0002) across all stages. Through SHAP analysis, the top 5 contributing performance indicator were consistent across all stages, including proportion of medical service revenue in total medical revenue, proportion of outpatient revenue from medical insurance fund, proportion of consumables in total medical revenue, proportion of inpatient revenue from medical insurance fund, and average inpatient expense per admission. For clinical practice, the web tool, ClinDeptPredictor, was developed based on LR model, accesible at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/clindeptpredictor-tool.streamlit.app\/\" ext-link-type=\"uri\">https:\/\/clindeptpredictor-tool.streamlit.app\/<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The study presented a novel dynamic framework for clinical departmental performance prediction in tertiary public hospital using explainable machine learning. The framework supports progressive performance monitoring across multiple time windows and provides timely insights to inform managerial decisions. In addition, the development of the web tool helps facilitate practical application, offering methodological support and practical reference for performance management in public healthcare settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s13755-025-00394-y","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:34:09Z","timestamp":1764689649000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Construction of performance score dynamic prediction system for clinical departments using explainable machine learning"],"prefix":"10.1007","volume":"14","author":[{"given":"Huashu","family":"Wen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haibo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Birong","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaying","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"issue":"6","key":"394_CR1","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1108\/14777260810916597","volume":"22","author":"PA Rivers","year":"2008","unstructured":"Rivers PA, Glover SH. Health care competition, strategic mission, and patient satisfaction: research model and propositions. J Health Organ Manag. 2008;22(6):627\u201341.","journal-title":"J Health Organ Manag"},{"issue":"9","key":"394_CR2","first-page":"1889","volume":"52","author":"A Homauni","year":"2023","unstructured":"Homauni A, Markazi-Moghaddam N, Mosadeghkhah A, Noori M, Abbasiyan K, Jame SZB. Budgeting in healthcare systems and organizations: a systematic review. Iran J Public Health. 2023;52(9):1889.","journal-title":"Iran J Public Health"},{"key":"394_CR3","doi-asserted-by":"publisher","DOI":"10.62754\/joe.v3i8.5341","author":"MSM Aldhumayri","year":"2024","unstructured":"Aldhumayri MSM, Aldaghmani YF, Alruwaili MMM, Alalyan SFS, Alrwailiy KMM, Alrwaili AEN, et al. Critical review of innovations in healthcare delivery: addressing efficiency, patient-centered care, and global health. J Ecohumanism. 2024. https:\/\/doi.org\/10.62754\/joe.v3i8.5341.","journal-title":"J. Ecohumanism"},{"issue":"3","key":"394_CR4","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2018-022155","volume":"9","author":"S Ahmed","year":"2019","unstructured":"Ahmed S, Hasan MZ, MacLennan M, Dorin F, Ahmed MW, Hasan MM, et al. Measuring the efficiency of health systems in Asia: a data envelopment analysis. BMJ Open. 2019;9(3):e022155.","journal-title":"BMJ Open"},{"issue":"2","key":"394_CR5","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s40258-022-00785-2","volume":"21","author":"R Mbau","year":"2023","unstructured":"Mbau R, Musiega A, Nyawira L, Tsofa B, Mulwa A, Molyneux S, et al. Analysing the efficiency of health systems: a systematic review of the literature. Appl Health Econ Health Policy. 2023;21(2):205\u201324.","journal-title":"Appl Health Econ Health Policy"},{"key":"394_CR6","doi-asserted-by":"crossref","unstructured":"Garrick R, Sullivan J, Doran M, Keenan J. 2019 The Role of the Hospital in the Healthcare System. The Modern Hospital: Patients Centered, Disease Based, Research Oriented, Technology Driven.47\u201360.","DOI":"10.1007\/978-3-030-01394-3_6"},{"key":"394_CR7","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.830102","volume":"10","author":"A Imani","year":"2022","unstructured":"Imani A, Alibabayee R, Golestani M, Dalal K. Key indicators affecting hospital efficiency: a systematic review. Front Public Health. 2022;10:830102.","journal-title":"Front Public Health"},{"issue":"1","key":"394_CR8","doi-asserted-by":"publisher","DOI":"10.4103\/jehp.jehp_393_18","volume":"8","author":"S Mahdiyan","year":"2019","unstructured":"Mahdiyan S, Dehghani A, Tafti AD, Pakdaman M, Askari R. Hospitals\u2019 efficiency in Iran: a systematic review and meta-analysis. J Educ Health Promot. 2019;8(1):126.","journal-title":"J Educ Health Promot"},{"issue":"1","key":"394_CR9","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1186\/s12913-024-10940-1","volume":"24","author":"SA Hadian","year":"2024","unstructured":"Hadian SA, Rezayatmand R, Shaarbafchizadeh N, Ketabi S, Pourghaderi AR. Hospital performance evaluation indicators: a scoping review. BMC Health Serv Res. 2024;24(1):561.","journal-title":"BMC Health Serv Res"},{"issue":"4","key":"394_CR10","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s10916-020-01546-1","volume":"44","author":"M Pestana","year":"2020","unstructured":"Pestana M, Pereira R, Moro S. Improving health care management in hospitals through a productivity dashboard. J Med Syst. 2020;44(4):87.","journal-title":"J Med Syst"},{"issue":"7","key":"394_CR11","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.3390\/healthcare10071267","volume":"10","author":"THP Sanjuluca","year":"2022","unstructured":"Sanjuluca THP, de Almeida AA, Cruz-Correia RJ. Assessing the use of hospital information systems (HIS) to support decision-making: A Cross-Sectional Study in Public Hospitals in the Hu\u00edla Health Region of Southern Angola. Healthc. 2022;10(7):1267.","journal-title":"Healthc"},{"issue":"3","key":"394_CR12","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.30574\/wjarr.2024.22.3.1774","volume":"22","author":"N Nwosu","year":"2024","unstructured":"Nwosu N. Reducing operational costs in healthcare through advanced BI tools and data integration. World J Adv Res and Rev. 2024;22(3):1144\u201356.","journal-title":"World J. Adv Res and Rev"},{"key":"394_CR13","doi-asserted-by":"crossref","unstructured":"Garg A. 2023 Monitoring tools for setting up the hospital project: initial planning, building and equipment: Springer New york.","DOI":"10.1007\/978-981-99-6203-7"},{"issue":"12","key":"394_CR14","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1136\/bmjqs-2018-007784","volume":"27","author":"AMJ Weggelaar-Jansen","year":"2018","unstructured":"Weggelaar-Jansen AMJ, Broekharst DS, De Bruijne M. Developing a hospital-wide quality and safety dashboard: a qualitative research study. BMJ Qual Saf. 2018;27(12):1000\u20137.","journal-title":"BMJ Qual Saf"},{"key":"394_CR15","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-02037-8","author":"R Rabiei","year":"2022","unstructured":"Rabiei R, Almasi S. Requirements and challenges of hospital dashboards: a systematic literature review. BMC Med Inform Decis Mak. 2022. https:\/\/doi.org\/10.1186\/s12911-022-02037-8.","journal-title":"BMC Med Inform Decis Mak"},{"key":"394_CR16","doi-asserted-by":"publisher","DOI":"10.1002\/hpm.3452","author":"J-B Gartner","year":"2022","unstructured":"Gartner J-B, Lemaire C. Dimensions of performance and related key performance indicators addressed in healthcare organisations: a literature review. Int J Health Plann Manage. 2022. https:\/\/doi.org\/10.1002\/hpm.3452.","journal-title":"Int J Health Plann Manage"},{"key":"394_CR17","doi-asserted-by":"publisher","DOI":"10.38124\/ijisrt\/IJISRT24MAY1147","author":"J Kobi","year":"2024","unstructured":"Kobi J. Developing Dashboard Analytics and Visualization Tools for Effective Performance Management and Continuous Process Improvement. Int J Innovative Sci and Res Technol. 2024. https:\/\/doi.org\/10.38124\/ijisrt\/IJISRT24MAY1147.","journal-title":"Int J. Innovative Sci and Res Technol"},{"key":"394_CR18","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.79400","author":"AM Abdelrazig Merghani","year":"2025","unstructured":"Abdelrazig Merghani AM, Ahmed Esmail AK, Mubarak Osman AME, Abdelfrag Mohamed NA, Mohamed Ali Shentour SM, Abdelrazig Merghani SM. The role of machine learning in management of operating room: a systematic review. Cureus. 2025. https:\/\/doi.org\/10.7759\/cureus.79400.","journal-title":"Cureus"},{"key":"394_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/ICDS62089.2024.10756336","author":"K Bouramtane","year":"2024","unstructured":"Bouramtane K, Kharraja S, Riffi J, Beqqali OE, Boujraf S. Improving hospital preparedness for future pandemics with machine learning and queue management solutions. Sixth Int Conf on Intell Computing in Data Sci (ICDS). 2024. https:\/\/doi.org\/10.1109\/ICDS62089.2024.10756336.","journal-title":"Sixth Int Conf on Intell Computing in Data Sci (ICDS)"},{"key":"394_CR20","doi-asserted-by":"crossref","unstructured":"Arora K, P P, Yadav H, Kavitha J, K., Mishra BR, Kumar KS. Big data and machine learning for healthcare resource allocation and optimization. South Eastern European Journal of Public Health. 2025.","DOI":"10.70135\/seejph.vi.3821"},{"key":"394_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.orhc.2021.100289","volume":"28","author":"S McRae","year":"2021","unstructured":"McRae S. Long-term forecasting of regional demand for hospital services. Oper Res Health Care. 2021;28:100289.","journal-title":"Oper Res Health Care"},{"key":"394_CR22","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1111\/j.2517-6161.1958.tb00292.x","volume":"20","author":"DR Cox","year":"1958","unstructured":"Cox DR. The regression analysis of binary sequences. J R Stat Soc Series B Stat Methodol. 1958;20:215\u201332.","journal-title":"J R Stat Soc Series B Stat Methodol"},{"issue":"1","key":"394_CR23","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1023\/A:1022643204877","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR. Induction of decision trees. Mach Learn. 1986;1(1):81\u2013106.","journal-title":"Mach Learn"},{"issue":"1","key":"394_CR24","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332.","journal-title":"Mach Learn"},{"key":"394_CR25","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451","author":"JH Friedman","year":"2001","unstructured":"Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001. https:\/\/doi.org\/10.1214\/aos\/1013203451.","journal-title":"Ann Stat"},{"key":"394_CR26","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C, 2016 Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.","DOI":"10.1145\/2939672.2939785"},{"key":"394_CR27","unstructured":"Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. 2018 CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems.31."},{"key":"394_CR28","unstructured":"Commission NH. 2024 Notice of the general Office of the National Health Commission on printing and distributing the performance appraisal operation manual for National Tertiary Public Hospitals."},{"key":"394_CR29","unstructured":"Lundberg SM, Lee S-I, editors. (2017) A Unified Approach to Interpreting Model Predictions. Neural Information Processing Systems. 30"},{"key":"394_CR30","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion GG, Chen H, DeGrave AJ, Prutkin JM, Nair BG, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2:56\u201367.","journal-title":"Nat Mach Intell"},{"key":"394_CR31","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","volume":"41","author":"E \u0160trumbelj","year":"2014","unstructured":"\u0160trumbelj E, Kononenko I. Explaining prediction models and individual predictions with feature contributions. Knowl Inf Syst. 2014;41:647\u201365.","journal-title":"Knowl Inf Syst"},{"key":"394_CR32","first-page":"507","volume":"498","author":"M Khorasani","year":"2022","unstructured":"Khorasani M, Abdou M, Hern\u00e1ndez FJ. Web Application Development with Streamlit. Software Development. 2022;498:507.","journal-title":"Software Development."},{"key":"394_CR33","unstructured":"Moscato R. (2024) Web App Development Made Simple with Streamlit A web developer's guide to effortless web app development, deployment, and scalability. Packt Publishing England"},{"key":"394_CR34","unstructured":"Richards T, Treuille A. Streamlit for Data Science: Packt Publishing England."},{"issue":"7","key":"394_CR35","first-page":"4305","volume":"4","author":"TT Moharekar","year":"2022","unstructured":"Moharekar TT, Pol UR, Ombase R, Moharekar TJ. Detection and classification of plant leaf diseases using convolution neural networks and streamlit. Int Res J Mod in Eng Technol and Sci. 2022;4(7):4305\u20139.","journal-title":"Int Res J. Mod in Eng Technol and Sci"},{"issue":"6","key":"394_CR36","doi-asserted-by":"publisher","DOI":"10.1002\/ctm2.842","volume":"12","author":"B Kui","year":"2022","unstructured":"Kui B, Pint\u00e9r J, Molontay R, Nagy M, Farkas N, Gede N, et al. EASY-APP: an artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis. Clin Transl Med. 2022;12(6):e842.","journal-title":"Clin Transl Med"},{"issue":"1","key":"394_CR37","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1186\/s12874-023-01955-z","volume":"23","author":"C-M Zhou","year":"2023","unstructured":"Zhou C-M, Wang Y, Xue Q, Yang J-J, Zhu Y. Predicting early postoperative PONV using multiple machine-learning-and deep-learning-algorithms. BMC Med Res Methodol. 2023;23(1):133.","journal-title":"BMC Med Res Methodol"},{"issue":"2","key":"394_CR38","doi-asserted-by":"publisher","first-page":"2244","DOI":"10.52783\/jes.1991","volume":"20","author":"R Patil","year":"2024","unstructured":"Patil R, Ahire P, Bamane K, Patankar A, Patil PD, Badoniya S, et al. Real-time traffic sign detection and recognition system using computer vision and machine learning. J Electr Syst. 2024;20(2):2244\u201354.","journal-title":"J. Electr Syst"},{"key":"394_CR39","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.868015","volume":"13","author":"C Lee","year":"2022","unstructured":"Lee C, Lin J, Prokop A, Gopalakrishnan V, Hanna RN, Papa E, et al. Stargazer: a hybrid intelligence platform for drug target prioritization and digital drug repositioning using streamlit. Front Genet. 2022;13:868015.","journal-title":"Front Genet"},{"issue":"1","key":"394_CR40","doi-asserted-by":"publisher","DOI":"10.4103\/jehp.jehp_2102_23","volume":"14","author":"A Talebpour","year":"2025","unstructured":"Talebpour A, Sadeghi-Bazargani H, Janati A, Pashazadeh F, Gholizadeh M. Crucial key performance indicators for hospital evaluation: a scoping review. J Educ Health Promot. 2025;14(1):195.","journal-title":"J Educ Health Promot"},{"issue":"4","key":"394_CR41","first-page":"199","volume":"3","author":"H Rahimi","year":"2014","unstructured":"Rahimi H, Khammar-nia M, Kavosi Z, Eslahi M. Indicators of hospital performance evaluation: a systematic review. International Journal of Hospital Research. 2014;3(4):199\u2013208.","journal-title":"International Journal of Hospital Research"},{"issue":"1","key":"394_CR42","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s12962-018-0166-z","volume":"16","author":"K Pourmohammadi","year":"2018","unstructured":"Pourmohammadi K, Hatam N, Shojaei P, Bastani P. A comprehensive map of the evidence on the performance evaluation indicators of public hospitals: a scoping study and best fit framework synthesis. Cost Eff Resour Alloc. 2018;16(1):64.","journal-title":"Cost Eff Resour Alloc"},{"issue":"3","key":"394_CR43","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1093\/intqhc\/mzn008","volume":"20","author":"O Groene","year":"2008","unstructured":"Groene O, Skau JK, Fr\u00f8lich A. An international review of projects on hospital performance assessment. Int J Qual Health Care. 2008;20(3):162\u201371.","journal-title":"Int J Qual Health Care"},{"issue":"10","key":"394_CR44","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s10916-014-0127-9","volume":"38","author":"B Hadji","year":"2014","unstructured":"Hadji B, Meyer R, Melikeche S, Escalon S, Degoulet P. Assessing the relationships between hospital resources and activities: a systematic review. J Med Syst. 2014;38(10):127.","journal-title":"J Med Syst"},{"issue":"1","key":"394_CR45","doi-asserted-by":"publisher","first-page":"63","DOI":"10.4103\/jehp.jehp_563_19","volume":"9","author":"V Rasi","year":"2020","unstructured":"Rasi V, Delgoshaee B, Maleki M. Identification of common indicators of hospital performance evaluation models: a scoping review. J Educ Health Promot. 2020;9(1):63.","journal-title":"J Educ Health Promot"},{"issue":"7","key":"394_CR46","first-page":"855","volume":"45","author":"M Bahadori","year":"2016","unstructured":"Bahadori M, Izadi AR, Ghardashi F, Ravangard R, Hosseini SM. The evaluation of hospital performance in Iran: a systematic review article. Iran J Public Health. 2016;45(7):855.","journal-title":"Iran J Public Health"},{"issue":"9","key":"394_CR47","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.124.011608","volume":"18","author":"J Pollack","year":"2025","unstructured":"Pollack J, Yang W, Arnaoutakis GJ, Kallan MJ, Kimmel SE. Dynamic updating strategies to assess hospital performance of surgical aortic valve replacement. Circ Cardiovasc Qual Outcomes. 2025;18(9):e011608.","journal-title":"Circ Cardiovasc Qual Outcomes"},{"issue":"1","key":"394_CR48","first-page":"125","volume":"13","author":"RA Lewandowski","year":"2014","unstructured":"Lewandowski RA. Cost control of medical care in public hospitals\u2013a comparative analysis. Int J Contemp Manag. 2014;13(1):125\u201336.","journal-title":"Int J Contemp Manag"},{"issue":"1","key":"394_CR49","first-page":"53","volume":"15","author":"J Krupi\u010dka","year":"2020","unstructured":"Krupi\u010dka J. The management accounting practices in healthcare: the case of Czech Republic hospitals. Eur Financ and Account J. 2020;15(1):53\u201366.","journal-title":"Eur Financ and Account J."},{"issue":"3","key":"394_CR50","first-page":"332","volume":"3","author":"HM Moin-Ul-Aziz","year":"2025","unstructured":"Moin-Ul-Aziz HM, Azeem SF, Hanif S, Khan KH. Cost Control Optimization Strategies in Hospital Financial Management: An Empirical Study Based on Chinese Hospitals. The Crit Rev of Soc Sci Stud. 2025;3(3):332\u201350.","journal-title":"The Crit Rev of Soc Sci Stud"},{"key":"394_CR51","doi-asserted-by":"publisher","first-page":"50","DOI":"10.51847\/ZkaN9ofevu","volume":"10","author":"CF Lascu","year":"2023","unstructured":"Lascu CF, Cheregi CD, Davidescu L, Manole F. A Review of Factors, Drivers, and Barriers to Improving Hospital Cost Management. Entomol and Appl Sci Lett. 2023;10:50\u20135.","journal-title":"Entomol and Appl Sci Lett"},{"key":"394_CR52","doi-asserted-by":"crossref","unstructured":"Almehwari SA, Almalki IS, Abumilha BA, Altharwi BH. Improving hospital efficiency and cost management: a systematic review and Meta-analysis. Cureus. 2024;16(10).","DOI":"10.7759\/cureus.71721"},{"issue":"5","key":"394_CR53","doi-asserted-by":"publisher","first-page":"4159","DOI":"10.24294\/jipd.v8i5.4159","volume":"8","author":"S Ahmad","year":"2024","unstructured":"Ahmad S, Khan I. Effectiveness and impact of management accounting in hospitals: evaluation of the effectiveness of cost accounting and revenue budgeting. J Infrastruct, Policy and Dev. 2024;8(5):4159.","journal-title":"J. Infrastruct, Policy and Dev"},{"issue":"1","key":"394_CR54","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3233\/THC-240110","volume":"33","author":"A-L Lin","year":"2025","unstructured":"Lin A-L, Hou J-H. Diagnosis-related groups payment reform and hospital cost control. Technol Health Care. 2025;33(1):17\u201324.","journal-title":"Technol Health Care"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-025-00394-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-025-00394-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-025-00394-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,7]],"date-time":"2025-12-07T04:14:13Z","timestamp":1765080853000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-025-00394-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["394"],"URL":"https:\/\/doi.org\/10.1007\/s13755-025-00394-y","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,2]]},"assertion":[{"value":"5 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors approved the final manuscript and the submission to this journal. \u2022 Data availability. The data generated and analyzed during the current study are not publicly available but can be obtained from the corresponding author upon reasonable request.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"8"}}