{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:15:50Z","timestamp":1775189750621,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Almoosa College of Health Sciences"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>This systematic review examines the cost-effectiveness, utility, and budget impact of clinical artificial intelligence (AI) interventions across diverse healthcare settings. Nineteen studies spanning oncology, cardiology, ophthalmology, and infectious diseases demonstrate that AI improves diagnostic accuracy, enhances quality-adjusted life years, and reduces costs\u2014largely by minimizing unnecessary procedures and optimizing resource use. Several interventions achieved incremental cost-effectiveness ratios well below accepted thresholds. However, many evaluations relied on static models that may overestimate benefits by not capturing the adaptive learning of AI systems over time. Additionally, indirect costs, infrastructure investments, and equity considerations were often underreported, suggesting that reported economic benefits may be overstated. Dynamic modeling indicates sustained long-term value, but further research is needed to incorporate comprehensive cost components and subgroup analyses. These findings underscore the clinical promise and economic complexity of AI in healthcare, emphasizing the need for context-specific, methodologically robust evaluations to guide future policy and practice effectively.<\/jats:p>","DOI":"10.1038\/s41746-025-01722-y","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T11:21:56Z","timestamp":1756207316000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare"],"prefix":"10.1038","volume":"8","author":[{"given":"Rabie Adel","family":"El Arab","sequence":"first","affiliation":[]},{"given":"Omayma Abdulaziz","family":"Al Moosa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"1722_CR1","doi-asserted-by":"publisher","unstructured":"Jaboob, A., Durrah, O. & Chakir, A. Artificial intelligence: an overview, 3\u201322, https:\/\/doi.org\/10.1007\/978-3-031-50300-9_1 (2024).","DOI":"10.1007\/978-3-031-50300-9_1"},{"key":"1722_CR2","first-page":"100066","volume":"8","author":"P Gupta","year":"2024","unstructured":"Gupta, P., Ding, B., Guan, C. & Ding, D. Generative AI: a systematic review using topic modelling techniques. Data Inf. Manag 8, 100066 (2024).","journal-title":"Data Inf. Manag"},{"key":"1722_CR3","doi-asserted-by":"publisher","first-page":"58","DOI":"10.3934\/publichealth.2024004","volume":"11","author":"A Rahman","year":"2024","unstructured":"Rahman, A. et al. Machine learning and deep learning-based approach in smart healthcare: recent advances, applications, challenges and opportunities. AIMS Public Health 11, 58 (2024).","journal-title":"AIMS Public Health"},{"key":"1722_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.modpat.2024.100688","volume":"38","author":"HH Rashidi","year":"2025","unstructured":"Rashidi, H. H. et al. Introduction to artificial intelligence and machine learning in pathology and medicine: generative and nongenerative artificial intelligence basics. Mod. Pathol. 38, 100688 (2025).","journal-title":"Mod. Pathol."},{"key":"1722_CR5","doi-asserted-by":"publisher","first-page":"e188","DOI":"10.7861\/fhj.2021-0095","volume":"8","author":"J Bajwa","year":"2021","unstructured":"Bajwa, J., Munir, U., Nori, A. & Williams, B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Health J. 8, e188 (2021).","journal-title":"Future Health J."},{"key":"1722_CR6","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.3390\/jpm13081214","volume":"13","author":"DG Poalelungi","year":"2023","unstructured":"Poalelungi, D. G. et al. Advancing patient care: how artificial intelligence is transforming healthcare. J. Pers. Med 13, 1214 (2023).","journal-title":"J. Pers. Med"},{"key":"1722_CR7","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.3390\/healthcare10122493","volume":"10","author":"NN Khanna","year":"2022","unstructured":"Khanna, N. N. et al. Economics of artificial intelligence in healthcare: diagnosis vs. treatment. Healthcare 10, 2493 (2022).","journal-title":"Healthcare"},{"key":"1722_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e33186","volume":"10","author":"A Hussain","year":"2024","unstructured":"Hussain, A. et al. Exploring sustainable healthcare: Innovations in health economics, social policy, and management. Heliyon 10, e33186 (2024).","journal-title":"Heliyon"},{"key":"1722_CR9","unstructured":"Szczepa\u0144ski, M. Economic impacts of artificial intelligence (AI) [Briefing]. European Parliamentary Research Service. https:\/\/www.europarl.europa.eu\/RegData\/etudes\/BRIE\/2019\/637967\/EPRS_BRI(2019)637967_EN.pdf (2019)."},{"key":"1722_CR10","doi-asserted-by":"crossref","unstructured":"Comunale, M. & Manera, A. The economic impacts and the regulation of AI: a review of the academic literature and policy actions. IMF Working Papers (IMF, 2024).","DOI":"10.5089\/9798400268588.001"},{"key":"1722_CR11","doi-asserted-by":"publisher","first-page":"1273253","DOI":"10.3389\/fpubh.2023.1273253","volume":"11","author":"M Li","year":"2023","unstructured":"Li, M., Jiang, Y., Zhang, Y. & Zhu, H. Medical image analysis using deep learning algorithms. Front. Public Health 11, 1273253 (2023).","journal-title":"Front. Public Health"},{"key":"1722_CR12","doi-asserted-by":"publisher","first-page":"2101","DOI":"10.1002\/cncr.35307","volume":"130","author":"L Kolla","year":"2024","unstructured":"Kolla, L. & Parikh, R. B. Uses and limitations of artificial intelligence for oncology. Cancer 130, 2101 (2024).","journal-title":"Cancer"},{"key":"1722_CR13","doi-asserted-by":"publisher","unstructured":"Steffen, M. The challenges for health systems and policies: growing medicalization and global risks. Governance Sustain. Fut. 335\u2013364, https:\/\/doi.org\/10.1007\/978-981-99-4771-3_17 (2023).","DOI":"10.1007\/978-981-99-4771-3_17"},{"key":"1722_CR14","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1108\/S0573-855520210000295017","volume":"295","author":"E Costa","year":"2021","unstructured":"Costa, E., Santos, R. & Barros, P. P. The financial sustainability of the portuguese health system. Contrib. Econ. Anal. 295, 209\u2013229 (2021).","journal-title":"Contrib. Econ. Anal."},{"key":"1722_CR15","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1080\/13696998.2021.2007691","volume":"24","author":"M Jakovljevic","year":"2021","unstructured":"Jakovljevic, M. et al. The Global South political economy of health financing and spending landscape \u2013 history and presence. J. Med Econ. 24, 25\u201333 (2021).","journal-title":"J. Med Econ."},{"key":"1722_CR16","first-page":"2302","volume":"19","author":"FS Tonin","year":"2021","unstructured":"Tonin, F. S., Aznar-Lou, I., Pontinha, V. M., Pontarolo, R. & Fernandez-Llimos, F. Principles of pharmacoeconomic analysis: the case of pharmacist-led interventions. Pharm. Pr. 19, 2302 (2021).","journal-title":"Pharm. Pr."},{"key":"1722_CR17","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1007\/s40273-020-00907-5","volume":"38","author":"M Franklin","year":"2020","unstructured":"Franklin, M., Lomas, J. & Richardson, G. Conducting value for money analyses for non-randomised interventional studies including service evaluations: an educational review with recommendations. Pharmacoeconomics 38, 665 (2020).","journal-title":"Pharmacoeconomics"},{"key":"1722_CR18","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.jval.2021.11.1362","volume":"25","author":"MM Voets","year":"2022","unstructured":"Voets, M. M., Veltman, J., Slump, C. H., Siesling, S. & Koffijberg, H. Systematic review of health economic evaluations focused on artificial intelligence in healthcare: the tortoise and the cheetah. Value Health 25, 340\u2013349 (2022).","journal-title":"Value Health"},{"key":"1722_CR19","doi-asserted-by":"publisher","first-page":"1220950","DOI":"10.3389\/fphar.2023.1220950","volume":"14","author":"J Vithlani","year":"2023","unstructured":"Vithlani, J., Hawksworth, C., Elvidge, J., Ayiku, L. & Dawoud, D. Economic evaluations of artificial intelligence-based healthcare interventions: a systematic literature review of best practices in their conduct and reporting. Front. Pharm. 14, 1220950 (2023).","journal-title":"Front. Pharm."},{"key":"1722_CR20","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1016\/j.gie.2020.03.3759","volume":"92","author":"Y Mori","year":"2020","unstructured":"Mori, Y. et al. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Gastrointest. Endosc. 92, 905\u2013911.e1 (2020).","journal-title":"Gastrointest. Endosc."},{"key":"1722_CR21","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.jval.2021.08.015","volume":"25","author":"N Hendrix","year":"2022","unstructured":"Hendrix, N., Veenstra, D. L., Cheng, M., Anderson, N. C. & Verguet, S. Assessing the economic value of clinical artificial intelligence: challenges and opportunities. Value Health 25, 331\u2013339 (2022).","journal-title":"Value Health"},{"key":"1722_CR22","doi-asserted-by":"crossref","unstructured":"Murtoj\u00e4rvi, M. et al. Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets. Int. J. Med. Inform. 133, 104014 (2020).","DOI":"10.1016\/j.ijmedinf.2019.104014"},{"key":"1722_CR23","doi-asserted-by":"publisher","first-page":"e240","DOI":"10.1016\/S2589-7500(20)30060-1","volume":"2","author":"Y Xie","year":"2020","unstructured":"Xie, Y. et al. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit Health 2, e240\u2013e249 (2020).","journal-title":"Lancet Digit Health"},{"key":"1722_CR24","doi-asserted-by":"crossref","unstructured":"Karabeg, M. et al. A pilot cost-analysis study comparing AI-based EyeArt\u00ae and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway. Int. J. Retina Vitreous 10, 40 (2024).","DOI":"10.1186\/s40942-024-00547-3"},{"key":"1722_CR25","doi-asserted-by":"crossref","unstructured":"Ziegelmayer, S., Graf, M., Makowski, M., Gawlitza, J. & Gassert, F. Cost-effectiveness of artificial intelligence support in computed tomography-based lung cancer screening. Cancers 14, 1729 (2022).","DOI":"10.3390\/cancers14071729"},{"key":"1722_CR26","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1080\/13696998.2019.1706543","volume":"23","author":"NR Hill","year":"2020","unstructured":"Hill, N. R. et al. Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm. J. Med. Econ. 23, 386\u2013393 (2020).","journal-title":"J. Med. Econ."},{"key":"1722_CR27","first-page":"1186","volume":"27","author":"S Kessler","year":"2021","unstructured":"Kessler, S. et al. Economic and utilization outcomes of medication management at a large Medicaid plan with disease management pharmacists using a novel artificial intelligence platform from 2018 to 2019: a retrospective observational study using regression methods. J. Manag Care Spec. Pharm. 27, 1186\u20131196 (2021).","journal-title":"J. Manag Care Spec. Pharm."},{"key":"1722_CR28","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1093\/europace\/euac016","volume":"24","author":"T Szymanski","year":"2022","unstructured":"Szymanski, T. et al. Budget impact analysis of a machine learning algorithm to predict high risk of atrial fibrillation among primary care patients. Europace 24, 1240\u20131247 (2022).","journal-title":"Europace"},{"key":"1722_CR29","doi-asserted-by":"crossref","unstructured":"Gomez Rossi, J., Rojas-Perilla, N., Krois, J. & Schwendicke, F. Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy. JAMA Netw Open 5, e220269 (2022).","DOI":"10.1001\/jamanetworkopen.2022.0269"},{"key":"1722_CR30","doi-asserted-by":"crossref","unstructured":"Salcedo, J. et al. Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: a modeling study. PLoS One 16, e0254950 (2021).","DOI":"10.1371\/journal.pone.0254950"},{"key":"1722_CR31","doi-asserted-by":"crossref","unstructured":"Nsengiyumva, N. P. et al. Triage of persons with tuberculosis symptoms using artificial intelligence-based chest radiograph interpretation: a cost-effectiveness analysis. Open Forum Infect Dis. 8, ofab567 (2021).","DOI":"10.1093\/ofid\/ofab567"},{"key":"1722_CR32","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.jval.2021.06.018","volume":"25","author":"J de Vos","year":"2022","unstructured":"de Vos, J. et al. The potential cost-effectiveness of a machine learning tool that can prevent untimely intensive care unit discharge. Value Health 25, 359\u2013367 (2022).","journal-title":"Value Health"},{"key":"1722_CR33","doi-asserted-by":"publisher","first-page":"e436","DOI":"10.1016\/S2589-7500(22)00042-5","volume":"4","author":"M Areia","year":"2022","unstructured":"Areia, M. et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digit Health 4, e436\u2013e444 (2022).","journal-title":"Lancet Digit Health"},{"key":"1722_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-022-09613-1","volume":"22","author":"S Mital","year":"2022","unstructured":"Mital, S. & Nguyen, H. V. Cost-effectiveness of using artificial intelligence versus polygenic risk score to guide breast cancer screening. BMC Cancer 22, 1\u201316 (2022).","journal-title":"BMC Cancer"},{"key":"1722_CR35","doi-asserted-by":"crossref","unstructured":"Xiao, X., Xue, L., Ye, L., Li, H. & He, Y. Health care cost and benefits of artificial intelligence-assisted population-based glaucoma screening for the elderly in remote areas of China: a cost-offset analysis. BMC Public Health 21, 1065 (2021).","DOI":"10.1186\/s12889-021-11097-w"},{"key":"1722_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12913-022-07655-6","volume":"22","author":"XM Huang","year":"2022","unstructured":"Huang, X. M. et al. Cost-effectiveness of artificial intelligence screening for diabetic retinopathy in rural China. BMC Health Serv. Res 22, 1\u201312 (2022).","journal-title":"BMC Health Serv. Res"},{"key":"1722_CR37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13244-021-01077-4","volume":"12","author":"KG van Leeuwen","year":"2021","unstructured":"van Leeuwen, K. G. et al. Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment. Insights Imaging 12, 1\u20139 (2021).","journal-title":"Insights Imaging"},{"key":"1722_CR38","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1177\/0022034520972335","volume":"100","author":"F Schwendicke","year":"2021","unstructured":"Schwendicke, F. et al. Cost-effectiveness of artificial intelligence for proximal caries detection. J. Dent. Res. 100, 369\u2013376 (2021).","journal-title":"J. Dent. Res."},{"key":"1722_CR39","doi-asserted-by":"publisher","first-page":"555","DOI":"10.3390\/math13040555","volume":"13","author":"PA Kowalski","year":"2025","unstructured":"Kowalski, P. A., Scherer, R., Sol\u00eds-Mart\u00edn, D., Gal\u00e1n-P\u00e1ez, J. & Borrego-D\u00edaz, J. A model for learning-curve estimation in efficient neural architecture search and its application in predictive health maintenance. Mathematics 13, 555 (2025).","journal-title":"Mathematics"},{"key":"1722_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Y. et al. Economic evaluation and costs of remote patient monitoring for cardiovascular disease in the United States: a systematic review. Int. J. Technol. Assess. Health Care 39, e25 (2023).","DOI":"10.1017\/S0266462323000156"},{"key":"1722_CR41","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1038\/d41586-024-00478-x","volume":"626","author":"K Crawford","year":"2024","unstructured":"Crawford, K. Generative AI\u2019s environmental costs are soaring \u2014 and mostly secret. Nature 626, 693 (2024).","journal-title":"Nature"},{"key":"1722_CR42","doi-asserted-by":"publisher","first-page":"12042","DOI":"10.3390\/app142412042","volume":"14","author":"P Afzali","year":"2024","unstructured":"Afzali, P., Hosseini, S. A. & Peyghami, S. A comprehensive review on uncertainty and risk modeling techniques and their applications in power systems. Appl. Sci. 14, 12042 (2024).","journal-title":"Appl. Sci."},{"key":"1722_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.hlpt.2022.100702","volume":"12","author":"R Agarwal","year":"2023","unstructured":"Agarwal, R. et al. Addressing algorithmic bias and the perpetuation of health inequities: an AI bias aware framework. Health Policy Technol. 12, 100702 (2023).","journal-title":"Health Policy Technol."},{"key":"1722_CR44","doi-asserted-by":"publisher","first-page":"e0000022","DOI":"10.1371\/journal.pdig.0000022","volume":"1","author":"LA Celi","year":"2022","unstructured":"Celi, L. A. et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities\u2014a global review. PLOS Digital Health 1, e0000022 (2022).","journal-title":"PLOS Digital Health"},{"key":"1722_CR45","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1016\/j.jval.2024.05.006","volume":"27","author":"J Elvidge","year":"2024","unstructured":"Elvidge, J. et al. Consolidated health economic evaluation reporting standards for interventions that use artificial intelligence (CHEERS-AI). Value Health 27, 1196\u20131205 (2024).","journal-title":"Value Health"},{"key":"1722_CR46","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1016\/j.jval.2023.04.004","volume":"26","author":"DD Kim","year":"2023","unstructured":"Kim, D. D. et al. Developing criteria for health economic quality evaluation tool. Value Health 26, 1225\u20131234 (2023).","journal-title":"Value Health"},{"key":"1722_CR47","doi-asserted-by":"publisher","unstructured":"Adel El Arab, R. et al. Bridging the gap: from AI success in clinical trials to real-world healthcare implementation-a narrative review. https:\/\/doi.org\/10.3390\/healthcare13070701 (2025).","DOI":"10.3390\/healthcare13070701"},{"key":"1722_CR48","doi-asserted-by":"publisher","unstructured":"Cousineau, C., Dara, R. & Chowdhury, A. Trustworthy AI: AI developers\u2019 lens to implementation challenges and opportunities. Data Inf. Manag. 100082, https:\/\/doi.org\/10.1016\/J.DIM.2024.100082 (2024).","DOI":"10.1016\/J.DIM.2024.100082"},{"key":"1722_CR49","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.jval.2021.10.008","volume":"25","author":"D Husereau","year":"2022","unstructured":"Husereau, D. et al. Consolidated health economic evaluation reporting standards (CHEERS) 2022 explanation and elaboration: a report of the ISPOR CHEERS II good practices task force. Value Health 25, 10\u201331 (2022).","journal-title":"Value Health"},{"key":"1722_CR50","doi-asserted-by":"publisher","first-page":"8140","DOI":"10.1136\/bmjgh-2021-008140","volume":"7","author":"AV Avance\u00f1a","year":"2022","unstructured":"Avance\u00f1a, A. V. & Prosser, L. A. Innovations in cost-effectiveness analysis that advance equity can expand its use in health policy Commentary. BMJ Glob. Health 7, 8140 (2022).","journal-title":"BMJ Glob. Health"},{"key":"1722_CR51","doi-asserted-by":"publisher","unstructured":"Griffiths, M. J. S. et al. Primer on health equity research in health economics and outcomes research: an ISPOR special interest group report. https:\/\/doi.org\/10.1016\/j.jval.2024.09.012 (2025).","DOI":"10.1016\/j.jval.2024.09.012"},{"key":"1722_CR52","doi-asserted-by":"publisher","first-page":"e245031\u2013e245031","DOI":"10.1001\/jamahealthforum.2024.5031","volume":"6","author":"KB Johnson","year":"2025","unstructured":"Johnson, K. B., Horn, I. B. & Horvitz, E. Pursuing equity with artificial intelligence in health care. JAMA Health Forum 6, e245031\u2013e245031 (2025).","journal-title":"JAMA Health Forum"},{"key":"1722_CR53","doi-asserted-by":"publisher","first-page":"130","DOI":"10.60087\/jklst.vol1.n1.p138","volume":"1","author":"J Sreerama","year":"2022","unstructured":"Sreerama, J. & Krishnamoorthy, G. Ethical considerations in AI addressing bias and fairness in machine learning models. J. Knowl. Learn. Sci. Technol. 1, 130\u2013138 (2022).","journal-title":"J. Knowl. Learn. Sci. Technol."},{"key":"1722_CR54","doi-asserted-by":"publisher","first-page":"e0000278","DOI":"10.1371\/journal.pdig.0000278","volume":"2","author":"LH Nazer","year":"2023","unstructured":"Nazer, L. H. et al. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health 2, e0000278 (2023).","journal-title":"PLOS Digital Health"},{"key":"1722_CR55","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jclinepi.2021.02.003","volume":"134","author":"MJ Page","year":"2021","unstructured":"Page, M. J. et al. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. J. Clin. Epidemiol. 134, 103\u2013112 (2021).","journal-title":"J. Clin. Epidemiol."},{"key":"1722_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13643-016-0384-4","volume":"5","author":"M Ouzzani","year":"2016","unstructured":"Ouzzani, M., Hammady, H., Fedorowicz, Z. & Elmagarmid, A. Rayyan-a web and mobile app for systematic reviews. Syst. Rev. 5, 1\u201310 (2016).","journal-title":"Syst. Rev."},{"key":"1722_CR57","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1111\/j.1524-4733.2006.00114.x","volume":"9","author":"R Ernst","year":"2006","unstructured":"Ernst, R. Indirect costs and cost-effectiveness analysis. Value Health 9, 253\u2013261 (2006).","journal-title":"Value Health"},{"key":"1722_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2165\/00019053-199813010-00001","volume":"13","author":"B Liljas","year":"1998","unstructured":"Liljas, B. How to calculate indirect costs in economic evaluations. Pharmacoeconomics 13, 1\u20137 (1998).","journal-title":"Pharmacoeconomics"},{"key":"1722_CR59","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1191\/1478088706qp063oa","volume":"3","author":"V Braun","year":"2006","unstructured":"Braun, V. & Clarke, V. Using thematic analysis in psychology. Qual. Res Psychol. 3, 77\u2013101 (2006).","journal-title":"Qual. Res Psychol."},{"key":"1722_CR60","doi-asserted-by":"crossref","unstructured":"Thomas, J. & Harden, A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med. Res. Methodol. 8, 45 (2008).","DOI":"10.1186\/1471-2288-8-45"},{"key":"1722_CR61","unstructured":"Standard installation (NVivo 14 Windows). https:\/\/techcenter.qsrinternational.com\/Content\/nv14\/nv14_standard_installation.htm. (2023)"},{"key":"1722_CR62","unstructured":"Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L. & Torrance, G. W. Methods for the Economic Evaluation of Health Care Programmes. Consumption Benefits of Healthcare (Oxford University Press, London, 2015)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01722-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01722-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01722-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T18:29:27Z","timestamp":1757442567000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01722-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,26]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1722"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01722-y","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,26]]},"assertion":[{"value":"16 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"548"}}