{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T15:54:05Z","timestamp":1791388445597,"version":"4.3.3"},"reference-count":57,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":15,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Heart failure is a complex clinical syndrome requiring repeated reassessment, close monitoring, and timely treatment decisions throughout hospitalization. With the increasing emergence of artificial intelligence applications, there is growing interest in how artificial intelligence-based tools may support physicians along the inpatient heart failure care pathway. This systematic review assessed artificial intelligence applications supporting adult patients with heart failure from admission through inpatient management to discharge. The review followed PRISMA guidelines and was prospectively registered in PROSPERO (CRD420261336670). PubMed, Web of Science, and Embase were searched through February 2026. Eligible studies evaluated artificial intelligence-based interventions or tools relevant to inpatient heart failure care. Studies limited to intensive care, isolated imaging settings, classic telemedicine, or theoretical prediction models without demonstrated clinical use were excluded. Study selection was performed using Covidence, and methodological quality was assessed with STROBE criteria for observational studies and RoB 2 for randomized controlled trials. 19 studies were included, comprising 16 observational studies and three randomized controlled trials. Seven studies addressed admission, four inpatient management, and eight discharge-related applications. During admission, artificial intelligence mainly supported early recognition of heart failure and reduced ejection fraction, with the strongest evidence for ECG-based models. In inpatient care, applications were heterogeneous and supported clinical decision-making, patient identification, and medication adherence prediction, using electronic health record-derived data. At discharge, large language models and digital tools mainly supported patient education and self-management, although limited actionability and implementation barriers remained. Artificial intelligence may support inpatient heart failure care, but evidence of measurable clinical benefit remains limited.<\/jats:p>\n                  <jats:p>Trial Registration PROSPERO: CRD420261336670.<\/jats:p>","DOI":"10.1093\/ehjdh\/ztag151","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:24:55Z","timestamp":1789755895000},"source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence to support clinical practice across the inpatient heart failure care pathway: a systematic review"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9342-5224","authenticated-orcid":false,"given":"Elias V","family":"Wolf","sequence":"first","affiliation":[{"name":"Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg , Im Neuenheimer Feld 460, Heidelberg 69120 ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3837-0931","authenticated-orcid":false,"given":"Miriam","family":"Grapp","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg , Im Neuenheimer Feld 460, Heidelberg 69120 ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9329-1104","authenticated-orcid":false,"given":"Mechthild","family":"Hartmann","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg , Im Neuenheimer Feld 460, Heidelberg 69120 ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4344-8959","authenticated-orcid":false,"given":"Hans-Christoph","family":"Friederich","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg , Im Neuenheimer Feld 460, Heidelberg 69120 ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8287-2635","authenticated-orcid":false,"given":"Till Johannes","family":"Bugaj","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg , Im Neuenheimer Feld 460, Heidelberg 69120 ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"2026100711214681900_ztag151-B1","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1001\/jamainternmed.2023.7410","article-title":"Population-based trends in complexity of hospital inpatients","volume":"184","author":"Naik","year":"2024","journal-title":"JAMA Intern Med"},{"key":"2026100711214681900_ztag151-B2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41746-020-0221-y","article-title":"An overview of clinical decision support systems: benefits, risks, and strategies for success","volume":"3","author":"Sutton","year":"2020","journal-title":"NPJ Digit Med"},{"key":"2026100711214681900_ztag151-B3","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/1748-5908-6-91","article-title":"Computerized clinical decision support systems for acute care management: a decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes","volume":"6","author":"Sahota","year":"2011","journal-title":"Implement Sci"},{"key":"2026100711214681900_ztag151-B4","doi-asserted-by":"crossref","DOI":"10.2196\/47715","article-title":"The impact of digital hospitals on patient and clinician experience: systematic review and qualitative evidence synthesis","volume":"26","author":"Canfell","year":"2024","journal-title":"J Med Internet Res"},{"key":"2026100711214681900_ztag151-B5","doi-asserted-by":"crossref","DOI":"10.2196\/50357","article-title":"Experience of health care professionals using digital tools in the hospital: qualitative systematic review","volume":"10","author":"Wosny","year":"2023","journal-title":"JMIR Hum Factors"},{"key":"2026100711214681900_ztag151-B6","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly","year":"2019","journal-title":"BMC Med"},{"key":"2026100711214681900_ztag151-B7","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N Engl J Med"},{"key":"2026100711214681900_ztag151-B8","doi-asserted-by":"crossref","first-page":"106649","DOI":"10.1016\/j.compbiomed.2023.106649","article-title":"Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review","volume":"155","author":"Hossain","year":"2023","journal-title":"Comput Biol Med"},{"key":"2026100711214681900_ztag151-B9","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"28","author":"Vasey","year":"2022","journal-title":"Nat Med"},{"key":"2026100711214681900_ztag151-B10","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/s11897-017-0352-x","article-title":"Inpatient monitoring of decompensated heart failure: what is needed?","volume":"14","author":"Ali","year":"2017","journal-title":"Curr Heart Fail Rep"},{"key":"2026100711214681900_ztag151-B11","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.cardfail.2025.07.007","article-title":"HF STATS 2025: heart failure epidemiology and outcomes statistics an updated 2025 report from the heart failure society of America","volume":"32","author":"Writing Committee M","year":"2026","journal-title":"J Card Fail"},{"key":"2026100711214681900_ztag151-B12","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.1093\/eurheartj\/ehaf323","article-title":"Heart failure: assessment of the global economic burden","volume":"46","author":"Darvish","year":"2025","journal-title":"Eur Heart J"},{"key":"2026100711214681900_ztag151-B13","doi-asserted-by":"crossref","first-page":"159","DOI":"10.36628\/ijhf.2023.0015","article-title":"Etiologies and predictors of 30-day readmission in heart failure: an updated analysis","volume":"5","author":"Jain","year":"2023","journal-title":"Int J Heart Fail"},{"key":"2026100711214681900_ztag151-B14","doi-asserted-by":"crossref","first-page":"11","DOI":"10.36628\/ijhf.2023.0050","article-title":"Application and potential of artificial intelligence in heart failure: past, present, and future","volume":"6","author":"Yoon","year":"2024","journal-title":"Int J Heart Fail"},{"key":"2026100711214681900_ztag151-B15","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1093\/ehjdh\/ztac025","article-title":"Applications of artificial intelligence and machine learning in heart failure","volume":"3","author":"Averbuch","year":"2022","journal-title":"Eur Heart J Digit Health"},{"key":"2026100711214681900_ztag151-B16","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1007\/s40471-020-00259-w","article-title":"Heart failure diagnosis, readmission, and mortality prediction using machine learning and artificial intelligence models","volume":"7","author":"Guo","year":"2020","journal-title":"Curr Epidemiol Rep"},{"key":"2026100711214681900_ztag151-B17","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1093\/ehjdh\/ztac035","article-title":"Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping review()","volume":"3","author":"Croon","year":"2022","journal-title":"Eur Heart J Digit Health"},{"key":"2026100711214681900_ztag151-B18","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1093\/eurheartj\/ehab368","article-title":"2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure","volume":"42","author":"McDonagh","year":"2021","journal-title":"Eur Heart J"},{"key":"2026100711214681900_ztag151-B19","first-page":"e895","article-title":"2022 AHA\/ACC\/HFSA guideline for the management of heart failure: a report of the American College of Cardiology\/American Heart Association joint committee on clinical practice guidelines","volume":"145","author":"Heidenreich","year":"2022","journal-title":"Circulation"},{"key":"2026100711214681900_ztag151-B20","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.3390\/jcm13041085","article-title":"Inpatient vs. outpatient: a systematic review of information needs throughout the heart failure patient journey","volume":"13","author":"Cotie","year":"2024","journal-title":"J Clin Med"},{"key":"2026100711214681900_ztag151-B21","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1000097","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement","volume":"6","author":"Moher","year":"2009","journal-title":"PLoS Med"},{"key":"2026100711214681900_ztag151-B22","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/S1532-3382(01)70024-3","article-title":"Enhancing your practice through evidence-based decision making: PICO, learning how to ask good questions","volume":"1","author":"Miller","year":"2001","journal-title":"The Journal of Evidenced-Based Dental Practice"},{"key":"2026100711214681900_ztag151-B23","doi-asserted-by":"crossref","DOI":"10.2196\/72211","article-title":"Exploring primary care patients\u2019 perspectives on artificial intelligence: systematic literature review and qualitative meta-synthesis","volume":"4","author":"Mundzic","year":"2025","journal-title":"JMIR AI"},{"key":"2026100711214681900_ztag151-B24","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.jclinepi.2007.11.008","article-title":"The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies","volume":"61","author":"von Elm","year":"2008","journal-title":"J Clin Epidemiol"},{"key":"2026100711214681900_ztag151-B25","doi-asserted-by":"crossref","first-page":"l4898","DOI":"10.1136\/bmj.l4898","article-title":"Rob 2: a revised tool for assessing risk of bias in randomised trials","volume":"366","author":"Sterne","year":"2019","journal-title":"BMJ"},{"key":"2026100711214681900_ztag151-B26","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1093\/jamia\/ocaf162","article-title":"Machine learning based prediction of medication adherence in heart failure using large electronic health record cohort with linkages to pharmacy-fill and neighborhood-level data","volume":"32","author":"Adhikari","year":"2025","journal-title":"J Am Med Inform Assoc"},{"key":"2026100711214681900_ztag151-B27","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1016\/j.jacc.2025.10.065","article-title":"Predicting heart failure from 12-lead ECGs using AI: a HeartShare\/AMP-HF pooled cohort analysis","volume":"87","author":"Desai","year":"2026","journal-title":"J Am Coll Cardiol"},{"key":"2026100711214681900_ztag151-B28","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1001\/jamacardio.2025.0492","article-title":"Artificial intelligence-enabled prediction of heart failure risk from single-lead electrocardiograms","volume":"10","author":"Dhingra","year":"2025","journal-title":"JAMA Cardiol"},{"key":"2026100711214681900_ztag151-B29","doi-asserted-by":"crossref","DOI":"10.1136\/openhrt-2024-002924","article-title":"Use of artificial intelligence-powered ECG to differentiate between cardiac and pulmonary pathologies in patients with acute dyspnoea in the emergency department","volume":"11","author":"Jang","year":"2024","journal-title":"Open Heart"},{"key":"2026100711214681900_ztag151-B30","article-title":"Evaluating the quality of cardiovascular disease information from AI chatbots: a comparative study","volume":"17","author":"Singavarapu","year":"2025","journal-title":"Cureus"},{"key":"2026100711214681900_ztag151-B31","doi-asserted-by":"crossref","first-page":"102797","DOI":"10.1016\/j.cpcardiol.2024.102797","article-title":"Evaluating ChatGPT platform in delivering heart failure educational material: a comparison with the leading national cardiology institutes","volume":"49","author":"Anaya","year":"2024","journal-title":"Curr Probl Cardiol"},{"key":"2026100711214681900_ztag151-B32","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1093\/ehjdh\/ztac030","article-title":"Automated detection of low ejection fraction from a one-lead electrocardiogram: application of an AI algorithm to an electrocardiogram-enabled digital stethoscope()","volume":"3","author":"Attia","year":"2022","journal-title":"Eur Heart J Digit Health"},{"key":"2026100711214681900_ztag151-B33","article-title":"Assessing information provided by ChatGPT: heart failure versus patent ductus arteriosus","volume":"17","author":"Bhupathi","year":"2025","journal-title":"Cureus"},{"key":"2026100711214681900_ztag151-B34","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s41746-020-0261-3","article-title":"Artificial intelligence for the diagnosis of heart failure","volume":"3","author":"Choi","year":"2020","journal-title":"NPJ Digit Med"},{"key":"2026100711214681900_ztag151-B35","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1080\/00015385.2025.2576451","article-title":"Evaluating the accuracy and readability of ChatGPT-4 responses about cardiac rehabilitation for heart failure patients","volume":"80","author":"Coskun","year":"2025","journal-title":"Acta Cardiol"},{"key":"2026100711214681900_ztag151-B36","doi-asserted-by":"crossref","first-page":"13502","DOI":"10.1038\/s41598-025-97113-0","article-title":"AI analysis for ejection fraction estimation from 12-lead ECG","volume":"15","author":"Devkota","year":"2025","journal-title":"Sci Rep"},{"key":"2026100711214681900_ztag151-B37","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1093\/jamia\/ocv197","article-title":"Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation","volume":"23","author":"Evans","year":"2016","journal-title":"J Am Med Inform Assoc"},{"key":"2026100711214681900_ztag151-B38","article-title":"Evaluating AI-generated patient education guides: a comparative study of ChatGPT and deepseek","volume":"17","author":"Jabeen","year":"2025","journal-title":"Cureus"},{"key":"2026100711214681900_ztag151-B39","doi-asserted-by":"crossref","DOI":"10.2196\/68817","article-title":"Improving the readability of institutional heart failure-related patient education materials using GPT-4: observational study","volume":"9","author":"King","year":"2025","journal-title":"JMIR Cardio"},{"key":"2026100711214681900_ztag151-B40","doi-asserted-by":"crossref","first-page":"153878","DOI":"10.1016\/j.jelectrocard.2025.153878","article-title":"Estimating very low ejection fraction from the 12 lead ECG among patients with acute heart failure","volume":"89","author":"Pokhrel Bhattarai","year":"2025","journal-title":"J Electrocardiol"},{"key":"2026100711214681900_ztag151-B41","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1177\/00033197241238403","article-title":"ChatGPT and patients with heart failure","volume":"76","author":"Dimitriadis","year":"2025","journal-title":"Angiology"},{"key":"2026100711214681900_ztag151-B42","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1016\/S0140-6736(25)02156-7","article-title":"Triple cardiovascular disease detection with an artificial intelligence-enabled stethoscope (TRICORDER) in the UK: a cluster-randomised controlled implementation trial","volume":"407","author":"Kelshiker","year":"2026","journal-title":"Lancet"},{"key":"2026100711214681900_ztag151-B43","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1186\/s12916-025-04190-z","article-title":"Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial","volume":"23","author":"Tsai","year":"2025","journal-title":"BMC Med"},{"key":"2026100711214681900_ztag151-B44","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1093\/ehjdh\/ztab085","article-title":"An m-health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation","volume":"2","author":"Zisis","year":"2021","journal-title":"Eur Heart J Digit Health"},{"key":"2026100711214681900_ztag151-B45","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1038\/s41746-024-01248-9","article-title":"Effects of artificial intelligence implementation on efficiency in medical imaging-a systematic literature review and meta-analysis","volume":"7","author":"Wenderott","year":"2024","journal-title":"NPJ Digit Med"},{"key":"2026100711214681900_ztag151-B46","doi-asserted-by":"crossref","first-page":"103228","DOI":"10.1016\/j.eclinm.2025.103228","article-title":"Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review","volume":"83","author":"Lawrence","year":"2025","journal-title":"EClinicalMedicine"},{"key":"2026100711214681900_ztag151-B47","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1001\/jamainternmed.2025.0821","article-title":"Physician- and large language model-generated hospital discharge summaries","volume":"185","author":"Williams","year":"2025","journal-title":"JAMA Intern Med"},{"key":"2026100711214681900_ztag151-B48","doi-asserted-by":"crossref","DOI":"10.2196\/68998","article-title":"Scientific evidence for clinical text summarization using large language models scoping review","volume":"27","author":"Bednarczyk","year":"2025","journal-title":"J Med Internet Res"},{"key":"2026100711214681900_ztag151-B49","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2022.33946","article-title":"Randomized clinical trials of machine learning interventions in health care: a systematic review","volume":"5","author":"Plana","year":"2022","journal-title":"JAMA Netw Open"},{"key":"2026100711214681900_ztag151-B50","doi-asserted-by":"crossref","first-page":"m689","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"Nagendran","year":"2020","journal-title":"BMJ"},{"key":"2026100711214681900_ztag151-B51","doi-asserted-by":"crossref","first-page":"m3164","DOI":"10.1136\/bmj.m3164","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension","volume":"370","author":"Liu","year":"2020","journal-title":"BMJ"},{"key":"2026100711214681900_ztag151-B52","doi-asserted-by":"crossref","first-page":"e549","DOI":"10.1016\/S2589-7500(20)30219-3","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension","volume":"2","author":"Rivera S","year":"2020","journal-title":"Lancet Digit Health"},{"key":"2026100711214681900_ztag151-B53","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41746-023-00852-5","article-title":"An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals","volume":"6","author":"Lambert","year":"2023","journal-title":"NPJ Digit Med"},{"key":"2026100711214681900_ztag151-B54","doi-asserted-by":"crossref","DOI":"10.1097\/CRD.0000000000001016","article-title":"AI-Guided Decision support in acute cardiac care: from chest pain to STEMI","author":"Tran","year":"2025","journal-title":"Cardiol Rev"},{"key":"2026100711214681900_ztag151-B55","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation models for generalist medical artificial intelligence","volume":"616","author":"Moor","year":"2023","journal-title":"Nature"},{"key":"2026100711214681900_ztag151-B56","doi-asserted-by":"crossref","first-page":"101029","DOI":"10.1016\/j.jacadv.2024.101029","article-title":"AI and heart failure: present state and future with multimodal large language models","volume":"3","author":"Cheema","year":"2024","journal-title":"JACC Adv"},{"key":"2026100711214681900_ztag151-B57","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.jacc.2024.05.003","article-title":"Transforming cardiovascular care with artificial intelligence: from discovery to practice: JACC state-of-the-art review","volume":"84","author":"Khera","year":"2024","journal-title":"J Am Coll Cardiol"}],"container-title":["European Heart Journal - Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ehjdh\/advance-article-pdf\/doi\/10.1093\/ehjdh\/ztag151\/71234591\/ztag151.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/7\/8\/ztag151\/71234591\/ztag151.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/7\/8\/ztag151\/71234591\/ztag151.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T15:21:59Z","timestamp":1791386519000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ehjdh\/article\/doi\/10.1093\/ehjdh\/ztag151\/8818052"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,4]]},"references-count":57,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,9,4]]}},"URL":"https:\/\/doi.org\/10.1093\/ehjdh\/ztag151","relation":{},"ISSN":["2634-3916"],"issn-type":[{"value":"2634-3916","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,10]]},"published":{"date-parts":[[2026,9,4]]},"article-number":"ztag151"}}