{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T21:04:15Z","timestamp":1784322255854,"version":"3.55.0"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100011488","name":"Shanghai Pudong New Area Health Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011488","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Medical Informatics"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.ijmedinf.2026.106598","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T06:39:37Z","timestamp":1783751977000},"page":"106598","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Real-world evaluation of medication recommendation workflows: Retrieval augmentation, physician-RAG collaborative workflow, and prescribing quality"],"prefix":"10.1016","volume":"220","author":[{"given":"Long","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yabin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ijmedinf.2026.106598_b0005","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0010","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0015","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","author":"Singhal","year":"2023","journal-title":"Nature 620"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0020","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1056\/NEJMsr2214184","article-title":"Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine","volume":"388","author":"Lee","year":"2023","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0025","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000198","article-title":"Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models","volume":"2","author":"Kung","year":"2023","journal-title":"PLOS Digit. Health"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0030","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1001\/jamainternmed.2023.1838","article-title":"Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum","volume":"183","author":"Ayers","year":"2023","journal-title":"JAMA Intern. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0035","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1038\/s41591-019-0548-6","article-title":"Do no harm: a roadmap for responsible machine learning for health care","volume":"25","author":"Wiens","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0040","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1001\/jama.2019.20866","article-title":"Challenges to the reproducibility of machine learning models in health care","volume":"323","author":"Beam","year":"2020","journal-title":"JAMA"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0045","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1001\/jamainternmed.2018.3763","article-title":"Potential biases in machine learning algorithms using electronic health record data","volume":"178","author":"Gianfrancesco","year":"2018","journal-title":"JAMA Intern. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0050","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1038\/s41591-020-1037-7","article-title":"SPIRIT-AI and CONSORT-AI working group, guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension","volume":"26","author":"Cruz Rivera","year":"2020","journal-title":"Nat. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0055","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"SPIRIT-AI and CONSORT-AI working group, reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0060","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"DECIDE-AI expert group, 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":"10.1016\/j.ijmedinf.2026.106598_b0065","first-page":"17","article-title":"An overview of clinical decision support systems: benefits, risks, and strategies for success, npj Digit","volume":"3","author":"Sutton","year":"2020","journal-title":"Med"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0070","first-page":"96","article-title":"Prevalence and economic burden of medication errors in the NHS in England, BMJ Qual","volume":"30","author":"Elliott","year":"2021","journal-title":"Saf"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0075","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1186\/s12916-020-01774-9","article-title":"Preventable medication harm across health care settings: a systematic review and meta-analysis","volume":"18","author":"Hodkinson","year":"2020","journal-title":"BMC Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0080","article-title":"Prevalence, severity, and nature of preventable patient harm across medical care settings: Systematic review and meta-analysis","volume":"366","author":"Panagioti","year":"2019","journal-title":"BMJ"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0085","series-title":"Medication without harm: WHO global patient safety challenge on medication safety","author":"World Health Organization","year":"2017"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0090","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1093\/ageing\/afu145","article-title":"STOPP\/START criteria for potentially inappropriate prescribing in older people: version 2","volume":"44","author":"O\u2019Mahony","year":"2015","journal-title":"Age Ageing"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0095","doi-asserted-by":"crossref","first-page":"72","DOI":"10.5414\/CPP46072","article-title":"STOPP (Screening tool of older persons\u2019 potentially inappropriate prescriptions) and START (screening tool to alert doctors to right treatment): consensus validation","volume":"46","author":"Gallagher","year":"2008","journal-title":"Int. J. Clin. Pharmacol. Ther."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0100","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive NLP tasks","volume":"33","author":"Lewis","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0105","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large language models in medicine","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nat. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0110","first-page":"e428","article-title":"Ethical and regulatory challenges of large language models in medicine, Lancet Digit","volume":"6","author":"Ong","year":"2024","journal-title":"Health"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0115","first-page":"62","article-title":"Generative AI and large language models in health care: pathways to implementation, npj Digit","volume":"7","author":"Raza","year":"2024","journal-title":"Med"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0120","doi-asserted-by":"crossref","DOI":"10.1056\/AIp2400223","article-title":"A call for artificial intelligence implementation science centers to evaluate clinical effectiveness","volume":"1","author":"Longhurst","year":"2024","journal-title":"NEJM AI"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0125","doi-asserted-by":"crossref","first-page":"8236","DOI":"10.1038\/s41467-024-52415-1","article-title":"Evaluating the use of large language models to provide clinical recommendations in the emergency department","volume":"15","author":"Williams","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0130","article-title":"Can large language models reason about medical questions?","volume":"5","author":"Li\u00e9vin","year":"2024","journal-title":"Cell Rep. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0135","doi-asserted-by":"crossref","DOI":"10.1056\/AIdbp2500120","article-title":"Assessment of large language models in clinical reasoning: A novel benchmarking study","volume":"2","author":"McCoy","year":"2025","journal-title":"NEJM AI"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0140","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2025.26021","article-title":"Fidelity of medical reasoning in large language models","volume":"8","author":"Bedi","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0145","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1038\/s41598-025-31010-4","article-title":"Benchmarking large language models on the United States medical licensing examination for clinical reasoning and medical licensing scenarios","volume":"16","author":"Siam","year":"2026","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0150","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1056\/NEJMsa2206117","article-title":"The safety of inpatient health care","volume":"388","author":"Bates","year":"2023","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0155","first-page":"133","article-title":"The impact of transition to a digital hospital on medication errors (TIME study), npj Digit","volume":"6","author":"Engstrom","year":"2023","journal-title":"Med"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0160","first-page":"87","article-title":"Short- and long-term effects of an electronic medication management system on prescribing errors: a before-and-after study, npj Digit","volume":"5","author":"Westbrook","year":"2022","journal-title":"Med"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0165","doi-asserted-by":"crossref","DOI":"10.1056\/AIoa2300068","article-title":"Almanac-Retrieval-augmented language models for clinical medicine","volume":"1","author":"Zakka","year":"2024","journal-title":"NEJM AI"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0170","doi-asserted-by":"crossref","DOI":"10.1056\/AIra2400380","article-title":"RAG in health care: a novel framework for improving communication and decision-making by addressing LLM limitations","volume":"2","author":"Ng","year":"2025","journal-title":"NEJM AI"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0175","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1093\/jamia\/ocaf008","article-title":"Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines","volume":"32","author":"Liu","year":"2025","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.ijmedinf.2026.106598_b0180","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000877","article-title":"Retrieval augmented generation for large language models in healthcare: A systematic review","author":"Amugongo","year":"2025","journal-title":"PLOS Digit. Health"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0185","doi-asserted-by":"crossref","DOI":"10.1056\/AIoa2400181","article-title":"Retrieval-augmented generation-enabled GPT-4 for clinical trial screening","volume":"1","author":"Unlu","year":"2024","journal-title":"NEJM AI"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0190","doi-asserted-by":"crossref","DOI":"10.1056\/AIcs2401161","article-title":"Assessing retrieval-augmented large language models for medical coding","volume":"2","author":"Klang","year":"2025","journal-title":"NEJM AI"},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106598_b0195","doi-asserted-by":"crossref","DOI":"10.1056\/AIdbp2500418","article-title":"Verifying facts in patient care documents generated by large language models using electronic health records","volume":"3","author":"Chung","year":"2025","journal-title":"NEJM AI"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0200","first-page":"187","article-title":"Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness, npj Digit","volume":"8","author":"Ke","year":"2025","journal-title":"Med"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0205","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2024.40969","article-title":"Large language model influence on diagnostic reasoning: A randomized clinical trial","volume":"7","author":"Goh","year":"2024","journal-title":"JAMA Netw. Open"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0210","first-page":"47","article-title":"Human-AI teaming in healthcare: 1 + 1 > 2? npj Artif","volume":"1","author":"Liu","year":"2025","journal-title":"Intell"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0215","first-page":"195","article-title":"Human-large language model collaboration in clinical medicine: A systematic review and meta-analysis, npj Digit","volume":"9","author":"Wang","year":"2026","journal-title":"Med"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0220","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyt.2026.1729175","article-title":"The augmented clinician as a framework for human-AI collaboration in mental healthcare","volume":"17","author":"Ruan","year":"2026","journal-title":"Front. Psychiatry"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0225","first-page":"87","article-title":"The algorithm journey map: A tangible approach to implementing AI solutions in healthcare, npj Digit","volume":"7","author":"Boag","year":"2024","journal-title":"Med"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0230","doi-asserted-by":"crossref","DOI":"10.1056\/AIoa2300030","article-title":"Characterizing the clinical adoption of medical AI devices through U.S. insurance claims","volume":"1","author":"Wu","year":"2024","journal-title":"NEJM AI"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0235","first-page":"462","article-title":"Clinical and economic impact of a large language model in perioperative medicine: a randomized crossover trial, npj Digit","volume":"8","author":"Ke","year":"2025","journal-title":"Med"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0240","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1001\/jama.2024.21700","article-title":"Testing and evaluation of health care applications of large language models: A systematic review","volume":"333","author":"Bedi","year":"2025","journal-title":"JAMA"},{"key":"10.1016\/j.ijmedinf.2026.106598_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpi.2025.100520","article-title":"Retrieval-augmented generation for interpreting clinical laboratory regulations using large language models","volume":"19","author":"Nanua","year":"2025","journal-title":"J. Pathol. Inform."}],"container-title":["International Journal of Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505626003382?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505626003382?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T20:22:07Z","timestamp":1784319727000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1386505626003382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":49,"alternative-id":["S1386505626003382"],"URL":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2026.106598","relation":{},"ISSN":["1386-5056"],"issn-type":[{"value":"1386-5056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Real-world evaluation of medication recommendation workflows: Retrieval augmentation, physician-RAG collaborative workflow, and prescribing quality","name":"articletitle","label":"Article Title"},{"value":"International Journal of Medical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2026.106598","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106598"}}