{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T01:30:59Z","timestamp":1776994259960,"version":"3.51.4"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T00:00:00Z","timestamp":1754092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T00:00:00Z","timestamp":1754092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001230","name":"Macquarie University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001230","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>This mixed-methods study evaluated clinicians\u2019 user experience (UX) with Generative AI (GenAI) in Electronic Health Record (EHR) systems across three clinical documentation tasks (Information Extraction, Summarization, and Speech-to-Text) at varying levels of user supervision (low, medium, high), focusing on workflow improvements, safety, and acceptable automation levels. Using conceptual prototyping in a usability study framework, we evaluated how incorporating GenAI into EHR could support the three documentation tasks at varying automation levels. A total of 38 clinicians interacted with the prototype and completed a questionnaire on task relevance, perceived importance, desired automation level, and EHR satisfaction. Both quantitative (descriptive statistics, Kruskal-Wallis tests, Spearman correlations) and qualitative (thematic) analyses were conducted with equal priority to explore preferences, perceived safety, and practical requirements. Clinicians showed positive reception to GenAI integration, particularly for streamlining documentation. While task relevance and importance were strongly correlated, EHR satisfaction did not significantly predict automation acceptance. Medium automation emerged as the preferred level, considered \u201csafe with caution\u201d. Five key themes emerged from qualitative analysis: efficiency and quality benefits; system reliability concerns; safety and medico-legal considerations; automation bias and loss of nuance; and deployment requirements including adjustable settings and oversight. While clinicians welcome GenAI-driven documentation, they prefer moderate automation to balance efficiency with clinical control. Successful integration requires addressing safety concerns, conducting real-world trials, and mitigating potential biases and medico-legal challenges. These findings suggest a cautious but optimistic path forward for AI integration in EHR systems, emphasizing the importance of maintaining clinician oversight while leveraging automation benefits.<\/jats:p>","DOI":"10.1007\/s10916-025-02234-8","type":"journal-article","created":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T04:42:30Z","timestamp":1754109750000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Understanding Clinician Perceptions of GenAI: A Mixed Methods Analysis of Clinical Documentation Tasks"],"prefix":"10.1007","volume":"49","author":[{"given":"David","family":"Fraile Navarro","sequence":"first","affiliation":[]},{"given":"A. Baki","family":"Kocaballi","sequence":"additional","affiliation":[]},{"given":"Shlomo","family":"Berkovsky","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,2]]},"reference":[{"key":"2234_CR1","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.ijmedinf.2015.03.003","volume":"84","author":"L Colligan","year":"2015","unstructured":"Colligan, L., Potts, H. W., and Finn, C. T., et al., Cognitive workload changes for nurses transitioning from a legacy system with paper documentation to a commercial electronic health record. Int. J. Med. Inform. 84:469\u2013476, 2015.","journal-title":"Int. J. Med. Inform."},{"key":"2234_CR2","unstructured":"Bathelt, F., The usage of OHDSI OMOP\u2013a scoping review. In: Proceedings of the German Medical Data Sciences (GMDS), pp. 95\u201395, 2021."},{"key":"2234_CR3","first-page":"279","volume":"121","author":"K Donnelly","year":"2006","unstructured":"Donnelly, K., SNOMED-CT: The advanced terminology and coding system for ehealth. Stud. Health Technol. Inform. 121:279, 2006.","journal-title":"Stud. Health Technol. Inform."},{"key":"2234_CR4","unstructured":"Lehne, M., Luijten, S., and Imbusch, P. V. F., et al., The use of FHIR in digital health-a review of the scientific literature. GMDS, 52\u201358, 2019."},{"key":"2234_CR5","doi-asserted-by":"publisher","unstructured":"Quiroz, J. C., Laranjo, L., and Kocaballi, A. B., et al., Challenges of developing a digital scribe to reduce clinical documentation burden. Npj Digit. Med. 2:1\u20136, 2019. https:\/\/doi.org\/10.1038\/s41746-019-0190-1","DOI":"10.1038\/s41746-019-0190-1"},{"key":"2234_CR6","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1377\/hlthaff.2012.0913","volume":"32","author":"ES Huang","year":"2013","unstructured":"Huang, E. S., and Finegold, K., Seven million americans live in areas where demand for primary care may exceed supply by more than 10 percent. Health Aff. 32:614\u2013621, 2013.","journal-title":"Health Aff."},{"key":"2234_CR7","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1056\/CAT.23.0404","volume":"5","author":"AA Tierney","year":"2024","unstructured":"Tierney, A. A., Gayre, G., and Hoberman, B., et al., Ambient artificial intelligence scribes to alleviate the burden of clinical documentation. NEJM Catalyst. 5:23-0404, 2024. https:\/\/doi.org\/10.1056\/CAT.23.0404","journal-title":"NEJM Catalyst."},{"key":"2234_CR8","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1093\/jamia\/ocaa131","volume":"27","author":"AB Kocaballi","year":"2020","unstructured":"Kocaballi, A. B., Ijaz, K., and Laranjo, L., et al., Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners. J. Am. Med. Inform. Assoc. 27:1695\u20131704, 2020.","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"2234_CR9","doi-asserted-by":"publisher","unstructured":"Navarro, D. F., Kocaballi, A. B., and Dras, M., et al., Collaboration, not confrontation: Understanding general practitioners\u2019 attitudes towards natural language and text automation in clinical practice. ACM Trans. Comput.-Hum. Interact. 2022. https:\/\/doi.org\/10.1145\/3569893. Published Online First: 27 Oct 2022.","DOI":"10.1145\/3569893"},{"key":"2234_CR10","doi-asserted-by":"crossref","unstructured":"Nielsen, J., Usability engineering. Morgan Kaufmann, 1994.","DOI":"10.1016\/B978-0-08-052029-2.50009-7"},{"key":"2234_CR11","doi-asserted-by":"publisher","first-page":"2400659","DOI":"10.1056\/AIoa2400659","volume":"1","author":"T-L Liu","year":"2024","unstructured":"Liu, T.-L., Hetherington, T. C., and Dharod, A., et al., Does AI-powered clinical documentation enhance clinician efficiency? A longitudinal study. NEJM AI. 1:2400659, 2024. https:\/\/doi.org\/10.1056\/AIoa2400659","journal-title":"NEJM AI."},{"key":"2234_CR12","doi-asserted-by":"crossref","unstructured":"McKnight, P. E., and Najab, J., Kruskal\u2013wallis test. In: The Corsini Encyclopedia of Psychology. pp. 1\u20131. 2010.","DOI":"10.1002\/9780470479216.corpsy0491"},{"key":"2234_CR13","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1038\/s41746-018-0066-9","volume":"1","author":"E Coiera","year":"2018","unstructured":"Coiera, E., Kocaballi, B., and Halamka, J., et al., The digital scribe. NPJ Digit. Med. 1:58, 2018","journal-title":"NPJ Digit. Med."},{"key":"2234_CR14","doi-asserted-by":"crossref","unstructured":"Zar, J. H., Spearman rank correlation. In: Encyclopedia of Biostatistics. Vol. 7. 2005.","DOI":"10.1002\/0470011815.b2a15150"},{"key":"2234_CR15","doi-asserted-by":"crossref","unstructured":"Sun, T., Gaut, A., and Tang, S., et al., Mitigating gender bias in natural language processing: Literature review. arXiv:1906.08976, 2019.","DOI":"10.18653\/v1\/P19-1159"},{"key":"2234_CR16","doi-asserted-by":"crossref","unstructured":"Chen, J. H., and Asch, S. M., Machine learning and prediction in medicine\u2014beyond the peak of inflated expectations. N. Engl. J. Med. 376:2507, 2017.","DOI":"10.1056\/NEJMp1702071"},{"key":"2234_CR17","doi-asserted-by":"publisher","first-page":"2212","DOI":"10.1053\/j.gastro.2020.12.065","volume":"160","author":"E Sinagra","year":"2021","unstructured":"Sinagra, E., Rossi, F., and Raimondo, D., Use of artificial intelligence in endoscopic training: Is deskilling a real fear? Gastroenterology. 160:2212, 2021.","journal-title":"Gastroenterology."},{"key":"2234_CR18","doi-asserted-by":"crossref","unstructured":"Cai, C. J., Reif, E., and Hegde, N., et al., Human-centered tools for coping with imperfect algorithms during medical decision-making. In: Proceedings of the 2019 Chi Conference on Human Factors in Computing Systems. pp. 1\u201314. 2019.","DOI":"10.1145\/3290605.3300234"},{"key":"2234_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-017-0425-5","volume":"17","author":"D Lyell","year":"2017","unstructured":"Lyell, D., Magrabi, F., and Raban, M. Z., et al., Automation bias in electronic prescribing. BMC Med. Inform. Decis. Mak. 17:1\u201310, 2017.","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"2234_CR20","doi-asserted-by":"publisher","first-page":"19274","DOI":"10.2196\/19274","volume":"22","author":"T Tajirian","year":"2020","unstructured":"Tajirian, T., Stergiopoulos, V., and Strudwick, G., et al., The influence of electronic health record use on physician burnout: Cross-sectional survey. J. Med. Internet Res. 22:19274, 2020.","journal-title":"J. Med. Internet Res."},{"key":"2234_CR21","unstructured":"Bubeck, S., Chandrasekaran, V., and Eldan, R., et al.: Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv:2303.12712, 2023."},{"key":"2234_CR22","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., Mann, B., and Ryder, N., et al., Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33:1877\u20131901, 2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2234_CR23","doi-asserted-by":"crossref","unstructured":"Biswas, S., ChatGPT and the future of medical writing. Radiology. 2023. 10.1148\/radiol.223312. Published Online First: 2 Feb. 2023.","DOI":"10.1148\/radiol.223312"},{"key":"2234_CR24","unstructured":"Tomitsch, M., Wrigley, C., Borthwick, M., Ahmadpour, N., Frawley, J., Kocaballi, A. B., N\u00fanez-Pacheco, C., and Straker, K., Design. think. make. break. repeat. A handbook of methods. BIS publishers, 2018."},{"key":"2234_CR25","doi-asserted-by":"crossref","unstructured":"Parasuraman, R., Sheridan, T. B., and Wickens, C. D., A model for types and levels of human interaction with automation. Vol. 30, pp. 286\u2013297, 2000.","DOI":"10.1109\/3468.844354"},{"issue":"3","key":"2234_CR26","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1080\/001401399185595","volume":"42","author":"MR Endsley","year":"1999","unstructured":"Endsley, M. R., and Kaber, D. B., Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics. 42(3):462\u2013492, 1999.","journal-title":"Ergonomics."},{"key":"2234_CR27","doi-asserted-by":"crossref","unstructured":"Sedgwick, P., Multiple significance tests: the bonferroni correction. Bmj. 344, 2012.","DOI":"10.1136\/bmj.e509"},{"key":"2234_CR28","doi-asserted-by":"crossref","unstructured":"Braun, V., and Clarke, V., Using thematic analysis in psychology. Vol. 3, pp. 77\u2013101. 2006.","DOI":"10.1191\/1478088706qp063oa"},{"key":"2234_CR29","doi-asserted-by":"crossref","unstructured":"Lin, S. Y., Mahoney, M. R., and Sinsky, C. A., Ten ways artificial intelligence will transform primary care. J. Gen. Intern. Med. 34:1626\u20131630, 2019.","DOI":"10.1007\/s11606-019-05035-1"},{"key":"2234_CR30","doi-asserted-by":"publisher","first-page":"12802","DOI":"10.2196\/12802","volume":"21","author":"C Blease","year":"2019","unstructured":"Blease, C., Kaptchuk, T. J., and Bernstein, M. H., et al., Artificial intelligence and the future of primary care: Exploratory qualitative study of UK general practitioners\u2019 views. J. Med. Internet Res. 21:12802, 2019.","journal-title":"J. Med. Internet Res."},{"key":"2234_CR31","doi-asserted-by":"publisher","unstructured":"Cabral, S., Restrepo, D., and Kanjee, Z., et al., Clinical reasoning of a generative artificial intelligence model compared with physicians. JAMA Intern. Med. 2024. https:\/\/doi.org\/10.1001\/jamainternmed.2024.0295. Published Online First: 1 April 2024","DOI":"10.1001\/jamainternmed.2024.0295"},{"key":"2234_CR32","doi-asserted-by":"crossref","unstructured":"Haim, A., Katson, M., and Cohen-Shelly, M., et al., Evaluating GPT-4 as a clinical decision support tool in ischemic stroke management. 2024.01.18.24301409, 2024.","DOI":"10.1101\/2024.01.18.24301409"},{"key":"2234_CR33","unstructured":"Australian Digital Health Agency: My Health Record Statistics. https:\/\/www.digitalhealth.gov.au\/initiatives-and-programs\/my-health-record. Accessed: 2024. 2024."},{"issue":"2","key":"2234_CR34","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1177\/001872089403600215","volume":"36","author":"JR Lewis","year":"1994","unstructured":"Lewis, J. R., Sample sizes for usability studies: Additional considerations. Hum. Factors 36(2):368\u2013378, 1994.","journal-title":"Hum. Factors"},{"key":"2234_CR35","doi-asserted-by":"crossref","unstructured":"Sauro, J., and Lewis, J. R., Quantifying the user experience: practical statistics for user research. Morgan Kaufmann, 2016.","DOI":"10.1016\/B978-0-12-802308-2.00002-3"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02234-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-025-02234-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02234-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T11:40:34Z","timestamp":1757331634000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-025-02234-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,2]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2234"],"URL":"https:\/\/doi.org\/10.1007\/s10916-025-02234-8","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,2]]},"assertion":[{"value":"30 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 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 study was approved by Macquarie University Ethics (REF-52022931342227). All participants provided informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"The authors declare no competing interests","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"101"}}