{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:44:59Z","timestamp":1778604299360,"version":"3.51.4"},"reference-count":77,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2423235"],"award-info":[{"award-number":["2423235"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Clinician notes are a rich source of patient information, but often contain inconsistencies due to varied writing styles, abbreviations, medical jargon, grammatical errors, and non-standard formatting. These inconsistencies hinder their direct use in patient care and degrade the performance of downstream computational applications that rely on these notes as input, such as quality improvement, population health analytics, precision medicine, clinical decision support, and research. We present a large-language-model (LLM) approach to the preprocessing of 1618 neurology notes. The LLM corrected spelling and grammatical errors, expanded acronyms, and standardized terminology and formatting, without altering clinical content. Expert review of randomly sampled notes confirmed that no significant information was lost. To evaluate downstream impact, we applied an ontology-based NLP pipeline (Doc2Hpo) to extract biomedical concepts from the notes before and after editing. F1 scores for Human Phenotype Ontology extraction improved from 0.40 to 0.61, confirming our hypothesis that better inputs yielded better outputs. We conclude that LLM-based preprocessing is an effective error correction strategy that improves data quality at the level of free text in clinical notes. This approach may enhance the performance of a broad class of downstream applications that derive their input from unstructured clinical documentation.<\/jats:p>","DOI":"10.3390\/info16060446","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T11:12:57Z","timestamp":1748344377000},"page":"446","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Preprocessing of Physician Notes by LLMs Improves Clinical Concept Extraction Without Information Loss"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6179-0793","authenticated-orcid":false,"given":"Daniel B.","family":"Hier","sequence":"first","affiliation":[{"name":"Department of Neurology & Rehabilitation, University of Illinois at Chicago, Chicago, IL 60612, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5337-0804","authenticated-orcid":false,"given":"Michael A.","family":"Carrithers","sequence":"additional","affiliation":[{"name":"Department of Neurology & Rehabilitation, University of Illinois at Chicago, Chicago, IL 60612, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8270-1932","authenticated-orcid":false,"given":"Steven K.","family":"Platt","sequence":"additional","affiliation":[{"name":"Laboratory for Applied Artificial Intelligence, Loyola University Chicago, Chicago, IL 60611, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8836-7944","authenticated-orcid":false,"given":"Anh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Laboratory for Applied Artificial Intelligence, Loyola University Chicago, Chicago, IL 60611, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Giannopoulos","sequence":"additional","affiliation":[{"name":"Laboratory for Applied Artificial Intelligence, Loyola University Chicago, Chicago, IL 60611, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0155-9733","authenticated-orcid":false,"given":"Tayo","family":"Obafemi-Ajayi","sequence":"additional","affiliation":[{"name":"Engineering Program, Missouri State University, Springfield, MO 65897, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.2147\/RMHP.S12985","article-title":"Benefits and drawbacks of electronic health record systems","volume":"4","author":"Menachemi","year":"2011","journal-title":"Risk Manag. Healthc. Policy"},{"key":"ref_2","first-page":"189","article-title":"Handwriting errors: Harmful, wasteful and preventable","volume":"99","author":"Bruner","year":"2001","journal-title":"J.-Ky. Med Assoc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/BF02599144","article-title":"Deciphering the physician note","volume":"9","author":"Kozak","year":"1994","journal-title":"J. Gen. Intern. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1177\/014107680209501105","article-title":"Illegible handwriting in medical records","volume":"95","author":"Marin","year":"2002","journal-title":"J. R. Soc. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2426956","DOI":"10.1001\/jamanetworkopen.2024.26956","article-title":"Electronic health record usability, satisfaction, and burnout for family physicians","volume":"7","author":"Holmgren","year":"2024","journal-title":"JAMA Netw. Open"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Muhiyaddin, R., Elfadl, A., Mohamed, E., Shah, Z., Alam, T., Abd-Alrazaq, A., and Househ, M. (2022). Electronic health records and physician burnout: A scoping review. Informatics and Technology in Clinical Care and Public Health, IOS Press.","DOI":"10.3233\/SHTI210962"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"50","DOI":"10.7326\/M18-0139","article-title":"Physician burnout in the electronic health record era: Are we ignoring the real cause?","volume":"169","author":"Downing","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s12529-021-10010-4","article-title":"Electronic health records, medical practice problems, and physician distress","volume":"29","author":"Elliott","year":"2022","journal-title":"Int. J. Behav. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1001\/jamapediatrics.2014.3115","article-title":"How health information technology is failing to achieve its full potential","volume":"169","author":"Carroll","year":"2015","journal-title":"JAMA Pediatr."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fern\u00e1ndez, J.M., Loeb, J.A., and Hier, D.B. (2022). It\u2019s time to change our documentation philosophy: Writing better neurology notes without the burnout. Front. Digit. Health, 4.","DOI":"10.3389\/fdgth.2022.1063141"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"316","DOI":"10.3122\/jabfm.2015.03.140244","article-title":"Physician information needs and electronic health records (EHRs): Time to reengineer the clinic note","volume":"28","author":"Koopman","year":"2015","journal-title":"J. Am. Board Fam. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21501319231166921","DOI":"10.1177\/21501319231166921","article-title":"Burnout related to electronic health record use in primary care","volume":"14","author":"Budd","year":"2023","journal-title":"J. Prim. Care Community Health"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2040","DOI":"10.1093\/jamia\/ocae157","article-title":"Disambiguation of acronyms in clinical narratives with large language models","volume":"31","author":"Kugic","year":"2024","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1136\/bmj.p1516","article-title":"When I use a word\u2026Medical slang: A taxonomy","volume":"382","author":"Aronson","year":"2023","journal-title":"BMJ"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1016\/j.pec.2017.02.018","article-title":"Patient-centric medical notes: Identifying areas for improvement in the age of open medical records","volume":"100","author":"Lee","year":"2017","journal-title":"Patient Educ. Couns."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"S85","DOI":"10.5993\/AJHB.31.s1.11","article-title":"Babel babble: Physicians\u2019 use of unclarified medical jargon with patients","volume":"31","author":"Castro","year":"2007","journal-title":"Am. J. Health Behav."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1007\/s11606-019-05526-1","article-title":"Eradicating jargon-oblivion\u2014a proposed classification system of medical jargon","volume":"35","author":"Pitt","year":"2020","journal-title":"J. Gen. Intern. Med."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Workman, T.E., Shao, Y., Divita, G., and Zeng-Treitler, Q. (2019). An efficient prototype method to identify and correct misspellings in clinical text. BMC Res. Notes, 12.","DOI":"10.1186\/s13104-019-4073-y"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1136\/postgradmedj-2017-135515","article-title":"Frequency, comprehension and attitudes of physicians towards abbreviations in the medical record","volume":"94","author":"Hamiel","year":"2018","journal-title":"Postgrad. Med J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1136\/jamia.2010.006130","article-title":"A randomized-controlled trial of computerized alerts to reduce unapproved medication abbreviation use","volume":"18","author":"Myers","year":"2011","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"294","DOI":"10.4212\/cjhp.v65i4.1160","article-title":"Prohibited abbreviations: Seeking to educate, not enforce","volume":"65","author":"Horon","year":"2012","journal-title":"Can. J. Hosp. Pharm."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1177\/1060028017740140","article-title":"Audit on the use of dangerous abbreviations, symbols, and dose designations in paper compared to electronic medication orders: A multicenter study","volume":"52","author":"Cheung","year":"2018","journal-title":"Ann. Pharmacother."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"246","DOI":"10.4212\/cjhp.v64i4.1036","article-title":"Avoiding potential medication errors associated with non-intuitive medication abbreviations","volume":"64","author":"Shultz","year":"2011","journal-title":"Can. J. Hosp. Pharm."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1310\/hpj4109-809","article-title":"Campaign to Eliminate Use of Error-Prone Abbreviations","volume":"41","author":"Baker","year":"2006","journal-title":"Hosp. Pharm."},{"key":"ref_25","unstructured":"American Hospital Association, and American Society of Health-System Pharmacists (2005). Medication safety issue brief. Eliminating dangerous abbreviations, acronyms and symbols. Hosp. Health Networks, 79, 41\u201342."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1055\/s-0039-1692164","article-title":"Challenges and opportunities to improve the clinician experience reviewing electronic progress notes","volume":"10","author":"Hultman","year":"2019","journal-title":"Appl. Clin. Inform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1197\/jamia.M3390","article-title":"Quantifying clinical narrative redundancy in an electronic health record","volume":"17","author":"Wrenn","year":"2010","journal-title":"J. Am. Med Inform. Assoc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e12","DOI":"10.2105\/AJPH.2014.302164","article-title":"Effectiveness of computerized decision support systems linked to electronic health records: A systematic review and meta-analysis","volume":"104","author":"Moja","year":"2014","journal-title":"Am. J. Public Health"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1111\/joim.12119","article-title":"Electronic health records: New opportunities for clinical research","volume":"274","author":"Coorevits","year":"2013","journal-title":"J. Intern. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.clp.2023.01.008","article-title":"The electronic health record as a quality improvement tool: Exceptional potential with special considerations","volume":"50","author":"Carr","year":"2023","journal-title":"Clin. Perinatol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e35724","DOI":"10.2196\/35724","article-title":"Fast healthcare interoperability resources (FHIR) for interoperability in health research: Systematic review","volume":"10","author":"Vorisek","year":"2022","journal-title":"JMIR Med. Inform."},{"key":"ref_32","unstructured":"Lehne, M., Luijten, S., Vom Felde Genannt Imbusch, P., and Thun, S. (2019). The use of FHIR in digital health\u2013a review of the scientific literature. German Medical Data Sciences: Shaping Change\u2013Creative Solutions for Innovative Medicine, IOS Press."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sitapati, A., Kim, H., Berkovich, B., Marmor, R., Singh, S., El-Kareh, R., Clay, B., and Ohno-Machado, L. (2017). Integrated precision medicine: The role of electronic health records in delivering personalized treatment. Wiley Interdiscip. Rev. Syst. Biol. Med., 9.","DOI":"10.1002\/wsbm.1378"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1007\/s10916-018-1075-6","article-title":"The use of electronic health records to support population health: A systematic review of the literature","volume":"42","author":"Kruse","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"100511","DOI":"10.1016\/j.cosrev.2022.100511","article-title":"Neural natural language processing for unstructured data in electronic health records: A review","volume":"46","author":"Li","year":"2022","journal-title":"Comput. Sci. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: A systematic review","volume":"25","author":"Xiao","year":"2018","journal-title":"J. Am. Med Inform. Assoc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1373\/49.4.624","article-title":"LOINC, a universal standard for identifying laboratory observations: A 5-year update","volume":"49","author":"McDonald","year":"2003","journal-title":"Clin. Chem."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/2041-1480-4-44","article-title":"Building a drug ontology based on RxNorm and other sources","volume":"4","author":"Hanna","year":"2013","journal-title":"J. Biomed. Semant."},{"key":"ref_39","first-page":"14","article-title":"Comparison of the accuracy of inpatient morbidity coding with ICD-11 and ICD-10","volume":"54","author":"Zarei","year":"2025","journal-title":"Health Inf. Manag. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e11","DOI":"10.1136\/amiajnl-2013-001636","article-title":"Literature review of SNOMED CT use","volume":"21","author":"Lee","year":"2014","journal-title":"J. Am. Med Inform. Assoc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"D865","DOI":"10.1093\/nar\/gkw1039","article-title":"The human phenotype ontology in 2017","volume":"45","author":"Vasilevsky","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1080\/20009666.2017.1379852","article-title":"Use of dictation as a tool to decrease documentation errors in electronic health records","volume":"7","author":"Upadhaya","year":"2017","journal-title":"J. Community Hosp. Intern. Med. Perspect."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1093\/jamia\/ocaa199","article-title":"Safe use of the EHR by medical scribes: A qualitative study","volume":"28","author":"Ash","year":"2021","journal-title":"J. Am. Med Inform. Assoc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"790","DOI":"10.4338\/ACI-2015-11-RA-0164","article-title":"The use of evidence-based, problem-oriented templates as a clinical decision support in an inpatient electronic health record system","volume":"7","author":"Mehta","year":"2016","journal-title":"Appl. Clin. Inform."},{"key":"ref_45","first-page":"527","article-title":"Template Design and Analysis: Integrating Informatics Solutions to Improve Clinical Documentation","volume":"37","author":"Iannello","year":"2020","journal-title":"Fed. Pract."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.jbi.2015.04.008","article-title":"Automated misspelling detection and correction in clinical free-text records","volume":"55","author":"Lai","year":"2015","journal-title":"J. Biomed. Inform."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"59297","DOI":"10.1109\/ACCESS.2023.3284682","article-title":"Clinical errors from acronym use in electronic health record: A review of NLP-based disambiguation techniques","volume":"11","author":"Amosa","year":"2023","journal-title":"IEEE Access"},{"key":"ref_48","first-page":"26","article-title":"Technology in Medicine: Improving Clinical Documentation","volume":"537","author":"Baughman","year":"2024","journal-title":"FP Essentials"},{"key":"ref_49","unstructured":"NYU Langone Health (2025, May 08). Artificial Intelligence Feedback on Physician Notes Improves Patient Care. Available online: https:\/\/nyulangone.org\/news\/artificial-intelligence-feedback-physician-notes-improves-patient-care."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"100157","DOI":"10.1016\/j.fhj.2024.100157","article-title":"Use of an ambient artificial intelligence tool to improve quality of clinical documentation","volume":"11","author":"Balloch","year":"2024","journal-title":"Future Healthc. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e258614","DOI":"10.1001\/jamanetworkopen.2025.8614","article-title":"Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians","volume":"8","author":"Stults","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e6450","DOI":"10.1097\/GOX.0000000000006450","article-title":"Artificial Intelligence Scribe and Large Language Model Technology in Healthcare Documentation: Advantages, Limitations, and Recommendations","volume":"13","author":"Mess","year":"2025","journal-title":"Plast. Reconstr. Surg.-Open"},{"key":"ref_53","first-page":"e26330","article-title":"Interfacing With the Electronic Health Record (EHR): A Comparative Review of Modes of Documentation","volume":"14","author":"Avendano","year":"2022","journal-title":"Cureus"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1093\/jamia\/ocae074","article-title":"Large language models for biomedicine: Foundations, opportunities, challenges, and best practices","volume":"31","author":"Sahoo","year":"2024","journal-title":"J. Am. Med Inform. Assoc."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1994","DOI":"10.1093\/jamia\/ocae072","article-title":"Large language models facilitate the generation of electronic health record phenotyping algorithms","volume":"31","author":"Yan","year":"2024","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Munzir, S.I., Hier, D.B., and Carrithers, M.D. (2024). High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models. arXiv.","DOI":"10.1109\/EMBC53108.2024.10782119"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Omiye, J.A., Gui, H., Rezaei, S.J., Zou, J., and Daneshjou, R. (2023). Large language models in medicine: The potentials and pitfalls. arXiv.","DOI":"10.7326\/M23-2772"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s43856-023-00370-1","article-title":"The future landscape of large language models in medicine","volume":"3","author":"Clusmann","year":"2023","journal-title":"Commun. Med."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, H., Yerebakan, H., Shinagawa, Y., and Luo, Y. (2023). Enhancing Health Data Interoperability with Large Language Models: A FHIR Study. arXiv.","DOI":"10.1101\/2023.10.17.23297028"},{"key":"ref_60","first-page":"1134","article-title":"Clinical text summarization: Adapting large language models can outperform human experts","volume":"30","author":"Blankemeier","year":"2024","journal-title":"Res. Sq."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41746-023-00896-7","article-title":"Evaluating large language models on medical evidence summarization","volume":"6","author":"Tang","year":"2023","journal-title":"NPJ Digit. Med."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhou, W., Bitterman, D., Afshar, M., and Miller, T.A. (2023). Considerations for health care institutions training large language models on electronic health records. arXiv.","DOI":"10.2196\/preprints.57484"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"6074","DOI":"10.1109\/JBHI.2023.3316750","article-title":"Large ai models in health informatics: Applications, challenges, and the future","volume":"27","author":"Qiu","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_64","unstructured":"Wang, Y., Zhao, Y., and Petzold, L. (2023). Are Large Language Models Ready for Healthcare. A Comparative Study on Clinical Language Understanding. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1001\/archsurg.1981.01380140095028","article-title":"Grammar and Medicine","volume":"116","author":"Ficarra","year":"1981","journal-title":"Arch. Surg."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.ijmedinf.2016.05.005","article-title":"Incidence of speech recognition errors in the emergency department","volume":"93","author":"Goss","year":"2016","journal-title":"Int. J. Med Inform."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.jbi.2008.08.010","article-title":"Research electronic data capture (REDCap)\u2014A metadata-driven methodology and workflow process for providing translational research informatics support","volume":"42","author":"Harris","year":"2009","journal-title":"J. Biomed. Inform."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"W566","DOI":"10.1093\/nar\/gkz386","article-title":"Doc2Hpo: A web application for efficient and accurate HPO concept curation","volume":"47","author":"Liu","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_69","unstructured":"Powers, D.M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-023-06160-y","article-title":"Health system-scale language models are all-purpose prediction engines","volume":"619","author":"Jiang","year":"2023","journal-title":"Nature"},{"key":"ref_71","first-page":"CAT\u201323","article-title":"Scaling note quality assessment across an academic medical center with AI and GPT-4","volume":"5","author":"Feldman","year":"2024","journal-title":"NEJM Catal. Innov. Care Deliv."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1093\/jamia\/ocy186","article-title":"Can informatics innovation help mitigate clinician burnout?","volume":"26","author":"Bakken","year":"2019","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"216","DOI":"10.7326\/L18-0601","article-title":"Physician burnout in the electronic health record era","volume":"170","author":"Kapoor","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1055\/a-2203-3787","article-title":"Interventions to reduce electronic health record-related burnout: A systematic review","volume":"15","author":"Kang","year":"2024","journal-title":"Appl. Clin. Informatics"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s10916-025-02157-4","article-title":"Artificial Intelligence (AI)\u2013Powered Documentation Systems in Healthcare: A Systematic Review","volume":"49","author":"Bracken","year":"2025","journal-title":"J. Med Syst."},{"key":"ref_76","unstructured":"Henderson, J. (MedPage Today, 2025). Fewer Physicians Consider Leaving Medicine, Survey Finds, MedPage Today."},{"key":"ref_77","first-page":"269","article-title":"Bert-based ranking for biomedical entity normalization","volume":"2020","author":"Ji","year":"2020","journal-title":"AMIA Summits Transl. Sci. Proc."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/6\/446\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:41:06Z","timestamp":1760031666000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/6\/446"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,27]]},"references-count":77,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["info16060446"],"URL":"https:\/\/doi.org\/10.3390\/info16060446","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,27]]}}}