{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T20:04:12Z","timestamp":1779134652559,"version":"3.51.4"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Prior authorization (PA) is a major source of administrative burden, treatment delay, and clinician burnout. Artificial intelligence (AI), particularly large language models (LLMs), is increasingly used to assist with clinical documentation, yet its reliability for payer-facing administrative tasks remains uncertain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the quality of PA letters drafted by ChatGPT-5 for commonly used medications requiring PA in nephrology. Quality was evaluated based on correctness and strength of clinical reasoning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We created a single standardized prompt and applied it across 29 nephrology scenarios to generate PA letters. Each PA letter was reviewed against four criteria: 1) absence of false statements or hallucinations, 2) correctness of ICD-10 coding, 3) presence and validity of citations, and 4) clinical reasoning, rated on a 4-point Likert scale (illogical, weak, adequate and strong). FDA drug labels, KDIGO guidelines and related randomized controlled trials were used as reference standards.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Out of 29 letters, one letter (3.5%) contained false statements mentioning an irrelevant clinical trial. The ICD-10 diagnosis code was correct in 23 letters (79.3%), most errors were related to chronic kidney disease (CKD) staging or internal diagnostic inconsistencies. 27 letters (93.1%) cited valid references, with one letter citing an incorrect trial and another one citing a correct KDIGO guideline with inaccessible link. Twenty-six letters (89.7%) demonstrated strong clinical reasoning, supported by guideline-oriented or FDA label\u2013aligned justification. The remaining 3 letters were rated as adequate reasoning. The main areas for improvement involved citing relevant references and emphasizing special considerations, for example Risk Evaluation and Mitigation Strategy (REMS) compliance for eculizumab.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ChatGPT-5 can generate clinically coherent PA drafts for nephrology medications, but limitations in coding precision and citation reliability persist. With appropriate oversight, AI-assisted documentation may reduce administrative burden while maintaining safety and accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1767648","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T06:49:46Z","timestamp":1772520586000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Quality assessment of large language model\u2013generated prior authorization letters in nephrology"],"prefix":"10.3389","volume":"8","author":[{"given":"Noppawit","family":"Aiumtrakul","sequence":"first","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic","place":["Rochester, MN, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charat","family":"Thongprayoon","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic","place":["Rochester, MN, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chutawat","family":"Kookanok","sequence":"additional","affiliation":[{"name":"Department of Medicine, One Brooklyn Health, Interfaith Medical Center","place":["Brooklyn, NY, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Methavee","family":"Poochanasri","sequence":"additional","affiliation":[{"name":"Department of Medicine, Bhumibol Adulyadej Hospital","place":["Bangkok, Thailand"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kitinan","family":"Phichedwanichskul","sequence":"additional","affiliation":[{"name":"Phramongkutklao Hospital","place":["Bangkok, Thailand"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wisit","family":"Cheungpasitporn","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic","place":["Rochester, MN, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"B1","year":"2024"},{"key":"B2","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1001\/jamaneurol.2025.4560","article-title":"Barriers and consequences of prior authorization for neurologic medications: a scoping review","volume":"83","author":"Gotlieb","year":"2025","journal-title":"JAMA Neurol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e2338182","DOI":"10.1001\/jamanetworkopen.2023.38182","article-title":"The patient experience of prior authorization for cancer care","volume":"6","author":"Chino","year":"2023","journal-title":"JAMA Netw Open"},{"key":"B4","doi-asserted-by":"publisher","first-page":"e233804","DOI":"10.1001\/jamahealthforum.2023.3804","article-title":"Pharmacy benefit managers: history, business practices, economics, and policy","volume":"4","author":"Mattingly","year":"2023","journal-title":"JAMA Health Forum"},{"key":"B5","doi-asserted-by":"publisher","first-page":"e210859","DOI":"10.1001\/jamahealthforum.2021.0859","article-title":"Measuring the scope of prior authorization policies: applying private insurer rules to medicare part B","volume":"2","author":"Schwartz","year":"2021","journal-title":"JAMA Health Forum"},{"key":"B6","doi-asserted-by":"publisher","first-page":"641","DOI":"10.18553\/jmcp.2019.19069","article-title":"Prior authorization and utilization management concepts in managed care pharmacy","volume":"25","year":"2019","journal-title":"J Manag Care Spec Pharm"},{"key":"B7","year":"2024"},{"key":"B8","year":"2023"},{"key":"B9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.amjmed.2025.08.018","article-title":"Adverse effects of health plan prior authorization on clinical effectiveness and patient outcomes: a systematic review","volume":"139","author":"Murphy","year":"2026","journal-title":"Am J Med"},{"key":"B10","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1056\/NEJMra2204673","article-title":"Artificial intelligence in U.S. health care delivery","volume":"389","author":"Sahni","year":"2023","journal-title":"N Engl J Med"},{"key":"B11","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1001\/jama.2023.25054","article-title":"Will generative artificial intelligence deliver on its promise in health care?","volume":"331","author":"Wachter","year":"2024","journal-title":"Jama"},{"key":"B12","doi-asserted-by":"publisher","first-page":"e2549093","DOI":"10.1001\/jamanetworkopen.2025.49093","article-title":"Integrating prior authorization into clinical workflows for care access and practitioner experience","volume":"8","author":"Chen","year":"2025","journal-title":"JAMA Network Open"},{"key":"B13","doi-asserted-by":"publisher","first-page":"e69678","DOI":"10.2196\/69678","article-title":"Trust in artificial intelligence-based clinical decision support systems among health care workers. 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