{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T19:44:57Z","timestamp":1782675897442,"version":"3.54.5"},"reference-count":31,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T00:00:00Z","timestamp":1782604800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T00:00:00Z","timestamp":1782604800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["arthroscopyjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthroscopy"],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate the readability, understandability, and actionability of current orthopaedic discharge instructions and assess the effectiveness of generative artificial intelligence (AI) in addressing these areas of improvement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Orthopaedic discharge instruction documents were collected from a large academic hospital from March to May 2025. Subcategories frequently associated with postoperative inquiries\u2014activities, complications and follow\u2010up, medications, narcotics, pain management, physical therapy, and wound care\u2014were extracted for analysis. ChatGPT\u20104 was used to generate improved discharge instructions using structured prompting strategies. Readability and text complexity measures were assessed using 5 validated readability tests whereas understandability and actionability were assessed using a modified Patient Education Materials Assessment Tool (PEMAT). Paired t\u2010tests were used to compare scores between original and ChatGPT\u2010rewritten content whereas comparisons across subcategories were achieved with independent samples t\u2010tests (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u2009.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Fifty\u2010two subcategory excerpts were identified across twelve distinct discharge instructions. ChatGPT significantly reduced the average reading grade level of discharge instructions from 9.8 to 7.5 across all content categories (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u2009.001). The wound care subcategory showed the lowest reading grade level before and after ChatGPT. The narcotics subcategory showed the highest reading grade level before ChatGPT, whereas the medications and physical therapy subcategories showed the highest reading grade level after ChatGPT. Understandability and actionability scores improved from 63.6% to 87.9% (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u2009.001) and from 44.4% to 73.6%, respectively (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u2009.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Generative AI significantly improves the readability, understandability, and actionability of orthopaedic discharge instructions; however, not all documents achieved the desired sixth\u2010grade reading level. Subcategory analysis revealed opportunities for targeted interventions to improve patient education in specific topics, such as narcotics, medication, or physical therapy\u2010related instructions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Relevance<\/jats:title>\n                    <jats:p>This study describes a cost\u2010effective, scalable AI intervention for improving orthopaedic discharge instructions which may improve clinical outcomes, reduce healthcare burden, and support patients\u2019 management of postoperative recovery.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/arj.70400","type":"journal-article","created":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T19:19:04Z","timestamp":1782674344000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Natural Language Processing Improves Postoperative Discharge Instructions"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0912-3472","authenticated-orcid":false,"given":"Carrie N.","family":"Reaver","sequence":"first","affiliation":[{"name":"Department of Orthopedic Surgery, Washington University in St. Louis  St. Louis Missouri U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohit","family":"Siddabattula","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Washington University in St. Louis  St. Louis Missouri U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6356-9830","authenticated-orcid":false,"given":"Daniel E.","family":"Pereira","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Washington University in St. Louis  St. Louis Missouri 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