{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T12:44:43Z","timestamp":1791549883292,"version":"4.3.4"},"reference-count":29,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Nursing"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Patient safety investigation reports support organizational learning only when they are complete, usable, and sufficiently detailed. Conventional free-text reports are often inconsistent and may omit information needed for review and learning. Project NARRATE (Nursing AI-Refined for Accurate Transcription of Events) is a nursing-led ambient artificial intelligence workflow that uses prompts aligned with the World Health Organization Minimal Information Model for Patient Safety Incident Reporting and Learning Systems, Situation-Background-Assessment-Recommendation output, and visible missing-information cues to support structured supervisor reporting.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to compare the completeness and narrative quality of conventional and NARRATE-period supervisor investigation reports for falls and medication administration-related incidents.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We conducted a retrospective pre-post document-quality study at a tertiary academic medical center in Singapore. We reviewed 150 deidentified completed supervisor investigation reports: 75 conventional reports from June to August 2025 and 75 confirmed NARRATE reports from January to March 2026. NARRATE use was voluntary, and recorded use represented approximately 40% of eligible postimplementation reports. Two blinded reviewers rated reports using a World Health Organization (WHO)\u2013aligned completeness checklist and an adapted 8-domain Physician Documentation Quality Instrument (PDQI). Report-level comparisons were adjusted for repeated reports by the same supervisor using random-intercept linear mixed-effects models. A stratified 60-report plain-paragraph rerating examined whether visible structure influenced ratings.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n All 150 reports were analyzed. Unadjusted mean WHO total completeness was 11.81 (SD 3.39) for conventional reports and 13.61 (SD 2.54) for NARRATE reports; the unadjusted difference was 1.80 points, and the cluster-adjusted mean difference was 1.95 (95% CI 0.91\u20103.00;\n P<\/jats:italic>\n <.001). The adapted PDQI mean was 3.61 (SD 0.52) and 4.13 (SD 0.34), respectively; the unadjusted difference was 0.52 points, and the cluster-adjusted mean difference was 0.53 (95% CI 0.37\u20100.69;\n P<\/jats:italic>\n <.001). In the plain-paragraph sensitivity analysis, the completeness advantage remained (adjusted mean difference 1.70, 95% CI 0.27\u20103.14;\n P<\/jats:italic>\n =.02), as did the adapted PDQI mean advantage (adjusted mean difference 0.25, 95% CI 0.06\u20100.43;\n P<\/jats:italic>\n =.009). Explanation, organization, and comprehensibility remained significantly higher after deformatting; actions were borderline (\n P<\/jats:italic>\n =.05), and synthesis, internal consistency, and fairness\/balance were not statistically significant.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Among voluntary early adopters, NARRATE use was associated with more complete reports and higher adapted PDQI mean scores after accounting for supervisor clustering. Because recorded use represented approximately 40% of eligible postimplementation reports and users self-selected, findings may reflect adopter and supervisor characteristics. Results support the structured workflow as a whole, not any single AI component, and do not demonstrate downstream patient-safety effects. Confirmatory evaluation under broader adoption with a concurrent, reliably classified comparison group is needed.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/100775","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T09:10:06Z","timestamp":1784538606000},"page":"e100775-e100775","source":"Crossref","is-referenced-by-count":2,"title":["Nurse-Led Ambient AI Scribe for Patient Safety Incident Investigation Reports (Project NARRATE): Retrospective Pre-Post Comparative Document-Quality Study"],"prefix":"10.2196","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8096-9224","authenticated-orcid":false,"given":"Kai Yunn","family":"Teo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9088-0263","authenticated-orcid":false,"given":"Liwen","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9508-6275","authenticated-orcid":false,"given":"Kelly Chai Yuen","family":"Woh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9614-7012","authenticated-orcid":false,"given":"Shin Yuh","family":"Ang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4336-4436","authenticated-orcid":false,"given":"Jade Gaik Nai","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"issue":"7","key":"R1","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1097\/PTS.0000000000001151","article-title":"Multifaceted intervention to improve patient safety incident reporting in intensive care units","volume":"19","author":"Griffeth","journal-title":"J Patient Saf"},{"key":"R2","unstructured":"Patient safety incident reporting and learning systems: technical report and guidance. 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