{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T22:49:12Z","timestamp":1791586152108,"version":"4.3.4"},"reference-count":38,"publisher":"Ovid Technologies (Wolters Kluwer Health)","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"\n BACKGROUND:<\/jats:title>\n Postoperative delirium affects up to 50% of older adults with cognitive impairment, yet preoperative identification of at-risk patients remains limited. Artificial intelligence methods, such as natural language processing, can extract cognitive impairment signals from electronic health records and enable targeted clinical decision support interventions to promote adherence to perioperative best practices and reduce postoperative delirium. We aimed to evaluate the effectiveness of an artificial intelligence-driven non-interruptive clinical decision support intervention in reducing postoperative delirium by promoting adherence to best practices among anesthesia teams caring for surgical patients with cognitive impairment.<\/jats:p>\n <\/jats:sec>\n \n METHODS:<\/jats:title>\n We conducted a pragmatic randomized controlled trial embedded within routine clinical care at The Mount Sinai Hospital in New York City from June 20, 2023, to August 31, 2024, including patients with preoperative cognitive impairment identified immediately before surgery using a validated natural language processing algorithm, Mini-Mental State Examination scores, or diagnostic codes. Patients were randomized to an artificial intelligence-driven non-interruptive clinical decision support intervention within the electronic health record to promote adherence to best practices among anesthesia teams or routine perioperative care. The primary outcome was postoperative delirium, assessed with the 4 A\u2019s Test within seven days postoperatively. Secondary outcomes were adherence to 12 perioperative best practices. We used chi-square tests for primary and secondary outcome analyses.<\/jats:p>\n <\/jats:sec>\n \n RESULTS:<\/jats:title>\n \n Overall, 7294 patients were identified as having cognitive impairment immediately before surgery and were randomized to the intervention (n = 3624) or control (n = 3670) group. In total, 1255\/7294 (17.2%) patients had documented postoperative delirium assessments, of whom 426\/1255 (34%) developed this condition. Between intervention and control groups, there were no statistically significant differences in POD incidence (225\/641, 35.1% vs 201\/614, 32.7%,\n P =<\/jats:italic>\n .37) or best practice adherence (all\n P ><\/jats:italic>\n .05).\n <\/jats:p>\n <\/jats:sec>\n \n CONCLUSIONS:<\/jats:title>\n In this pragmatic randomized controlled trial, less than 20% of the sample had documented delirium assessments due to variability in routine POD screening practices, limiting our ability to conclude that artificial intelligence-driven clinical decision support was not effective in preventing postoperative delirium. However, based on our secondary outcome analyses, the intervention did not significantly change best practice adherence. Further work is needed to determine the best way to use these tools effectively.<\/jats:p>\n <\/jats:sec>","DOI":"10.1213\/ane.0000000000008318","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T22:00:23Z","timestamp":1791583223000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Driven Clinical Decision Support for Postoperative Delirium Prevention: A Pragmatic Randomized Controlled Trial"],"prefix":"10.1213","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3265-6667","authenticated-orcid":false,"given":"Danielle","family":"Scharp","sequence":"first","affiliation":[{"name":"Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, NY, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kerry","family":"Meyers","sequence":"additional","affiliation":[{"name":"Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, NY, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6319-4314","authenticated-orcid":false,"given":"Girish","family":"Nadkarni","sequence":"additional","affiliation":[{"name":"Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, NY, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Federman","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, NY, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2768-2323","authenticated-orcid":false,"given":"Catherine","family":"Sarkisian","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University California Los Angeles and Veterans Administration, Greater Los Angeles, Geriatrics Research Education and Clinical Center, Los Angeles, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6013-2684","authenticated-orcid":false,"given":"Matthew A.","family":"Levin","sequence":"additional","affiliation":[{"name":"Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, NY, New York"},{"name":"Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, NY, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Spencer","family":"Perry","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1945-1861","authenticated-orcid":false,"given":"Pooja","family":"Gownivaripally","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalia N.","family":"Egorova","sequence":"additional","affiliation":[{"name":"Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, NY, New York; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2671-6117","authenticated-orcid":false,"given":"Yuxia","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, NY, New York; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4099-3946","authenticated-orcid":false,"given":"Susana","family":"Vacas","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7456-6697","authenticated-orcid":false,"given":"Ira","family":"Hofer","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, NY, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"R1-20261009","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1097\/SLA.0000000000005077","article-title":"The incidence and cumulative risk of major surgery in older persons in the United States.","volume":"277","author":"Becher","year":"2023","journal-title":"Ann Surg"},{"key":"R2-20261009","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3233\/JAD-189004","article-title":"Recommendations for the nomenclature of cognitive change associated with Anaesthesia and surgery-20181.","volume":"66","author":"Evered","year":"2018","journal-title":"J Alzheimers Dis"},{"key":"R3-20261009","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1097\/ANA.0000000000000971","article-title":"Postoperative delirium and the older adult: untangling the confusion.","volume":"36","author":"Thedim","year":"2024","journal-title":"J Neurosurg Anesthesiol"},{"key":"R4-20261009","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1001\/jamasurg.2015.2606","article-title":"Effect of delirium and other major complications on outcomes after elective surgery in older adults.","volume":"150","author":"Gleason","year":"2015","journal-title":"JAMA Surg"},{"key":"R5-20261009","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1213\/ANE.0000000000006090","article-title":"Perioperative brain health in the older adult: a patient safety imperative.","volume":"135","author":"Vacas","year":"2022","journal-title":"Anesth Analg"},{"key":"R6-20261009","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1001\/jamasurg.2020.7260","article-title":"One-year medicare costs associated with delirium in older patients undergoing major elective surgery.","volume":"156","author":"Gou","year":"2021","journal-title":"JAMA Surg"},{"key":"R7-20261009","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1097\/ALN.0000000000001859","article-title":"Poor performance on a preoperative cognitive screening test predicts postoperative complications in older orthopedic surgical patients.","volume":"127","author":"Culley","year":"2017","journal-title":"Anesthesiology"},{"key":"R8-20261009","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1213\/ANE.0000000000007557","article-title":"Brain health screening in older surgical patients: a multicenter, retrospective, observational analysis and survey.","volume":"141","author":"Abess","year":"2025","journal-title":"Anesth Analg"},{"key":"R9-20261009","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.jaci.2019.12.897","article-title":"Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.","volume":"145","author":"Juhn","year":"2020","journal-title":"J Allergy Clin Immunol"},{"key":"R10-20261009","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1038\/s41390-022-02226-1","article-title":"Artificial intelligence-based clinical decision support in pediatrics.","volume":"93","author":"Ramgopal","year":"2023","journal-title":"Pediatr Res"},{"key":"R11-20261009","doi-asserted-by":"crossref","first-page":"e0123892","DOI":"10.1371\/journal.pone.0123892","article-title":"Association between intraoperative blood pressure and postoperative delirium in elderly hip fracture patients.","volume":"10","author":"Wang","year":"2015","journal-title":"PLoS One"},{"key":"R12-20261009","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1097\/ALN.0000000000000669","article-title":"Intraoperative tight glucose control using hyperinsulinemic normoglycemia increases delirium after cardiac surgery.","volume":"122","author":"Saager","year":"2015","journal-title":"Anesthesiology"},{"key":"R13-20261009","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1136\/bmj.326.7392.721","article-title":"Maintaining perioperative normothermia.","volume":"326","author":"Harper","year":"2003","journal-title":"BMJ"},{"key":"R14-20261009","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1016\/j.bja.2021.07.021","article-title":"Anaesthetic depth and delirium after major surgery: a randomised clinical trial.","volume":"127","author":"Evered","year":"2021","journal-title":"Br J Anaesth"},{"key":"R15-20261009","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.jamcollsurg.2012.06.017","article-title":"Optimal preoperative assessment of the geriatric surgical patient: a best practices guideline from the American College of Surgeons National Surgical Quality Improvement Program and the American Geriatrics Society.","volume":"215","author":"Chow","year":"2012","journal-title":"J Am Coll Surg"},{"key":"R16-20261009","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.jamcollsurg.2014.10.019","article-title":"Postoperative delirium in older adults: best practice statement from the American geriatrics society.","volume":"220","author":"Inouye","year":"2015","journal-title":"J Am Coll Surg"},{"key":"R17-20261009","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1097\/ALN.0000000000005172","article-title":"2025 American society of anesthesiologists practice advisory for perioperative care of older adults scheduled for inpatient surgery.","volume":"142","author":"Sieber","year":"2025","journal-title":"Anesthesiology"},{"key":"R18-20261009","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1016\/j.jamcollsurg.2015.12.026","article-title":"Optimal perioperative management of the geriatric patient: a best practices guideline from the american college of surgeons NSQIP and the American Geriatrics Society.","volume":"222","author":"Mohanty","year":"2016","journal-title":"J Am Coll Surg"},{"key":"R19-20261009","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13741-020-0136-9","article-title":"Adherence to recommended practices for perioperative anesthesia care for older adults among US anesthesiologists: results from the ASA Committee on Geriatric Anesthesia-Perioperative Brain Health Initiative ASA member survey.","volume":"9","author":"Deiner","year":"2020","journal-title":"Perioper Med (Lond)"},{"key":"R20-20261009","first-page":"906","article-title":"A systematic review on CDSS alert appropriateness.","volume":"270","author":"Olakotan","year":"2020","journal-title":"Stud Health Technol Inform"},{"key":"R21-20261009","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1111\/jgs.14744","article-title":"An automated approach to identifying patients with dementia using electronic medical records.","volume":"65","author":"Reuben","year":"2017","journal-title":"J Am Geriatr Soc"},{"key":"R22-20261009","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1212\/WNL.0b013e3182684707","article-title":"Recommendations for optimal ICD codes to study neurologic conditions: a systematic review.","volume":"79","author":"St Germaine-Smith","year":"2012","journal-title":"Neurology"},{"key":"R23-20261009","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1001\/jama.2025.4347","article-title":"CONSORT 2025 statement: updated guideline for reporting randomized trials.","volume":"333","author":"Hopewell","year":"2025","journal-title":"JAMA"},{"key":"R24-20261009","doi-asserted-by":"crossref","first-page":"e31176","DOI":"10.1097\/MD.0000000000031176","article-title":"Risk factor stratification for postoperative delirium: a retrospective database study.","volume":"101","author":"Vacas","year":"2022","journal-title":"Medicine (Baltim)"},{"key":"R25-20261009","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.1213\/ANE.0000000000001201","article-title":"A systematic approach to creation of a perioperative data warehouse.","volume":"122","author":"Hofer","year":"2016","journal-title":"Anesth Analg"},{"key":"R26-20261009","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1111\/anae.14682","article-title":"Screening for delirium after surgery: validation of the 4 A\u2019s test (4AT) in the post-Anaesthesia care unit.","volume":"74","author":"Saller","year":"2019","journal-title":"Anaesthesia"},{"key":"R27-20261009","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1093\/jamia\/ocac023","article-title":"Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.","volume":"29","author":"Hobensack","year":"2022","journal-title":"J Am Med Inform Assoc"},{"key":"R28-20261009","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1055\/s-0040-1701986","article-title":"Clinical decision support and implications for the clinician burnout crisis.","volume":"29","author":"Jankovic","year":"2020","journal-title":"Yearb Med Inform"},{"key":"R29-20261009","doi-asserted-by":"crossref","first-page":"e261515","DOI":"10.1001\/jamanetworkopen.2026.1515","article-title":"Perioperative best practices and delirium in patients with cognitive impairment.","volume":"9","author":"Scharp","year":"2026","journal-title":"JAMA Netw Open"},{"key":"R30-20261009","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1097\/ALN.0000000000001516","article-title":"A perioperative systems design to improve intraoperative glucose monitoring is associated with a reduction in surgical site infections in a diabetic patient population.","volume":"126","author":"Ehrenfeld","year":"2017","journal-title":"Anesthesiology"},{"key":"R31-20261009","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10877-015-9718-3","article-title":"Intraoperative blood glucose management: impact of a real-time decision support system on adherence to institutional protocol.","volume":"30","author":"Nair","year":"2016","journal-title":"J Clin Monit Comput"},{"key":"R32-20261009","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s10877-012-9425-2","article-title":"Near real-time notification of gaps in cuff blood pressure recordings for improved patient monitoring.","volume":"27","author":"Nair","year":"2013","journal-title":"J Clin Monit Comput"},{"key":"R33-20261009","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1213\/ANE.0000000000000027","article-title":"Anesthesia information management system-based near real-time decision support to manage intraoperative hypotension and hypertension.","volume":"118","author":"Nair","year":"2014","journal-title":"Anesth Analg"},{"key":"R34-20261009","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1097\/ALN.0b013e3182829de0","article-title":"Reducing wastage of inhalation anesthetics using real-time decision support to notify of excessive fresh gas flow.","volume":"118","author":"Nair","year":"2013","journal-title":"Anesthesiology"},{"key":"R35-20261009","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1097\/ALN.0000000000001296","article-title":"Effectiveness of an electronic alert for hypotension and low Bispectral index on 90-day postoperative mortality: a prospective, randomized trial.","volume":"125","author":"McCormick","year":"2016","journal-title":"Anesthesiology"},{"key":"R36-20261009","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1097\/ALN.0000000000000699","article-title":"Behavioral modification of intraoperative hyperglycemia management with a novel real-time audiovisual monitor.","volume":"123","author":"Sathishkumar","year":"2015","journal-title":"Anesthesiology"},{"key":"R37-20261009","doi-asserted-by":"crossref","first-page":"106220","DOI":"10.1016\/j.ijmedinf.2025.106220","article-title":"Effectiveness of computerized decision support systems linked to electronic health records: an updated systematic review with meta-analysis.","volume":"207","author":"Biffi","year":"2026","journal-title":"Int J Med Inform"},{"key":"R38-20261009","doi-asserted-by":"crossref","first-page":"m3216","DOI":"10.1136\/bmj.m3216","article-title":"Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials.","volume":"370","author":"Kwan","year":"2020","journal-title":"BMJ"}],"container-title":["Anesthesia & Analgesia"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.1213\/ANE.0000000000008318","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T22:00:28Z","timestamp":1791583228000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.1213\/ANE.0000000000008318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,9]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1213\/ane.0000000000008318","relation":{},"ISSN":["0003-2999","1526-7598"],"issn-type":[{"value":"0003-2999","type":"print"},{"value":"1526-7598","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,9]]}}}