{"title":"The Death of the Consult Note.","authors":"Benjamin Chin-Yee","doi":"10.1001/jama.2025.26848","DOIUrl":"https://doi.org/10.1001/jama.2025.26848","url":null,"abstract":"","PeriodicalId":518009,"journal":{"name":"JAMA","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julie C. Lauffenburger, Meekang Sung, Robert J. Glynn, Punam A. Keller, Ted Robertson, Dae H. Kim, Gauri Bhatkhande, Katharina Tabea Jungo, Nancy Haff, Kaitlin E. Hanken, Thomas Isaac, Niteesh K. Choudhry
Importance Potentially inappropriate medications are commonly overprescribed to older adults. Although electronic health record (EHR)–based tools can increase use of evidence-based medications, their ability to reduce prescription of potentially inappropriate medications remains unclear. Objective To test the effects of 2 EHR interventions, designed using behavioral science techniques, on the deprescribing of potentially inappropriate medications compared with usual care in older patients. Design, Setting, and Participants In this 3-group parallel randomized clinical trial, 201 primary care physicians (PCPs) in an academic center in Massachusetts were cluster-randomized in November 2022. Follow-up ended March 15, 2024. The intervention focused on patients of randomized PCPs who were 65 years or older, had a PCP visit between November 10, 2022, and March 15, 2024, and were prescribed at least 90 pills of benzodiazepines, nonbenzodiazepine sedative hypnotics, or at least 2 anticholinergic medications in the past 180 days. Interventions PCPs were randomized to usual care (no intervention) or to 1 of 2 sequential EHR interventions: a precommitment intervention, in which an EHR message was sent to the physician during the first patient visit asking the PCP to initiate deprescribing discussions with a second reminder EHR message at the patient’s second visit encouraging deprescribing; and a boostering intervention, in which PCPs received a notification encouraging deprescribing at the first patient visit and an in-basket reminder 4 weeks later. Main Outcomes and Measures The primary outcome was deprescribing at least 1 medication on or after the first patient visit though the end of follow-up. Deprescribing was defined as physician-directed discontinuation or medication tapering assessed at the patient level using EHR data. Generalized estimating equations with a log link and binary-distributed errors were used for analyses, adjusting for clustering and multiple testing using Holm-Bonferroni corrections. Results Of 1146 participants (mean age, 73.6 years [SD, 6.4]; 69.7% female, mean follow-up, 289.9 days), 373 (32.5%) had at least 1 medication deprescribed: 145 (36.8%) in the precommitment group, 122 (34.3%) in the boostering group, and 106 (26.8%) in usual care. Compared with usual care, deprescribing was 40% more likely (relative risk [RR], 1.40; 95% CI, 1.14-1.73; absolute difference, 10.4%) in the precommitment group and 26% more likely (RR, 1.26; 95% CI, 1.01-1.57; absolute difference, 6.5%) in the boostering group. No serious adverse events were reported through the adverse event reporting system. Death rates based on manual chart review were 1.4% in the precommitment group, 3.9% in the boostering group, and 1.8% in the usual care group. Conclusions and Relevance These results support use of EHR tools designed using behavioral science principles to significantly increase rates of deprescribing potentially inappropriate medications used by older
{"title":"Electronic Health Record Intervention and Deprescribing for Older Adults","authors":"Julie C. Lauffenburger, Meekang Sung, Robert J. Glynn, Punam A. Keller, Ted Robertson, Dae H. Kim, Gauri Bhatkhande, Katharina Tabea Jungo, Nancy Haff, Kaitlin E. Hanken, Thomas Isaac, Niteesh K. Choudhry","doi":"10.1001/jama.2025.26967","DOIUrl":"https://doi.org/10.1001/jama.2025.26967","url":null,"abstract":"Importance Potentially inappropriate medications are commonly overprescribed to older adults. Although electronic health record (EHR)–based tools can increase use of evidence-based medications, their ability to reduce prescription of potentially inappropriate medications remains unclear. Objective To test the effects of 2 EHR interventions, designed using behavioral science techniques, on the deprescribing of potentially inappropriate medications compared with usual care in older patients. Design, Setting, and Participants In this 3-group parallel randomized clinical trial, 201 primary care physicians (PCPs) in an academic center in Massachusetts were cluster-randomized in November 2022. Follow-up ended March 15, 2024. The intervention focused on patients of randomized PCPs who were 65 years or older, had a PCP visit between November 10, 2022, and March 15, 2024, and were prescribed at least 90 pills of benzodiazepines, nonbenzodiazepine sedative hypnotics, or at least 2 anticholinergic medications in the past 180 days. Interventions PCPs were randomized to usual care (no intervention) or to 1 of 2 sequential EHR interventions: a precommitment intervention, in which an EHR message was sent to the physician during the first patient visit asking the PCP to initiate deprescribing discussions with a second reminder EHR message at the patient’s second visit encouraging deprescribing; and a boostering intervention, in which PCPs received a notification encouraging deprescribing at the first patient visit and an in-basket reminder 4 weeks later. Main Outcomes and Measures The primary outcome was deprescribing at least 1 medication on or after the first patient visit though the end of follow-up. Deprescribing was defined as physician-directed discontinuation or medication tapering assessed at the patient level using EHR data. Generalized estimating equations with a log link and binary-distributed errors were used for analyses, adjusting for clustering and multiple testing using Holm-Bonferroni corrections. Results Of 1146 participants (mean age, 73.6 years [SD, 6.4]; 69.7% female, mean follow-up, 289.9 days), 373 (32.5%) had at least 1 medication deprescribed: 145 (36.8%) in the precommitment group, 122 (34.3%) in the boostering group, and 106 (26.8%) in usual care. Compared with usual care, deprescribing was 40% more likely (relative risk [RR], 1.40; 95% CI, 1.14-1.73; absolute difference, 10.4%) in the precommitment group and 26% more likely (RR, 1.26; 95% CI, 1.01-1.57; absolute difference, 6.5%) in the boostering group. No serious adverse events were reported through the adverse event reporting system. Death rates based on manual chart review were 1.4% in the precommitment group, 3.9% in the boostering group, and 1.8% in the usual care group. Conclusions and Relevance These results support use of EHR tools designed using behavioral science principles to significantly increase rates of deprescribing potentially inappropriate medications used by older ","PeriodicalId":518009,"journal":{"name":"JAMA","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
JAMA+ AI Editor in Chief Roy Perlis, MD, MSc, conducted an interview with ChatGPT about the history of chatbots and their clinical applications, for JAMA+ AI Conversations.
{"title":"What Can 50-Year-Old Chatbots Teach Us About Clinical Applications of AI?","authors":"Roy Perlis","doi":"10.1001/jama.2025.26751","DOIUrl":"https://doi.org/10.1001/jama.2025.26751","url":null,"abstract":"JAMA+ AI Editor in Chief Roy Perlis, MD, MSc, conducted an interview with ChatGPT about the history of chatbots and their clinical applications, for JAMA+ AI Conversations.","PeriodicalId":518009,"journal":{"name":"JAMA","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luc P Brion,Waldemar A Carlo,Namasivayam Ambalavanan
{"title":"Early Intratracheal Budesonide to Reduce Bronchopulmonary Dysplasia in Extremely Preterm Infants-Reply.","authors":"Luc P Brion,Waldemar A Carlo,Namasivayam Ambalavanan","doi":"10.1001/jama.2025.23799","DOIUrl":"https://doi.org/10.1001/jama.2025.23799","url":null,"abstract":"","PeriodicalId":518009,"journal":{"name":"JAMA","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Electronic Health Record Intervention and Deprescribing for Older Adults: Research Summary.","authors":"","doi":"10.1001/jama.2025.26996","DOIUrl":"https://doi.org/10.1001/jama.2025.26996","url":null,"abstract":"","PeriodicalId":518009,"journal":{"name":"JAMA","volume":"66 1","pages":"e2526996"},"PeriodicalIF":0.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David A. Simon, Michael K. Paasche-Orlow, Hooman Noorchashm
This Viewpoint proposes that the US Food and Drug Administration create a patent database for medical devices, the Yellow Book, similar to the Orange Book’s database of drug-related patents.
{"title":"A Yellow Book for Medical Devices—A Proposal for Public Health","authors":"David A. Simon, Michael K. Paasche-Orlow, Hooman Noorchashm","doi":"10.1001/jama.2025.27042","DOIUrl":"https://doi.org/10.1001/jama.2025.27042","url":null,"abstract":"This Viewpoint proposes that the US Food and Drug Administration create a patent database for medical devices, the Yellow Book, similar to the Orange Book’s database of drug-related patents.","PeriodicalId":518009,"journal":{"name":"JAMA","volume":"117 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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