{"doi":"10.1001/jama.2025.26967","title":"Electronic Health Record Intervention and Deprescribing for Older Adults","abstract":"<jats:sec>\n                    <jats:title>Importance</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Design, Setting, and Participants</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Interventions</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Outcomes and Measures</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions and Relevance</jats:title>\n                    <jats:p>These results support use of EHR tools designed using behavioral science principles to significantly increase rates of deprescribing potentially inappropriate medications used by older adults.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration</jats:title>\n                    <jats:p>\n                      ClinicalTrials.gov Identifier:\n                      <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov/study/NCT05538065\">NCT005538065</jats:ext-link>\n                    </jats:p>\n                  </jats:sec>","journal":"JAMA","year":2026,"id":622562,"datarank":0.29188652235829704,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.0,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1608651,"name":"Meekang Sung","orcid":null,"position":1,"is_corresponding":false},{"id":45776,"name":"Robert J. Glynn","orcid":"0000-0002-0697-8996","position":2,"is_corresponding":false},{"id":1608652,"name":"Punam A. Keller","orcid":null,"position":3,"is_corresponding":false},{"id":745194,"name":"Ted Robertson","orcid":"0000-0003-4427-062X","position":4,"is_corresponding":false},{"id":1608654,"name":"Dae H. Kim","orcid":null,"position":5,"is_corresponding":false},{"id":789783,"name":"Gauri Bhatkhande","orcid":"0000-0001-7972-9598","position":6,"is_corresponding":false},{"id":1411804,"name":"Katharina Tabea Jungo","orcid":"0000-0002-1782-1345","position":7,"is_corresponding":false},{"id":716576,"name":"Nancy Haff","orcid":"0000-0001-8156-7137","position":8,"is_corresponding":false},{"id":1608656,"name":"Kaitlin E. Hanken","orcid":null,"position":9,"is_corresponding":false},{"id":498345,"name":"Thomas Isaac","orcid":null,"position":10,"is_corresponding":false},{"id":270945,"name":"Niteesh K. Choudhry","orcid":"0000-0001-7719-2248","position":11,"is_corresponding":false},{"id":395612,"name":"Julie C. Lauffenburger","orcid":"0000-0002-4940-4140","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Electronic Health Record Intervention and Deprescribing for Older Adults","abstract":"<jats:sec>\n                    <jats:title>Importance</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Design, Setting, and Participants</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Interventions</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Outcomes and Measures</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>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.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions and Relevance</jats:title>\n                    <jats:p>These results support use of EHR tools designed using behavioral science principles to significantly increase rates of deprescribing potentially inappropriate medications used by older adults.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration</jats:title>\n                    <jats:p>\n                      ClinicalTrials.gov Identifier:\n                      <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov/study/NCT05538065\">NCT005538065</jats:ext-link>\n                    </jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":1.791759469228055,"endowment":1.791759469228055,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41609788","pmcid":null,"openalex_id":"https://openalex.org/W7126086642","authors":[],"funders":[{"funder_name":"NIA NIH HHS","grant_id":"R33 AG057388","title":null}],"total_grants":1,"fwci":145.754,"citation_percentile":0.99938408,"influential_citations":0,"citation_trend":[{"year":2026,"count":5}],"oa_status":"green","license":null,"oa_locations":[{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12856743/","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12856743/","host_type":"repository"},{"url":"https://jamanetwork.com/journals/jama/articlepdf/2844545/jama_lauffenburger_2026_oi_250125_1773433847.24411.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1001/jama.2025.26967","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41609788","host_type":"repository"}],"fields_of_study":["Electronic Health Records Systems","Pharmaceutical Practices and Patient Outcomes","Nursing Diagnosis and Documentation","Aged","Female","Humans","Male","Deprescriptions","Electronic Health Records","Inappropriate Prescribing","Potentially Inappropriate Medication List","Follow-Up Studies","Aged, 80 and over","Physicians, Primary Care"],"mesh_terms":["Potentially Inappropriate Medication List","Deprescriptions","Aged","Aged, 80 and over","Female","Follow-Up Studies","Humans","Male","Electronic Health Records","Inappropriate Prescribing","Physicians, Primary Care"],"keywords":["Deprescribing","Electronic health record","Intervention (counseling)","Health records","Polypharmacy","Medical record","MEDLINE"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T20:06:26.181270Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}