{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T13:20:06Z","timestamp":1788441606152,"version":"build-2803163510"},"reference-count":71,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Coprescribing naloxone with opioid analgesics is a Centers for Disease Control and Prevention (CDC) best practice to mitigate the risk of fatal opioid overdose, yet coprescription by emergency medicine clinicians is rare, occurring less than 5% of the time it is indicated. Clinical decision support (CDS) has been associated with increased naloxone prescribing; however, key CDS design characteristics and pragmatic outcome measures necessary to understand replicability and effectiveness have not been reported.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to rigorously evaluate and quantify the impact of CDS designed to improve emergency department (ED) naloxone coprescribing. We hypothesized CDS would increase naloxone coprescribing and the number of naloxone prescriptions filled by patients discharged from EDs in a large health care system.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Following user-centered design principles, we designed and implemented a fully automated, interruptive, electronic health record\u2013based CDS to nudge clinicians to coprescribe naloxone with high-risk opioid prescriptions. \u201cHigh-risk\u201d opioid prescriptions were defined as any opioid analgesic prescription \u226590 total morphine milligram equivalents per day or for patients with a prior diagnosis of opioid use disorder or opioid overdose. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to evaluate pragmatic CDS outcomes of reach, effectiveness, adoption, implementation, and maintenance. Effectiveness was the primary outcome of interest and was assessed by (1) constructing a Bayesian structural time-series model of the number of ED visits with naloxone coprescriptions before and after CDS implementation and (2) calculating the percentage of naloxone prescriptions associated with CDS that were filled at an outpatient pharmacy. Mann-Kendall tests were used to evaluate longitudinal trends in CDS adoption. All outcomes were analyzed in R (version 4.2.2; R Core Team).<\/jats:p>\n <\/jats:sec>\n \n Implementation (Results)<\/jats:title>\n \n Between November 2019 and July 2023, there were 1,994,994 ED visits. CDS reached clinicians in 0.83% (16,566\/1,994,994) of all visits and 15.99% (16,566\/103,606) of ED visits where an opioid was prescribed at discharge. Clinicians adopted CDS, coprescribing naloxone in 34.36% (6613\/19,246) of alerts. CDS was effective, increasing naloxone coprescribing from baseline by 18.1 (95% CI 17.9\u201018.3) coprescriptions per week or 2,327% (95% CI 3390\u20103490). Patients filled 43.80% (1989\/4541) of naloxone coprescriptions. The CDS was implemented simultaneously at every ED and no adaptations were made to CDS postimplementation. CDS was maintained beyond the study period and maintained its effect, with adoption increasing over time (\u03c4=0.454;\n P<\/jats:italic>\n <.001).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Our findings advance the evidence that electronic health record\u2013based CDS increases the number of naloxone coprescriptions and improves the distribution of naloxone. Our time series analysis controls for secular trends and strongly suggests that minimally interruptive CDS significantly improves process outcomes.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/58276","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:37:33Z","timestamp":1726043853000},"page":"e58276-e58276","source":"Crossref","is-referenced-by-count":12,"title":["Clinical Decision Support to Increase Emergency Department Naloxone Coprescribing: Implementation Report"],"prefix":"10.2196","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6526-0765","authenticated-orcid":false,"given":"Stuart W","family":"Sommers","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5555-5714","authenticated-orcid":false,"given":"Heather J","family":"Tolle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2041-7404","authenticated-orcid":false,"given":"Katy E","family":"Trinkley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6408-0458","authenticated-orcid":false,"given":"Christine G","family":"Johnston","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3057-0878","authenticated-orcid":false,"given":"Caitlin L","family":"Dietsche","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0872-4658","authenticated-orcid":false,"given":"Stephanie V","family":"Eldred","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4113-1985","authenticated-orcid":false,"given":"Abraham T","family":"Wick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3396-6907","authenticated-orcid":false,"given":"Jason A","family":"Hoppe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2024,11,6]]},"reference":[{"key":"R1","unstructured":"U.S. overdose deaths decrease in 2023, first time since 2018. 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