Background: Prescription narcotic overdoses and abuse have reached alarming numbers. To address this epidemic, integrated clinical decision support within the electronic medical record (EMR) to impact prescribing behavior was developed and tested.
Methods: A multidisciplinary Expert Panel identified risk factors for misuse, abuse, or diversion of opioids or benzodiazepines through literature reviews and consensus building for inclusion in a rule within the EMR. We ran the rule "silently" to test the rule and collect baseline data.
Results: Five criteria were programmed to trigger the alert; based on data collected during a "silent" phase, thresholds for triggers were modified. The alert would have fired in 21.75 % of prescribing encounters (1.30 % of all encounters; n = 9998), suggesting the alert will have a low prescriber burden yet capture a significant number of at-risk patients.
Conclusions: While the use of the EMR to provide clinical decision support is not new, utilizing it to develop and test an intervention is novel. We successfully built an alert system to address narcotic prescribing by providing critical, objective information at the point of care. The silent phase data were useful to appropriately tune the alert and obtain support for widespread implementation. Future healthcare initiatives can utilize similar methodology to collect data prospectively via the electronic medical record to inform the development, delivery, and evaluation of interventions.
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http://dx.doi.org/10.1186/s12911-016-0352-x | DOI Listing |
J Shoulder Elbow Surg
January 2025
Department of Orthopaedics; University Hospital Cleveland Medical Center, Cleveland, OH, USA.
Background: Recurrent shoulder dislocations often lead to multiple encounters for reduction and eventual surgical stabilization, both of which involve exposure to opioids and potentially increase the risk of chronic opioid exposure. The purpose of our study was to characterize shoulder instability and compare pre- and post-reduction opioid usage in singular dislocators (SD) and recurrent dislocators (RD).
Methods: This retrospective study was performed at a single academic institution using a prospective database.
Curr Pain Headache Rep
January 2025
Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.
Purpose Of Review: Artificial intelligence (AI) offers a new frontier for aiding in the management of both acute and chronic pain, which may potentially transform opioid prescribing practices and addiction prevention strategies. In this review paper, not only do we discuss some of the current literature around predicting various opioid-related outcomes, but we also briefly point out the next steps to improve trustworthiness of these AI models prior to real-time use in clinical workflow.
Recent Findings: Machine learning-based predictive models for identifying risk for persistent postoperative opioid use have been reported for spine surgery, knee arthroplasty, hip arthroplasty, arthroscopic joint surgery, outpatient surgery, and mixed surgical populations.
J Dermatolog Treat
December 2025
Hospital for Skin Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, Jiangsu, China.
Background: Hailey-Hailey disease (HHD), a genetic blistering disease, is caused by a mutation in a calcium transporter protein in the Golgi apparatus encoded by the gene. Clinically, HHD is characterized by flaccid vesicles, blisters, erosions, fissures, and maceration mainly in intertriginous regions. Some patients remain refractory to conventional treatments.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
Importance: During buprenorphine treatment for opioid use disorder (OUD), risk factors for opioid relapse or treatment dropout include comorbid substance use disorder, anxiety, or residual opioid craving. There is a need for a well-powered trial to evaluate virtually delivered groups, including both mindfulness and evidence-based approaches, to address these comorbidities during buprenorphine treatment.
Objective: To compare the effects of the Mindful Recovery Opioid Use Disorder Care Continuum (M-ROCC) vs active control among adults receiving buprenorphine for OUD.
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