Publications

INTRODUCTION: Although drug shortages for outpatient chronic conditions commonly occur, population-level data on how they impact patients' ability to refill prescriptions is scarce. We sought to identify distinct patterns of refill adherence following drug shortages and patient- and prescription-level factors associated with adherence trajectories reflecting potential shortage-related treatment disruption.

METHODS: We retrospectively analyzed panel data assembled from 2017 to 2020 Veterans Health Administration (VHA) electronic health record data. Patients were included if they were baseline users of medications subject to a shortage within VHA. Group-based trajectory modeling was applied to users' monthly proportion of days covered (PDC) values from 6-months before to 6-months after the reported drug supply chain disruption. Patient demographics and medication characteristics were compared between identified trajectory groups using multivariable logistic regression.

RESULTS: Among 1.5 million episodes of medication use (representing 1.3 million unique Veterans) for 29 medications in shortage in VHA, 6.3% were for female patients and the mean age was 66.4 ± 12.8 years. A 4-group trajectory model had the best fit: High Adherence (69.2% of observations), Moderate Adherence (14.1%), Potential Shortage-Related Disruption (8.5%), and Pre-Shortage Disruption (8.3%). Drug characteristics (drug class, number of manufacturers) were more strongly associated than patient characteristics with having Potential Shortage-Related Treatment Disruption vs. High Adherence.

CONCLUSIONS: We identified 4 trajectories of refill adherence for medications subject to VHA drug shortages, with 8.5% of users of affected drugs exhibiting a trajectory consistent with shortage-related treatment disruption. Drug characteristics may modify whether drug shortages lead to treatment disruption in VHA.

BACKGROUND: Alert fatigue is defined as alert dismissals due to excessive or irrelevant alerts and is frequently cited as a barrier to clinical decision support system use and impact. However, the criteria for determining the presence or absence of alert fatigue are poorly defined. The objective of this systematic review of systematic reviews was to identify operationalized definitions and measures of alert fatigue or alert-related metrics.

METHODS: Systematic reviews reporting at least one alert-related metric or measure/operationalization of alert fatigue for physician-directed electronic alerts were included. The Cochrane Library, Embase, and PubMed were searched from database start to 2024. The Revised Assessment of Multiple Systematic Reviews was used to assess study quality and risk of bias. Data were synthesized narratively and with descriptive statistics.

RESULTS: A total of 22 studies were included in the review. Studies reported between 1 and 11 alert metrics. Studies were most often of medium quality. Reporting of primary study characteristics was frequently judged to be insufficient. Only one article reported an operational definition of alert fatigue. The most common alert metrics were quantity, override rate, and acceptance rate.

DISCUSSION: Alert fatigue measurement methods are not clearly or consistently defined in systematic reviews related to alert fatigue in clinical decision support. Reporting of other primary study characteristics is often limited. We recommend that future efforts use a significant, sustained decrease in appropriate alert response rates from an established baseline as a measure of alert fatigue.

BACKGROUND: The aim of this study was to determine whether dental providers wrote fewer prescriptions for opioid-containing medications and more prescriptions for non-opioid analgesics after taking a continuing education course that targeted both knowledge about an American Dental Association-endorsed guideline on the management of acute dental pain and challenges in shared decision-making.

METHODS: The implementation strategy comprised a prerecorded 1-h video continuing education course and supplementary materials that were previously shown to increase knowledge about shared decision-making. Using propensity score matching, we matched 420 dentists and dental specialists who took the continuing education course to 4,200 providers who had not. We used regression analyses to compare learners with their propensity score-matched controls on their change in opioid prescribing and change in non-opioid analgesic prescribing from the 6 mo before to the 6 mo after course completion.

RESULTS: Providers who took the continuing education course decreased the number of opioid prescriptions they wrote by 0.60 prescriptions more than providers who did not take the continuing education course (B = -0.60; 95% confidence interval [CI] -1.07, -0.13; t = -2.50; P < 0.01). Among providers who decreased their prescribing by 3 or more prescriptions, a greater percentage took the continuing education course. There was no difference in non-opioid analgesic prescribing between providers who took the continuing education course and providers who did not (B = -0.10; 95% CI -0.87 0.68; t = -0.25, ns).

CONCLUSION: Equipping dental providers with skills in shared decision-making to use in conversations with patients about approaches to acute pain management may enable them to decrease the number of opioid prescriptions they write more than providers who are not exposed to these skills.Knowledge Transfer Statement:The results of this study can be used by groups and organizations seeking to improve dental providers' adherence to the guideline on managing acute dental pain following simple and surgical tooth extraction or toothache.

BACKGROUND/OBJECTIVES: Preclinical evidence suggests beta blockers may reduce cartilage degradation and delay knee osteoarthritis (OA) progression. While beta blockers are widely used in patients with hypertension, their potential role in preventing total knee arthroplasty (TKA) is unclear. Therefore, we assessed the association between beta blocker use and TKA in knee OA patients with hypertension.

METHODS: We conducted a nested case-control study using a nationally representative sample of Medicare beneficiaries with newly diagnosed knee OA and prevalent hypertension from 2011 to 2020. Beneficiaries who underwent TKA were defined as cases, while those without TKA were defined as controls. Cases and controls were matched at a 1:4 ratio based on pre-specified criteria using incident density sampling. We measured binary (exposed/unexposed) and cumulative exposure of beta blockers during 6 months before TKA using total standardized daily doses (TSDD) for each patient, categorized as unexposed (0), < 1-200, 201-400, 401-600, 601-900, > 900. Confounding was addressed using propensity score adjustment and stratification for the binary exposure and direct covariate adjustment for cumulative exposure in conditional logistic regression models.

RESULTS: We included 30 338 beneficiaries with TKA and 106 145 matched controls. The mean age (SD) was 74.4 (5.5) years, and 67.1% were women in both groups. There was no significant association between beta blocker use and odds of TKA (adjusted OR [aOR] 1.01; 95% CI, 0.97-1.02) compared with unexposed individuals. Smilarly, no cumulative exposure category was associated with TKA risk (TSDD: < 1-200 [aOR, 1.01; 95% CI,0.97-1.04]; TSDD: 201-400 [aOR 1.00; 95% CI, 0.96-1.05]; TSDD: 401-600 [aOR, 1.02; 95% CI, 0.96-1.08]; TSDD: 601-900 [aOR 0.94; 95% CI, 0.87-1.00]; and, TSDD: > 900 [aOR 0.99; 95% CI, 0.91-1.08]), compared with the unexposed group.

CONCLUSION: We found no evidence to support that beta blocker exposure reduces the likelihood of TKA.

OBJECTIVES: This study aimed to describe changes in psychotropic medication use over time in commercially insured children with autism spectrum disorder (ASD) across age groups and characterize the comorbidity burden in patients with more complex treatment regimens.

METHODS: Using deidentified administrative claims from the Workpartners Research Reference Database, we conducted a retrospective cohort study of employee dependents aged 0-17 years with ASD followed for 3 years. Psychotropic medication use was analyzed across three age groups (0-4, 5-9, and 10-17 years). In a subgroup with high treatment complexity, defined as polypharmacy (≥3 drug classes) and/or antipsychotic use, the prevalence of various co-occurring conditions associated with ASD was also described.

RESULTS: Among 2747 children with ASD, psychotropic medication use and polypharmacy were more common in older age groups. At Year 1, 32.8% of children aged 10-17 used ≥2 drug classes concurrently, compared with 0.9% and 15.3% in the 0-4 and 5-9 age groups, respectively. From Year 1 to Year 3, medication use increased in younger children but declined in the 10-17 age group. High treatment complexity was observed in 20.5% of children (n = 562) over the entire 3-year study period, most frequently in the 10-17 age group. A higher prevalence of comorbidities, including attention-deficit hyperactivity disorder, mental health conditions, conduct disorders, and irritability and agitation, was observed in those with high treatment complexity compared with those without.

CONCLUSIONS: Pharmacologic treatment patterns varied by age in children with ASD, and higher treatment complexity was associated with more frequent diagnoses of co-occurring psychiatric and behavioral conditions. Further understanding of longitudinal treatment trajectories should be explored in future research, such as by contextualizing treatment changes with symptom assessment and evaluating the social impact of treatment complexity.

BACKGROUND: Hospitalization for alcohol use disorder (AUD) offers an opportunity to initiate evidence-based medications for alcohol use disorder (MAUDs).

OBJECTIVE: To describe patterns and factors associated with hospital initiation of MAUD.

DESIGN: Retrospective cohort study.

SETTING: Veterans Health Administration (VHA).

PARTICIPANTS: Veterans hospitalized with a primary diagnosis of AUD in 2022 or 2023.

MEASUREMENTS: Patients had MAUD initiated as an inpatient or within 7 days of discharge. Logistic regression models estimated the predicted probabilities of MAUD initiation based on hospital fixed effects and demographic and clinical characteristics.

RESULTS: Among 29 041 hospitalizations for AUD of veterans without MAUD at baseline in 142 hospitals (median age, 55 years; 94% male), in 8932 hospitalizations (30.8%), MAUD was initiated as an inpatient or within 7 days; MAUDs were naltrexone (57.9%), acamprosate (16.5%), and injectable naltrexone (13.9%). Of MAUD initiations, 6221 (69.6%) were during an inpatient stay and the rest were within 7 days. Of the 6221 inpatient initiations, 97.7% had a prescription for MAUD within 30 days after discharge. In adjusted analyses, MAUD initiation was more likely for hospitalizations with a specialty addiction consultation and those receiving psychiatry versus medicine service. Initiation of MAUD was less likely for persons aged 65 years or older, men, American Indian or Alaska Native versus White veterans, frail veterans, veterans diagnosed with opioid use disorder, and those in the intensive care unit. The median hospital-level rate of MAUD initiation was 29.9% (IQR, 22.6% to 36.3%).

LIMITATION: Generalizability to other health care systems.

CONCLUSION: Within the VHA, 30% of hospitalizations for AUD resulted in MAUD initiation as an inpatient or within 7 days of discharge, with substantial variation across hospitals and patient demographic and clinical factors. These data indicate a need to identify and disseminate successful hospital-based strategies to increase prescribing of MAUD.

PRIMARY FUNDING SOURCE: U.S. Department of Veterans Affairs and National Institute on Aging.

BACKGROUND: Opioid overdose remains a leading cause of preventable death in the United States. Existing approaches to identify individuals at elevated risk rely on imprecise rule-based criteria that misclassify patients' risk of this serious health outcome. Machine learning (ML) algorithms can help improve prediction performance and can be combined with electronic health record (EHR) interventions to reduce overdose risk.

OBJECTIVE: The Machine Learning Prediction and Reducing Overdoses With EHR Nudges (mPROVEN) clinical trial integrates a validated ML overdose risk model with behavioral economics-informed EHR nudges to test whether the combination improves evidence-based prescribing behaviors associated with lower overdose risk and, ultimately, reduces overdose among elevated-risk patients.

METHODS: mPROVEN is a pragmatic cluster randomized controlled trial conducted in primary care practices within a large multistate integrated health system. Eligible patients are adults (≥18 years) identified by the ML algorithm as having elevated overdose risk and seen at a primary care visit during the study period. Primary care practices serve as the unit of randomization and will be randomized into three arms: (1) usual care; (2) elevated risk flag only, where clinicians see a noninterruptive EHR flag indicating elevated overdose risk; and (3) elevated risk flag + nudges, in which active choice and accountable justification alerts are embedded within the EHR in addition to the elevated risk flag. The trial will enroll a target cohort of 800 patients for the primary analysis. The intervention period is 4 months (or until the study ends, whichever occurs later). The primary outcome is a 3‑point composite measure of safer opioid prescribing at 4 months, awarding 1 point each for active naloxone prescription, average opioid dosage of 50 morphine milligram equivalents per day or less, and absence of opioid-benzodiazepine overlap. Secondary outcomes include the composite outcome at 6 months, individual score components, and all-cause and overdose-specific emergency department or inpatient visits. Outcomes will be compared across study arms using an intention‑to‑treat approach with linear mixed‑effects models accounting for clinic-level clustering.

RESULTS: Funded by the National Institutes of Health, in June 2022, enrollment began on March 10, 2025. Enrollment for the primary analysis cohort (n=798) was completed in May 2025 with additional participants enrolled for secondary analyses through December 2025 (n=1662). Primary cohort analyses began in January 2026, and results are expected by mid-2027.

CONCLUSIONS: The mPROVEN study is among the first pragmatic randomized controlled trials to integrate ML‑based opioid overdose risk prediction with behavioral nudges within a large health system EHR. By combining advances in data science and behavioral economics, the study aims to reduce opioid overdose risk in primary care using a scalable and low-touch intervention to address a high-priority public health issue.

TRIAL REGISTRATION: ClinicalTrials.gov NCT06806163; https://clinicaltrials.gov/study/NCT06806163.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/94007.

INTRODUCTION: Higher buprenorphine maintenance doses (>24 mg daily) may be more effective in improving opioid use disorder (OUD) treatment outcomes in the era of fentanyl predominance. Evidence on maintenance dose trends remains limited, especially among vulnerable populations, who are disproportionately affected by opioid overdose deaths. The objective of this study was to describe trends in maintenance dose of buprenorphine for OUD and the association of social vulnerability with maintenance dose.

METHODS: We identified buprenorphine treatment episodes initiated between January 2019 and June 2021 using the IQVIA Longitudinal Prescription database. Episodes were assigned to a maintenance dose category of <16, 16-24, or >24 mg, to reflect dosages lower than, consistent with, or higher than the guideline-recommended target dose range, respectively. County-level social vulnerability was measured with the Minority Health Index (MHI). We used multinomial logistic regression with generalized estimating equations to test the association between MHI (stratified into quartiles) and maintenance doses <16 mg and >24 mg (with 16-24 mg as the reference group), adjusting for patient, geographic, and time characteristics.

RESULTS: Of 1,044,460 treatment episodes among 543,326 individuals (mean [SD] age 41.9 [12.6] years, 43% female), the most common buprenorphine maintenance dose category was 16-24 mg (62.7%), followed by <16 mg (34.5%), then >24 mg (2.8%). The median (IQR) episode duration was 64 (109) days. The proportion of episodes in the <16 mg category increased from 34.1% in 2019 to 39.2% in 2021, whereas prevalence of the remaining categories decreased. A greater proportion of episodes in the >24 mg category were in rural versus urban counties; the magnitude of this difference increased over time. Relative to 16-24 mg, maintenance dose >24 mg was more likely among individuals in the most vulnerable MHI quartile compared to the least vulnerable quartile (aOR 1.28, 95% CI 1.19-1.38).

CONCLUSION: During a period of growing evidence supporting higher buprenorphine maintenance doses, the prevalence of treatment episodes with maintenance dose <16 mg increased. Individuals in the highest social vulnerability quartile tended to receive higher doses, suggesting that some higher-risk groups may receive more intensive treatment, even as important gaps remain. These results can inform efforts targeting equitable, high-quality buprenorphine prescribing.