Publications

The purpose of this project was to evaluate the impact of a comprehensive medication adherence packaging (RxMAP) service on patient medication-taking behaviors and patient-centered outcomes. Adult patients who utilized a single independent community pharmacy, enrolled in the RxMAP service for at least two consecutive cycles, and managed their own medications were eligible. The RxMAP service consists of multi-dose blister packaging in 28-day cycles, medication synchronization, monthly touchpoint calls, and delivery/mailing. A 13-item telephonic survey was administered, and patients' verbal responses were captured by audio-recording and detailed note taking. Descriptive statistics were used to quantify the results and illustrative quotes representing the interview domains were selected. There were 42 patients who completed the survey: 88% reported they missed fewer doses compared to before using RxMAP; 71% were more likely to take their medications on time each day; 86% were more confident with managing their medications; and 74% were more independent. Finally, 64% of patients stated their overall quality of life was better now compared to before using the packaging service. These results demonstrate that medication adherence packaging services can positively impact patients' medication-taking behaviors, increase their confidence in medication management, and improve perceived quality of life.

With increasing patient interest in and access to pharmacogenomic testing, clinicians practicing in primary care are more likely than ever to encounter a patient seeking or presenting with pharmacogenomic test results. Gene-based prescribing recommendations are available to healthcare providers through Food and Drug Administration-approved drug labeling and Clinical Pharmacogenetics Implementation Consortium guidelines. Given the lifelong utility of pharmacogenomic test results to optimize pharmacotherapy for commonly prescribed medications, appropriate documentation of these results in a patient's electronic health record (EHR) is essential. The current "gold standard" for pharmacogenomics implementation includes entering pharmacogenomic test results into EHRs as discrete results with associated clinical decision support (CDS) alerts that will fire at the point of prescribing, similar to drug allergy alerts. However, such infrastructure is limited to the few institutions that have invested in the resources and personnel to develop and maintain it. For the majority of clinicians who do not practice at an institution with a dedicated clinical pharmacogenomics team and integrated pharmacogenomics CDS in the EHR, this report provides practical tips for documenting pharmacogenomic test results in the problem list and allergy field to maximize the visibility and utility of results over time, especially when such results could prevent the occurrence of serious adverse drug reactions or predict therapeutic failure.

BACKGROUND: Nearly 300 medications contain pharmacogenomic information in their labeling approved by the U.S. Food and Drug Administration. As this number continues to grow, community pharmacists will be called on to use available pharmacogenomic data at the point of dispensing.

OBJECTIVE: This qualitative study aimed to describe how pharmacists envision the integration of pharmacogenomic data into the current workflows of community pharmacy practice.

METHODS: Community pharmacists from a regional supermarket chain pharmacy in the greater Pittsburgh area were interviewed using a semistructured interview guide. Participating pharmacists were presented with 3 clinical scenarios, followed by questions, to gain insight into how they envisioned the integration of pharmacogenomic data into community pharmacy workflow. The interview transcriptions were transcribed and coded. The content was analyzed to deduce the final themes. Supporting quotes were selected to illustrate each theme.

RESULTS: Ten community pharmacists from 3 different pharmacy locations participated in the study. A thematic analysis produced 6 themes: (1) integrating pharmacogenomic data into the dispensing software, (2) receiving an alert for pharmacogenomic information within the dispensing software, (3) accessing pharmacogenomic clinical guidelines to guide drug-decision-making, (4) contacting the prescriber by adding a task to the call queue, (5) placing a mandatory counseling alert on medications that were adjusted using pharmacogenomic data, and (6) counseling the patient on the first refill of a medication that was adjusted using pharmacogenomic data.

CONCLUSION: This study describes how pharmacists envisioned the integration of pharmacogenomic data into community pharmacy workflow. The participants sought the integration of pharmacogenomic data into existing dispensing software, alerts for actionable prescribing changes using patient-specific pharmacogenomic data when available, and access to clinical decision support. In addition, the participants preferred to engage prescribers and receive alerts to counsel patients at prescription pick-up. These findings are key to integrating pharmacogenomic data into community pharmacy practice.

OBJECTIVE: To determine internal medicine (IM) residents' knowledge of, attitudes towards, and barriers to prescribing buprenorphine for opioid use disorder (OUD).

METHODS: We conducted a cross-sectional study of IM residents across all 35 Accreditation Council for Graduate Medical Education (ACGME) accredited Florida IM residency programs. We used an online survey to collect information about resident demographics, substance use curriculums, career interests, content knowledge about diagnosing and managing OUD, and attitudes about and barriers to prescribing buprenorphine for OUD. We used Chi-square test to explore differences in interest in prescribing buprenorphine. We created a composite knowledge score and investigated distribution of knowledge among characteristics via Mann-Whitney U test.

RESULTS: There were 161 participants (response rate 16.0%, n = 1008) across 35 programs Seventy-seven percent of residents provided care for patients with OUD more than once per month. Seventy-four percent report no buprenorphine prescribing training. Higher knowledge scores, interest in primary care, being an intern, and caring for patients with OUD more than monthly were associated with interest in obtaining a buprenorphine waiver (P < 0.05). Limited knowledge about OUD was the most important barrier to prescribing buprenorphine. Eighty-nine percent support legislation to deregulate buprenorphine.

CONCLUSIONS: Knowledge about managing OUD was poor and represented the most commonly cited barrier to prescribing buprenorphine. Residents want to expand their role in treating OUD. Our findings warrant incorporating addiction medicine into residency curriculum standards. Legislation removing the buprenorphine waiver requirement may increase the number of resident buprenorphine prescribers and improve treatment options for patients with opioid addiction.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877-0.892 vs. C-statistic = 0.871; 95%CI = 0.863-0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

BACKGROUND: Survivors of opioid overdose have substantially increased mortality risk, although this risk is not evenly distributed across individuals. No study has focused on predicting an individual's risk of death after a nonfatal opioid overdose.

OBJECTIVE: To predict risk of death after a nonfatal opioid overdose.

DESIGN AND PARTICIPANTS: This retrospective cohort study included 9686 Pennsylvania Medicaid beneficiaries with an emergency department or inpatient claim for nonfatal opioid overdose in 2014-2016. The index date was the first overdose claim during this period.

EXPOSURES, MAIN OUTCOME, AND MEASURES: Predictor candidates were measured in the 180 days before the index overdose. Primary outcome was 180-day all-cause mortality. Using a gradient boosting machine model, we classified beneficiaries into six subgroups according to their risk of mortality (< 25th percentile of the risk score, 25th to < 50th, 50th to < 75th, 75th to < 90th, 90th to < 98th, ≥ 98th). We then measured receipt of medication for opioid use disorder (OUD), risk mitigation interventions (e.g., prescriptions for naloxone), and prescription opioids filled in the 180 days after the index overdose, by risk subgroup.

KEY RESULTS: Of eligible beneficiaries, 347 (3.6%) died within 180 days after the index overdose. The C-statistic of the mortality prediction model was 0.71. In the highest risk subgroup, the observed 180-day mortality rate was 20.3%, while in the lowest risk subgroup, it was 1.5%. Medication for OUD and risk mitigation interventions after overdose were more commonly seen in lower risk groups, while opioid prescriptions were more likely to be used in higher risk groups (both p trends < .001).

CONCLUSIONS: A risk prediction model performed well for classifying mortality risk after a nonfatal opioid overdose. This prediction score can identify high-risk subgroups to target interventions to improve outcomes among overdose survivors.

BACKGROUND: Most states have recently passed laws requiring prescribers to use prescription drug monitoring programs (PDMPs) before prescribing opioid medications. The impact of these mandates on discontinuing chronic opioid therapy among Veterans managed in the Veterans Health Administration (VA) is unknown. We assess the association between the earliest of these laws and discontinuation of chronic opioid therapy in Veterans receiving VA health care.

METHODS: We conducted a comparative interrupted time-series study in the 5 states mandating PDMP use before August 2013 (Ohio, West Virginia, Kentucky, New Mexico, and Tennessee), adjusting for trends in the 17 neighboring control states without such mandates. We modeled 25 months of prescribing for each state centered on the month the mandate became effective. We included Veterans prescribed long-term outpatient opioid therapy (305 of the preceding 365 d). Our outcomes were discontinuation of chronic opioid therapy (primary outcome) and the average daily quantity of opioids per Veteran over the following 6 months (secondary outcome).

RESULTS: We included 250 monthly cohorts with 225,665 unique Veterans and 3.4 million Veteran-months. Baseline discontinuation rates before the PDMP mandates were 0.4%-2.7% per month. Kentucky saw a discontinuation increase of 1 absolute percentage point following its PDMP mandate which decreased over time. The other 4 states had no significant association between their mandates and change in opioid discontinuation. There was no evidence of decreasing opioid quantities following PDMP mandates.

CONCLUSION: We did not find consistent evidence that state laws mandating provider PDMP use were associated with the discontinuation of chronic opioid therapy within the VA for the time period studied.