Publications

In this cohort study, pricing data from January 2007 to June 2018 from SSR Health were used to determine how list prices, net prices, and discounts for the originator biologics changed with biosimilar competition.

BACKGROUND: There is systemic undercoding of medical comorbidities within administrative claims in the Department of Veterans Affairs (VA). This leads to bias when applying claims-based risk adjustment indices to compare outcomes between VA and non-VA settings. Our objective was to compare the accuracy of a medication-based risk adjustment index (RxRisk-VM) to diagnostic claims-based indices for predicting mortality.

METHODS: We modified the RxRisk-V index (RxRisk-VM) by incorporating VA and Medicare pharmacy and durable medical equipment claims in Veterans dually-enrolled in VA and Medicare in 2012. Using the concordance (C) statistic, we compared its accuracy in predicting 1 and 3-year all-cause mortality to the following models: demographics only, demographics plus prescription count, or demographics plus a diagnostic claims-based risk index (e.g., Charlson, Elixhauser, or Gagne). We also compared models containing demographics, RxRisk-VM, and a claims-based index.

RESULTS: In our cohort of 271,184 dually-enrolled Veterans (mean age = 70.5 years, 96.1% male, 81.7% non-Hispanic white), RxRisk-VM (C = 0.773) exhibited greater accuracy in predicting 1-year mortality than demographics only (C = 0.716) or prescription counts (C = 0.744), but was less accurate than the Charlson (C = 0.794), Elixhauser (C = 0.80), or Gagne (C = 0.810) indices (all P < 0.001). Combining RxRisk-VM with claims-based indices enhanced its accuracy over each index alone (all models C ≥ 0.81). Relative model performance was similar for 3-year mortality.

CONCLUSIONS: The RxRisk-VM index exhibited a high level of, but slightly less, accuracy in predicting mortality in comparison to claims-based risk indices.

IMPLICATIONS: Its application may enhance the accuracy of studies examining VA and non-VA care and enable risk adjustment when diagnostic claims are not available or biased.

LEVEL OF EVIDENCE: Level 3.

OBJECTIVE: The objective of this study was to determine strategies to implement influenza pandemic vaccinations effectively at grocery store chain community pharmacies.

METHODS: Clinical pharmacy coordinators and pharmacy managers representing 3 grocery store chain community pharmacies across Pennsylvania were identified for participation in semistructured telephone interviews. Interviews were audio-recorded and transcribed. Transcripts were independently coded by 2 investigators and coding discrepancies were resolved. A thematic analysis was conducted, and supporting quotes were selected for each theme.

RESULTS: Twelve pharmacists participated in the interviews, which were conducted from September 2016 to November 2017. Five key themes were identified: (1) mobilize pharmacy staff members to specific locations to prepare for a high volume of vaccinations; (2) implement vaccination clinics during high-volume scenarios; (3) utilize nonpharmacy spaces to increase vaccination capabilities; (4) determine vaccine distribution by highest risk populations that each pharmacy serves; and (5) conduct training customized to the pharmacy chain that supplements national pandemic influenza training.

CONCLUSION: Grocery store chain community pharmacies are desirable sites for pandemic vaccination because of a variety of factors, such as space and staffing flexibility. Developing a pandemic vaccination plan will enable community pharmacists to contribute more effectively during influenza pandemics.

OBJECTIVE: To garner experience with the early implementation of pharmacist-provided comprehensive medication management at a regional supermarket pharmacy during the initial launch of a statewide community pharmacy enhanced services network payer contract.

METHODS: A series of key informant interviews were conducted with pharmacists at Giant Eagle Pharmacy locations in Pennsylvania. To be eligible to participate, pharmacists must have been trained by the Pennsylvania Pharmacists Care Network to deliver contracted comprehensive medication management services and willing to participate in audio recorded, telephonic interviews every 2 weeks. Interviews concluded when each pharmacist completed a total of 6 interviews or when the project period ended. A semistructured interview guide was developed by the investigators to elicit the pharmacists' experience providing contracted services. Interviews were transcribed and coded by 2 independent investigators. Coding discrepancies were resolved. The final coded transcripts were presented back to the project team to identify and finalize major themes. Illustrative quotes were selected to represent each theme.

RESULTS: Interviews from 10 pharmacists were included in the analysis. Five themes emerged as keys of successful early implementation: (1) promote commitment of the pharmacy team, (2) use effective whole-team patient engagement strategies, (3) personalize patient encounters by providing patient-centered care and practicing interpersonal skills, (4) make workflow and staffing resources easily accessible, and (5) make clinical patient care tools readily available.

CONCLUSION: These results highlight thematic trends for how pharmacists can successfully engage their patients in contracted comprehensive medication management services. Understanding the success of early implementation at a regional supermarket pharmacy can serve as a framework for other participants in community pharmacy enhanced services networks to replicate and scale contracted patient care services.

OBJECTIVES: Pharmacist leadership and knowledge of pharmacogenomics is critical to the acceleration and enhancement of clinical pharmacogenomic services. This study aims for a qualitative description of community pharmacists' pharmacogenomic educational needs when implementing clinical pharmacogenomic services at community pharmacies.

METHODS: Pharmacists practicing at Rite Aid Pharmacy locations in the Greater Pittsburgh Area were recruited to participate in this qualitative analysis. Pharmacists from pharmacy locations offering pharmacogenomic testing and robust patient care services were eligible to participate in a semistructured, audio-recorded interview. The semistructured interview covered 4 domains crafted by the investigative team: (1) previous knowledge of pharmacogenomics; (2) implementation resources; (3) workflow adaptation; and (4) learning preferences. Interviews were transcribed verbatim and independently coded by 2 researchers. A thematic analysis by the investigative team followed. Supporting quotes were selected to illustrate each theme.

RESULTS: Eleven pharmacists from 9 unique pharmacy locations participated in this study. The average length of practice as a community pharmacist was 12 years (range, 1.5-31 years). Pharmacist's pharmacogenomic educational needs were categorized into 5 key themes: (1) enriched pharmacogenomic education and training; (2) active learning to build confidence in using pharmacogenomic data in practice; (3) robust and reputable clinical resources to effectively implement pharmacogenomic services; (4) team-based approach throughout implementation; (5) readily accessible network of pharmacogenomic experts.

CONCLUSION: This study describes the educational needs and preferences of community pharmacists for the successful provision of clinical pharmacogenomic services in community pharmacies. Pharmacists recognized their needs for enriched knowledge and instruction, practice applying pharmacogenomic principles with team-based approaches, robust clinical resources, and access to pharmacogenomic experts. This deeper understanding of pharmacist needs for pharmacogenomic education could help to accelerate and enhance the clinical implementation of pharmacogenomic services led by community pharmacists.

IMPORTANCE: Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk.

OBJECTIVE: To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription.

DESIGN, SETTING, AND PARTICIPANTS: A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples.

EXPOSURES: Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation.

MAIN OUTCOMES AND MEASURES: Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity.

RESULTS: Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2% [142 180] of the cohort), medium-risk (18.6% [34 579] of the cohort), and high-risk (5.2% [9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome.

CONCLUSIONS AND RELEVANCE: Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.

RATIONALE: Telemedicine is an increasingly common care delivery strategy in the ICU. However, ICU telemedicine programs vary widely in their clinical effectiveness, with some studies showing a large mortality benefit and others showing no benefit or even harm.

OBJECTIVES: To identify the organizational factors associated with ICU telemedicine effectiveness.

METHODS: We performed a focused ethnographic evaluation of 10 ICU telemedicine programs using site visits, interviews, and focus groups in both facilities providing remote care and the target ICUs. Programs were selected based on their change in risk-adjusted mortality after adoption (decreased mortality, no change in mortality, and increased mortality). We used a constant comparative approach to guide data collection and analysis.

MEASUREMENTS AND MAIN RESULTS: We conducted 460 hours of direct observation, 222 interviews, and 18 focus groups across six telemedicine facilities and 10 target ICUs. Data analysis revealed three domains that influence ICU telemedicine effectiveness: 1) leadership (i.e., the decisions related to the role of the telemedicine, conflict resolution, and relationship building), 2) perceived value (i.e., expectations of availability and impact, staff satisfaction, and understanding of operations), and 3) organizational characteristics (i.e., staffing models, allowed involvement of the telemedicine unit, and new hire orientation). In the most effective telemedicine programs these factors led to services that are viewed as appropriate, integrated, responsive, and consistent.

CONCLUSIONS: The effectiveness of ICU telemedicine programs may be influenced by several potentially modifiable factors within the domains of leadership, perceived value, and organizational structure.