Publications

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.

OBJECTIVE: To characterize primary care physicians' (PCPs') perceptions of the reasons patients receive opioid medications from both VA and non-VA healthcare systems.

DESIGN: Qualitative.

SETTING: Department of Veterans Affairs (VA).

PARTICIPANTS: Forty-two VA PCPs who prescribed opioids to at least 15 patients and who practiced in Massachusetts, Illinois, or Pennsylvania.

METHODS: Thirty-minute, semistructured telephone interviews were conducted in 2016, addressing topics regarding PCPs' experiences and perspectives on patients who use both VA and non-VA healthcare systems to obtain prescription opioids. The analysis focused on two questions: attributes that PCPs believe characterize dual-use patients and reasons that PCPs believe patients obtain opioids from both VA and non-VA sources.

RESULTS: PCPs identified multiple attributes of, and reasons for, patients obtaining opioid medications from both VA and non-VA healthcare systems, including pain issues, opioid misuse, having healthcare managed through multiple healthcare systems, and transferring care between systems. More than half of the PCPs identified addiction and diversion as key attributes and reasons why patients obtain prescription opioids from multiple sources. PCPs also identified several behavioral and psychological factors as attributes of these patients.

CONCLUSIONS: PCPs within the VA have varying perceptions of patients obtaining opioid medications from multiple healthcare systems, with pain complaints and opioid misuse as the primary themes. This knowledge about PCPs' perceptions can be incorporated into interventions to better manage pain and prescription opioid use by VA patients.

BACKGROUND: More than half of enrollees in the U.S. Department of Veterans Affairs (VA) are also covered by Medicare and can choose to receive their prescriptions from VA or from Medicare-participating providers. Such dual-system care may lead to unsafe opioid use if providers in these 2 systems do not coordinate care or if prescription use is not tracked between systems.

OBJECTIVE: To evaluate the association between dual-system opioid prescribing and death from prescription opioid overdose.

DESIGN: Nested case-control study.

SETTING: VA and Medicare Part D.

PARTICIPANTS: Case and control patients were identified from all veterans enrolled in both VA and Part D who filled at least 1 opioid prescription from either system. The 215 case patients who died of a prescription opioid overdose in 2012 or 2013 were matched (up to 1:4) with 833 living control patients on the basis of date of death (that is, index date), using age, sex, race/ethnicity, disability, enrollment in Medicaid or low-income subsidies, managed care enrollment, region and rurality of residence, and a medication-based measure of comorbid conditions.

MEASUREMENTS: The exposure was the source of opioid prescriptions within 6 months of the index date, categorized as VA only, Part D only, or VA and Part D (that is, dual use). The outcome was unintentional or undetermined-intent death from prescription opioid overdose, identified from the National Death Index. The association between this outcome and source of opioid prescriptions was estimated using conditional logistic regression with adjustment for age, marital status, prescription drug monitoring programs, and use of other medications.

RESULTS: Among case patients, the mean age was 57.3 years (SD, 9.1), 194 (90%) were male, and 181 (84%) were non-Hispanic white. Overall, 60 case patients (28%) and 117 control patients (14%) received dual opioid prescriptions. Dual users had significantly higher odds of death from prescription opioid overdose than those who received opioids from VA only (odds ratio [OR], 3.53 [95% CI, 2.17 to 5.75]; P < 0.001) or Part D only (OR, 1.83 [CI, 1.20 to 2.77]; P = 0.005).

LIMITATION: Data are from 2012 to 2013 and cannot capture prescriptions obtained outside the VA or Medicare Part D systems.

CONCLUSION: Among veterans enrolled in VA and Part D, dual use of opioid prescriptions was independently associated with death from prescription opioid overdose. This risk factor for fatal overdose among veterans underscores the importance of care coordination across health care systems to improve opioid prescribing safety.

PRIMARY FUNDING SOURCE: U.S. Department of Veterans Affairs.

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.

BACKGROUND/OBJECTIVES: Prostate-specific antigen (PSA) screening can be of low value in older adults. Our objective was to quantify the prevalence and variation of low-value PSA screening across the Veterans Health Administration (VA), which has instituted programs to reduce low-value care.

DESIGN: Retrospective cohort.

SETTING: VA administrative data, 2014 to 2015.

PARTICIPANTS: National random sample (N = 214 480) of male veterans, aged 75 years or older.

MEASUREMENTS: We defined PSA screening in men aged 75 years or older without a history of prostate cancer as low value, per established definitions in Medicare. We calculated screening rates overall and by VA Medical Center (VAMC), adjusting for patient and VAMC-level factors. We characterized variation across VAMCs using the adjusted median odds ratio (OR) and compared the adjusted OR of screening between VAMCs in different deciles of low-value screening rates. In separate sensitivity analyses, we assessed screening in veterans at greatest risk of 1-year mortality and among veterans after excluding those who underwent prostatectomy, had a prior PSA elevation, or had a clinical indication for testing.

RESULTS: Overall, 37 867 (17.7%) of veterans underwent low-value PSA screening (VAMC range = 3.3%-38.2%). The adjusted median OR was 1.88, meaning the median odds of screening would increase by 88% were a veteran to transfer his care to a VAMC with higher screening rates. Veterans at VAMCs in the top decile had an adjusted OR of 12.9 (95% confidence interval = 11.0-15.2) compared to those veterans in the lowest decile. Among veterans with the greatest mortality risk (n = 23 377), 3496 (15.0%) underwent screening (VAMC range = 1.7%-46.3%). After excluding veterans with a prior prostatectomy, PSA elevation, or a potential clinical indication, 31 556 (14.7%) underwent screening (VAMC range = 2.0%-49.9%).

CONCLUSIONS: In a national cohort of older veterans, more than one in six received low-value PSA screening, with greater than 10-fold variation across VAMCs and high rates of screening among those with the greatest mortality risk. J Am Geriatr Soc 67:1922-1927, 2019.

BACKGROUND: Treatment by high-opioid prescribing physicians in the emergency department (ED) is associated with higher rates of long-term opioid use among Medicare beneficiaries. However, it is unclear if this result is true in other high-risk populations such as Veterans.

OBJECTIVE: To estimate the effect of exposure to high-opioid prescribing physicians on long-term opioid use for opioid-naïve Veterans.

DESIGN: Observational study using Veterans Health Administration (VA) encounter and prescription data.

SETTING AND PARTICIPANTS: Veterans with an index ED visit at any VA facility in 2012 and without opioid prescriptions in the prior 6 months in the VA system ("opioid naïve").

MEASUREMENTS: We assigned patients to emergency physicians and categorized physicians into within-hospital quartiles based on their opioid prescribing rates. Our primary outcome was long-term opioid use, defined as 6 months of days supplied in the 12 months subsequent to the ED visit. We compared rates of long-term opioid use among patients treated by high versus low quartile prescribers, adjusting for patient demographic, clinical characteristics, and ED diagnoses.

RESULTS: We identified 57,738 and 86,393 opioid-naïve Veterans managed by 362 and 440 low and high quartile prescribers, respectively. Patient characteristics were similar across groups. ED opioid prescribing rates varied more than threefold between the low and high quartile prescribers within hospitals (6.4% vs. 20.8%, p < 0.001). The frequency of long-term opioid use was higher among Veterans treated by high versus low quartile prescribers, though above the threshold for statistical significance (1.39% vs. 1.26%; adjusted OR 1.11, 95% CI 0.997-1.24, p = 0.056). In subgroup analyses, there were significant associations for patients with back pain (adjusted OR 1.25, 95% CI 1.01-1.55, p = 0.04) and for those with a history of depression (adjusted OR 1.28, 95% CI 1.08-1.51, p = 0.004).

CONCLUSIONS: ED physician opioid prescribing varied by over 300% within facility, with a statistically non-significant increased rate of long-term use among opioid-naïve Veterans exposed to the highest intensity prescribers.