In 2017, the United States Food and Drug Administration Reauthorization Act created a new category of hearing aids to be sold over the counter (OTC), disrupting how nearly 30 million persons with hearing loss will seek and purchase hearing aids. Laws and regulations do not require a medical evaluation or an appointment with an audiologist prior to purchasing OTC hearing aids. However, it is likely that patients will approach pharmacists with questions about OTC hearing aids when considering these devices available at the community pharmacy. The objective of this commentary is to discuss the opportunity for collaborative working relationships between pharmacists and audiologists in the context of OTC hearing aids. The most relevant barriers to pharmacist/audiologist collaboration are turf concerns, lack of trust, and distance between practice sites. OTC hearing aids can positively impact hearing health care across the nation with successful collaboration between the professions of pharmacy and audiology.
Publications
2022
BACKGROUND: Pharmacy accessibility is key for the emerging role of community pharmacists as providers of patient-centered, medication management services in addition to traditional dispensing roles.
OBJECTIVE: To quantify population access to community pharmacies across the United States.
METHODS: We obtained addresses for pharmacy locations in the United States from the National Council for Prescription Drug Programs and geocoded each. For a 1% sample of a U.S. synthetic population, we calculated the driving distance to the closest pharmacy using ArcGIS. We estimated the proportion of population living within 1, 2, 5, and 10 miles of a community pharmacy. We quantified the role of chain vs regional franchises or independently owned pharmacies in providing access across degrees of urbanicity.
RESULTS: We identified 61,715 pharmacies, including 37,954 (61.5%) chains, 23,521 (38.1%) regional franchises or independently owned pharmacies, and 240 (0.4%) government pharmacies. In large metropolitan areas, 62.8% of the pharmacies were chains; however, in rural areas, 76.5% of pharmacies were franchises or independent pharmacies. Across the overall U.S. population, 48.1% lived within 1 mile of any pharmacy, 73.1% within 2 miles, 88.9% within 5 miles, and 96.5% within 10 miles. Across the United States, 8.3% of counties had at least 50% of residents with a distance greater than 10 miles. These low-access counties were concentrated in Alaska, South Dakota, North Dakota, and Montana.
CONCLUSIONS: Community pharmacies may serve as accessible locations for patient-centered, medication management services that enhance the health and wellness of communities. Although chain pharmacies represent the majority of pharmacy locations across the country, access to community pharmacies in rural areas predominantly relies on regional franchises and independently owned pharmacies.
BACKGROUND AND AIMS: The time lag encountered when accessing health-care data is one major barrier to implementing opioid overdose prediction measures in practice. Little is known regarding how one's opioid overdose risk changes over time. We aimed to identify longitudinal patterns of individual predicted overdose risks among Medicaid beneficiaries after initiation of opioid prescriptions.
DESIGN, SETTING AND PARTICIPANTS: A retrospective cohort study in Pennsylvania, USA among Pennsylvania Medicaid beneficiaries aged 18-64 years who initiated opioid prescriptions between July 2017 and September 2018 (318 585 eligible beneficiaries (mean age = 39 ± 12 years, female = 65.7%, White = 62.2% and Black = 24.9%).
MEASUREMENTS: We first applied a previously developed and validated machine-learning algorithm to obtain risk scores for opioid overdose emergency room or hospital visits in 3-month intervals for each beneficiary who initiated opioid therapy, until disenrollment from Medicaid, death or the end of observation (December 2018). We performed group-based trajectory modeling to identify trajectories of these predicted overdose risk scores over time.
FINDINGS: Among eligible beneficiaries, 0.61% had one or more occurrences of opioid overdose in a median follow-up of 15 months. We identified five unique opioid overdose risk trajectories: three trajectories (accounting for 92% of the cohort) had consistent overdose risk over time, including consistent low-risk (63%), consistent medium-risk (25%) and consistent high-risk (4%) groups; another two trajectories (accounting for 8%) had overdose risks that substantially changed over time, including a group that transitioned from high- to medium-risk (3%) and another group that increased from medium- to high-risk over time (5%).
CONCLUSIONS: More than 90% of Medicaid beneficiaries in Pennsylvania USA with one or more opioid prescriptions had consistent, predicted opioid overdose risks over 15 months. Applying opioid prediction algorithms developed from historical data may not be a major barrier to implementation in practice for the large majority of individuals.
BACKGROUND: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state).
METHODS: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013-16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017-18 Pennsylvania Medicaid data (n=318 585) and (2) 2015-17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use.
FINDINGS: A total of 8641 (1·35%) 2013-16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017-18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015-17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013-16 Pennsylvania training dataset and validated on the 2013-16 Pennsylvania internal validation dataset, 2017-18 Pennsylvania external validation dataset, and 2015-17 Arizona external validation dataset were 0·841 (95% CI 0·835-0·847), 0·828 (0·822-0·834), and 0·817 (0·807-0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017-18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38-4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015-17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19-1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose.
INTERPRETATION: A machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries.
FUNDING: National Institute of Health, National Institute on Drug Abuse, National Institute on Aging.
IMPORTANCE: Metrics that detect low-value care in common forms of health care data, such as administrative claims or electronic health records, primarily focus on tests and procedures but not on medications, representing a major gap in the ability to systematically measure low-value prescribing.
OBJECTIVE: To develop a scalable and broadly applicable metric that contains a set of quality indicators (EVOLV-Rx) for use in health care data to detect and reduce low-value prescribing among older adults and that is informed by diverse stakeholders' perspectives.
DESIGN, SETTING, AND PARTICIPANTS: This qualitative study used an online modified-Delphi method to convene an expert panel of 15 physicians and pharmacists. This panel, comprising clinicians, health system leaders, and researchers, was tasked with rating and discussing candidate low-value prescribing practices that were derived from medication safety criteria; peer-reviewed literature; and qualitative studies of patient, caregiver, and physician perspectives. The RAND ExpertLens online platform was used to conduct the activities of the panel. The panelists were engaged for 3 rounds between January 1 and March 31, 2021.
MAIN OUTCOMES AND MEASURES: Panelists used a 9-point Likert scale to rate and then discuss the scientific validity and clinical usefulness of the criteria to detect low-value prescribing practices. Candidate low-value prescribing practices were rated as follows: 1 to 3, indicating low validity or usefulness; 3.5 to 6, uncertain validity or usefulness; and 6.5 to 9, high validity or usefulness. Agreement among panelists and the degree of scientific validity and clinical usefulness were assessed using the RAND/UCLA (University of California, Los Angeles) Appropriateness Method.
RESULTS: Of the 527 low-value prescribing recommendations identified, 27 discrete candidate low-value prescribing practices were considered for inclusion in EVOLV-Rx. After round 1, 18 candidate practices were rated by the panel as having high scientific validity and clinical usefulness (scores of ≥6.5). After round 2 panel deliberations, the criteria to detect 19 candidate practices were revised. After round 3, 18 candidate practices met the inclusion criteria, receiving final median scores of 6.5 or higher for both scientific validity and clinical usefulness. Of those practices that were not included in the final version of EVOLV-Rx, 3 received high scientific validity (scores ≥6.5) but uncertain clinical usefulness (scores <6.5) ratings, whereas 6 received uncertain scientific validity rating (scores <6.5).
CONCLUSIONS AND RELEVANCE: This study culminated in the development of EVOLV-Rx and involved a panel of experts who identified the 18 most salient low-value prescribing practices in the care of older adults. Applying EVOLV-Rx may enhance the detection of low-value prescribing practices, reduce polypharmacy, and enable older adults to receive high-value care across the full spectrum of health services.
IMPORTANCE: Older US veterans commonly receive health care outside of the US Veterans Health Administration (VHA) through Medicare, which may increase receipt of low-value care and subsequent care cascades.
OBJECTIVE: To characterize the frequency, cost, and source of low-value prostate-specific antigen (PSA) testing and subsequent care cascades among veterans dually enrolled in the VHA and Medicare and to determine whether receiving a PSA test through the VHA vs Medicare is associated with more downstream services.
DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used VHA and Medicare administrative data from fiscal years (FYs) 2017 to 2018. The study cohort consisted of male US veterans dually enrolled in the VHA and Medicare who were aged 75 years or older without a history of prostate cancer, elevated PSA, prostatectomy, radiation therapy, androgen deprivation therapy, or a urology visit. Data were analyzed from December 15, 2020, to October 20, 2022.
EXPOSURES: Receipt of low-value PSA testing.
MAIN OUTCOMES AND MEASURES: Differences in the use and cost of cascade services occurring 6 months after receipt of a low-value PSA test were assessed for veterans who underwent low-value PSA testing in the VHA and Medicare compared with those who did not, adjusted for patient- and facility-level covariates.
RESULTS: This study included 300 393 male US veterans at risk of undergoing low-value PSA testing. They had a mean (SD) age of 82.6 (5.6) years, and the majority (264 411 [88.0%]) were non-Hispanic White. Of these veterans, 36 459 (12.1%) received a low-value PSA test through the VHA, which was associated with 31.2 (95% CI, 29.2 to 33.2) additional cascade services per 100 veterans and an additional $24.5 (95% CI, $20.8 to $28.1) per veteran compared with the control group. In the same cohort, 17 981 veterans (5.9%) received a PSA test through Medicare, which was associated with 39.3 (95% CI, 37.2 to 41.3) additional cascade services per 100 veterans and an additional $35.9 (95% CI, $31.7 to $40.1) per veteran compared with the control group. When compared directly, veterans who received a PSA test through Medicare experienced 9.9 (95% CI, 9.7 to 10.1) additional cascade services per 100 veterans compared with those who underwent testing within the VHA.
CONCLUSIONS AND RELEVANCE: The findings of this cohort study suggest that US veterans dually enrolled in the VHA and Medicare commonly experienced low-value PSA testing and subsequent care cascades through both systems in FYs 2017 and 2018. Care cascades occurred more frequently through Medicare compared with the VHA. These findings suggest that low-value PSA testing has substantial downstream implications for patients and may be especially challenging to measure when care occurs in multiple health care systems.
IMPORTANCE: Within the Veterans Health Administration (VA), the use and cost of low-value services delivered by VA facilities or increasingly by VA Community Care (VACC) programs have not been comprehensively quantified.
OBJECTIVE: To quantify veterans' overall use and cost of low-value services, including VA-delivered care and VA-purchased community care.
DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study assessed a national population of VA-enrolled veterans. Data on enrollment, sociodemographic characteristics, comorbidities, and health care services delivered by VA facilities or paid for by the VA through VACC programs were compiled for fiscal year 2018 from the VA Corporate Data Warehouse. Data analysis was conducted from April 2020 to January 2022.
MAIN OUTCOMES AND MEASURES: VA administrative data were applied using an established low-value service metric to quantify the use of 29 potentially low-value tests and procedures delivered in VA facilities and by VACC programs across 6 domains: cancer screening, diagnostic and preventive testing, preoperative testing, imaging, cardiovascular testing and procedures, and other procedures. Sensitive and specific criteria were used to determine the low-value service counts per 100 veterans overall, by domain, and by individual service; count and percentage of each low-value service delivered by each setting; and estimated cost of each service.
RESULTS: Among 5.2 million enrolled veterans, the mean (SD) age was 62.5 (16.0) years, 91.7% were male, 68.0% were non-Hispanic White, and 32.3% received any service through VACC. By specific criteria, 19.6 low-value services per 100 veterans were delivered in VA facilities or by VACC programs, involving 13.6% of veterans at a total cost of $205.8 million. Overall, the most frequently delivered low-value service was prostate-specific antigen testing for men aged 75 years or older (5.9 per 100 veterans); this was also the service with the greatest proportion delivered by VA facilities (98.9%). The costliest low-value services were spinal injections for low back pain ($43.9 million; 21.4% of low-value care spending) and percutaneous coronary intervention for stable coronary disease ($36.8 million; 17.9% of spending).
CONCLUSIONS AND RELEVANCE: This cross-sectional study found that among veterans enrolled in the VA, more than 1 in 10 have received a low-value service from VA facilities or VACC programs, with approximately $200 million in associated costs. Such information on the use and costs of low-value services are essential to guide the VA's efforts to reduce delivery and spending on such care.
BACKGROUND: Low-value prescribing may result in adverse patient outcomes and increased medical expenditures. Clinicians' baseline strategies for navigating patient encounters involving low-value prescribing remain poorly understood, making it challenging to develop acceptable deprescribing interventions. Our objective was to characterize primary care physicians' (PCPs) approaches to reduce low-value prescribing in older adults through qualitative analysis of clinical scenarios.
METHODS: As part of an overarching qualitative study on low-value prescribing, we presented two clinical scenarios involving potential low-value prescribing during semi-structured interviews of 16 academic and community PCPs from general internal medicine, family medicine and geriatrics who care for patients aged greater than or equal to 65. We conducted a qualitative analysis of their responses to identify salient themes related to their approaches to prescribing, deprescribing, and meeting patients' expectations surrounding low-value prescribing.
RESULTS: We identified three key themes. First, when deprescribing, PCPs were motivated by their desire to mitigate patient harms and follow medication safety and deprescribing guidelines. Second, PCPs emphasized good communication with patients when navigating patient encounters related to low-value prescribing; and third, while physicians emphasized the importance of shared decision-making, they prioritized patients' well-being over satisfying their expectations.
CONCLUSIONS: When presented with real-life clinical scenarios, PCPs in our cohort sought to reduce low-value prescribing in a guideline-concordant fashion while maintaining good communication with their patients. This was driven primarily by a desire to minimize the potential for harm. This suggests that barriers other than clinician knowledge may be driving ongoing use of low-value medications in clinical practice.
Unlike demand studies in other industries, models of provider demand in health care often must omit a price, or any other factor that equilibrates the market such as a waiting time. Estimates of the consumer response to quality may consequently be attenuated, if the limited capacity of individual physicians prevents some consumers from obtaining higher quality. We propose a tractable method to address this problem by adding a congestion effect to standard discrete-choice models. We show analytically how this can improve forecasts of the consumer response to quality. We then apply this method to the market for heart surgery, and find that the attenuation bias in estimated quality effects can be important empirically.