Background: In response to high cost and inadequate quality, the healthcare system is in the midst of a transition from paying for volume to paying for value. Billions of dollars could be saved through more effective medication use, and evidence supports the role of the community pharmacist in lowering healthcare cost and improving healthcare quality through medication optimization. Despite this, value-based payment models for community pharmacies are rare, and those that do exist have not been critically evaluated and implementation in a commercially insured population is rare.
Objective: The first objective was to design and test a conceptual model of pharmacy value. The second objective was to evaluate variation in the value community pharmacies provide a commercial insurer by assessing the relationship between attributed patients’ healthcare quality and cost.
Methods: This study used prescription and medical claims data for 2012 and 2013 from a large commercial insurer in Iowa and South Dakota. Patients were attributed to the pharmacy filling the majority of their prescriptions. Pharmacies’ weekly prescription volume and Sunday prescription filling behavior were used as structural measures of healthcare quality. Percent of days covered (PDC) metrics for beta-blockers, statins, renin-angiotensin system antagonists and non-insulin diabetes agents were used as process metrics. Pharmacies were excluded if the denominator for any PDC metric was less than 15. Outcome metrics consisted of a non-trauma, non-cancer, unplanned hospitalization rate and a non-trauma ED visit rate. Cost impact was categorized into pharmaceutical, medical, and total cost of care. High quality pharmacies with typical or low associated costs or low cost pharmacies with typical to high quality were identified as high value and vice versa for low value. All metrics were risk-adjusted using mixed effect models with a random pharmacy intercept. The ratio between observed and expected quality scores was used for quality scoring. Quality outliers were identified by comparing the 95% CI around pharmacies’ risk-adjusted scores to the all-pharmacy risk-adjusted score mean. A t-test was used to assess variation in pharmacy value.
Results: There were 171 pharmacies and 74,581 patients eligible for scoring on all quality metrics. Mixed effects models observed a small but significant impact of pharmacy on process and outcome healthcare quality. No relationship between structures and processes, processes and outcomes was detected. Ten pharmacies were scored as high quality and nine as low quality. Similar numbers were identified for cost outliers, and significant variation in value was detected.
Implications/conclusions: Results support the hypothesis that high and low value pharmacies exist. A well-designed value-based payment model could be used to create incentives for pharmacists to enhance care for commercially insured patients, but validation is needed to ensure that incentives are aligned appropriately.