Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy - Torres-Robles, Andrea; Wiecek, Elyssa; Cutler, Rachelle; Drake, Barry; Benrimoj, Shalom I.; Fernandez-Llimos, Fernando; Garcia-Cardenas, Victoria (Frontiers in Pharmacology, 2019)
Background: Medication non-adherence remains a significant problem for the health care system with clinical, humanistic and economic impact. Dispensing data is a valuable and commonly utilized measure due accessibility in electronic health data. The purpose of this study is to analyze the changes on adherence implementation rates before and after a community pharmacy intervention integrated in usual practice, incorporating big data analysis techniques to evaluate Proportion of Days Covered (PDC) from pharmacy dispensing data. Methods: Retrospective observational study. A de-identified database of dispensing data from 20,335 patients (n= 11,257 on rosuvastatin, n= 6,797 on irbesartan, and n= 2,281 on desvenlafaxine) was analyzed. Included patients received a pharmacist-led medication adherence intervention and had dispensing records before and after the intervention. As a measure of adherence implementation, PDC was utilized. Analysis of the database was performed using SQL and Python. Results: Three months after the pharmacist intervention there was an increase on average PDC from 50.2% (SD: 30.1) to 66.9% (SD: 29.9) for rosuvastatin, from 50.8% (SD: 30.3) to 68% (SD: 29.3) for irbesartan and from 47.3% (SD: 28.4) to 66.3% (SD: 27.3) for desvenlafaxine. These rates declined over 12 months to 62.1% (SD: 32.0) for rosuvastatin, to 62.4% (SD: 32.5) for irbesartan and to 58.1% (SD: 31.1) for desvenlafaxine. In terms of the proportion of adherent patients (PDC>80.0%) the trend was similar, increasing after the pharmacist intervention from overall 17.4% to 41.2% and decreasing after 1 year of analysis to 35.3%. Conclusion: Big database analysis techniques provided results on adherence implementation over two years of analysis. Although an increase on the rates was observed after the pharmacist intervention, there is still a prevalence on sub-optimal implementation over time. Enhancing the current intervention using an evidence-based approach and integrating big database analysis techniques to a real-time measurement of adherence could help community pharmacies improve medication adherence.