Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy

Abstract

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 was to analyze the changes on adherence implementation rates before and after a community pharmacist intervention integrated in usual real life 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 one year of analysis to 35.3%. Conclusion: Big database analysis techniques provided results on adherence implementation over 2 years of analysis. An increase in adherence rates was observed after the pharmacist intervention, followed by a gradual decrease 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 and sustain medication adherence.

Impact of a Multicomponent Digital Therapeutic Mobile App on Medication Adherence in Patients with Chronic Conditions: Retrospective Analysis

“…The adherence rates of Perx users averaged over 85% across six months. This was significantly higher than previously observed dispensing data adherence rates in Australian patients, which were found to be between 50.2% and 66.9% [ 38 ]. While a slight decrease in adherence was observed over 6 months, the long-term rates remained above 80%, which is often considered to be an optimal threshold for medication adherence [ 36 ].…”

<p>Pharmacist-led medication non-adherence intervention: reducing the economic burden placed on the Australian health care system</p>

“…PDC was defined as the total number of days supplied with the medication during the six-month period before and after the intervention divided by the total number of days in the fixed period. Analysis was conducted per trimesters, 6 months before and 6 months after the first pharmacist intervention, calculating the average PDC (%) and standard deviation (SD) for all patients in each period using descriptive statistics 29. Adherence was defined as a PDC of 80% or greater, the most common threshold for adequate adherence to chronic medications 30.…”

Impact of Social Determinants of Health and Demographics on Refill Requests by Medicare Patients Using a Conversational Artificial Intelligence Text Messaging Solution: Cross-Sectional Study

“…Put another way, patients are considered adherent when they refill often enough to cover 80% or more of their medication plan as prescribed by their health care provider and as agreed to by the patient [5]. Dispensing or refill data is commonly used to compute adherence levels because of the validity, relative accessibility, and inexpensiveness of such data [6].…”