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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Pharmacol. | doi: 10.3389/fphar.2019.00130

Using dispensing data to evaluate adherence implementation rates in community pharmacy

  • 1Graduate School of Health, University of Technology Sydney, Australia
  • 2Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
  • 3Faculdade de Farmácia, Universidade de Lisboa, Portugal

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.

Keywords: Medication adherence (MeSH), community pharmacy, intervention - behavioral, Bid data, Dispensing records

Received: 03 Aug 2018; Accepted: 05 Feb 2019.

Edited by:

Kurt E. Hersberger, Universität Basel, Switzerland

Reviewed by:

Maria M. Salazar-Bookaman, Central University of Venezuela, Venezuela
Marc H. De Longueville, UCB Pharma (Belgium), Belgium  

Copyright: © 2019 Torres-Robles, Wiecek, Cutler, Drake, Benrimoj, Fernandez-Llimos and Garcia-Cardenas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Victoria Garcia-Cardenas, Graduate School of Health, University of Technology Sydney, Sydney, Australia, victoria.garciacardenas@uts.edu.au