Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy
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 o...
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doaj-4a2d20817ba4489fa54a6270b30d9f532020-11-24T23:48:13ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122019-02-011010.3389/fphar.2019.00130417766Using Dispensing Data to Evaluate Adherence Implementation Rates in Community PharmacyAndrea Torres-Robles0Elyssa Wiecek1Rachelle Cutler2Barry Drake3Shalom I. Benrimoj4Fernando Fernandez-Llimos5Victoria Garcia-Cardenas6Graduate School of Health, University of Technology Sydney, Sydney, NSW, AustraliaGraduate School of Health, University of Technology Sydney, Sydney, NSW, AustraliaGraduate School of Health, University of Technology Sydney, Sydney, NSW, AustraliaFaculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, AustraliaGraduate School of Health, University of Technology Sydney, Sydney, NSW, AustraliaDepartment of Social Pharmacy, Faculty of Pharmacy, University of Lisbon, Lisbon, PortugalGraduate School of Health, University of Technology Sydney, Sydney, NSW, AustraliaBackground: 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.https://www.frontiersin.org/article/10.3389/fphar.2019.00130/fullbig databasedispensing recordsmedication adherencecommunity pharmacyadherence implementation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Andrea Torres-Robles Elyssa Wiecek Rachelle Cutler Barry Drake Shalom I. Benrimoj Fernando Fernandez-Llimos Victoria Garcia-Cardenas |
spellingShingle |
Andrea Torres-Robles Elyssa Wiecek Rachelle Cutler Barry Drake Shalom I. Benrimoj Fernando Fernandez-Llimos Victoria Garcia-Cardenas Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy Frontiers in Pharmacology big database dispensing records medication adherence community pharmacy adherence implementation |
author_facet |
Andrea Torres-Robles Elyssa Wiecek Rachelle Cutler Barry Drake Shalom I. Benrimoj Fernando Fernandez-Llimos Victoria Garcia-Cardenas |
author_sort |
Andrea Torres-Robles |
title |
Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy |
title_short |
Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy |
title_full |
Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy |
title_fullStr |
Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy |
title_full_unstemmed |
Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy |
title_sort |
using dispensing data to evaluate adherence implementation rates in community pharmacy |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Pharmacology |
issn |
1663-9812 |
publishDate |
2019-02-01 |
description |
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. |
topic |
big database dispensing records medication adherence community pharmacy adherence implementation |
url |
https://www.frontiersin.org/article/10.3389/fphar.2019.00130/full |
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