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EMR-based medication adherence metric markedly enhances identification of nonadherent patients.

Authors :
Singer SR
Hoshen M
Shadmi E
Leibowitz M
Flaks-Manov N
Bitterman H
Balicer RD
Source :
The American journal of managed care [Am J Manag Care] 2012 Oct 01; Vol. 18 (10), pp. e372-7. Date of Electronic Publication: 2012 Oct 01.
Publication Year :
2012

Abstract

Objectives: To determine whether addition of written-prescription data to existing adherence measures improves identification of nonadherent patients and prediction of changes in low-density lipoprotein (LDL) cholesterol.<br />Study Design: Retrospective database analysis of all health plan members prescribed a statin in 2008 and followed through 2010.<br />Methods: We examined statin use in a 4-millionmember health plan with 100% electronic medical record coverage. A novel type of medication possession ratio (MPR), integrating prescribed with dispensed medication data, was developed. This measure, MPRp, was compared with a standard dispensed-only adherence measure, MPRd. Adherence below 20% was considered nonadherence. The 2 adherence measures were compared regarding (1) the number of patients identified as nonadherent, (2) percent changes in LDL from study enrollment to study termination, and (3) receiver-operator curves assessing the association between adherence and a 24% decrease in LDL.<br />Results: A total of 67,517 patients received 1,386,270 written prescriptions over the 3-year period. MPRp identified 93% more patients as nonadherent than did MPRd (P <.001). These newly identified patients exhibited minimal LDL decreases over the course of the study. Adherence by MPRp was more strongly associated with decreases in LDL than was adherence by MPRd (area under the curve 0.815 vs 0.770; P <.001). During the study period, 18.2% of patients did not fill any prescriptions and were thus unidentifiable by dispensed-only measures.<br />Conclusions: Addition of written-prescription data to adherence measures identified nearly twice the number of nonadherent patients and markedly improved prediction of changes in LDL.

Details

Language :
English
ISSN :
1936-2692
Volume :
18
Issue :
10
Database :
MEDLINE
Journal :
The American journal of managed care
Publication Type :
Academic Journal
Accession number :
23145845