title: EMR-based medication adherence metric markedly enhances identification of nonadherent patients.
abstract: 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.
  
  
  
  Retrospective database analysis of all health plan members prescribed a statin in 2008 and followed through 2010.
  
  
  
  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.
  
  
  
  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.
  
  
  
  Addition of written-prescription data to adherence measures identified nearly twice the number of nonadherent patients and markedly improved prediction of changes in LDL.
authors.affiliation: Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel. roee.singer@mail.huji.ac.il
authors.forename: Shepherd Roee
authors.initials: SR
authors.lastname: Singer
authors.forename: Moshe
authors.initials: M
authors.lastname: Hoshen
authors.forename: Efrat
authors.initials: E
authors.lastname: Shadmi
authors.forename: Morton
authors.initials: M
authors.lastname: Leibowitz
authors.forename: Natalie
authors.initials: N
authors.lastname: Flaks-Manov
authors.forename: Haim
authors.initials: H
authors.lastname: Bitterman
authors.forename: Ran D
authors.initials: RD
authors.lastname: Balicer
mesh_terms: D008078:Cholesterol, LDL / Q000097:blood; D011307:Drug Prescriptions / Q000706:statistics & numerical data; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012189:Retrospective Studies; D016903:Drug Monitoring
chemical_list: D008078:Cholesterol, LDL; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors