Journal of General Internal Medicine

, Volume 25, Issue 4, pp 284–290 | Cite as

Primary Medication Non-Adherence: Analysis of 195,930 Electronic Prescriptions

  • Michael A. FischerEmail author
  • Margaret R. Stedman
  • Joyce Lii
  • Christine Vogeli
  • William H. Shrank
  • M. Alan Brookhart
  • Joel S. Weissman
Original Article



Non-adherence to essential medications represents an important public health problem. Little is known about the frequency with which patients fail to fill prescriptions when new medications are started (“primary non-adherence”) or predictors of failure to fill.


Evaluate primary non-adherence in community-based practices and identify predictors of non-adherence.


75,589 patients treated by 1,217 prescribers in the first year of a community-based e-prescribing initiative.


We compiled all e-prescriptions written over a 12-month period and used filled claims to identify filled prescriptions. We calculated primary adherence and non-adherence rates for all e-prescriptions and for new medication starts and compared the rates across patient and medication characteristics. Using multivariable regressions analyses, we examined which characteristics were associated with non-adherence.


Primary medication non-adherence.


Of 195,930 e-prescriptions, 151,837 (78%) were filled. Of 82,245 e-prescriptions for new medications, 58,984 (72%) were filled. Primary adherence rates were higher for prescriptions written by primary care specialists, especially pediatricians (84%). Patients aged 18 and younger filled prescriptions at the highest rate (87%). In multivariate analyses, medication class was the strongest predictor of adherence, and non-adherence was common for newly prescribed medications treating chronic conditions such as hypertension (28.4%), hyperlipidemia (28.2%), and diabetes (31.4%).


Many e-prescriptions were not filled. Previous studies of medication non-adherence failed to capture these prescriptions. Efforts to increase primary adherence could dramatically improve the effectiveness of medication therapy. Interventions that target specific medication classes may be most effective.


adherence non-adherence electronic prescribing health information technology 



We acknowledge the assistance of BCBSMA, Tufts HP, and ZixCorp in providing data. The investigators retained control over all aspects of the analyses and presentation of results.

The research was supported by AHRQ grant R01 HS15175.

Dr. Brookhart is supported by a career development grant from NIH (AG12084).

Dr. Shrank is supported by a career development grant from NIH (HL090505).

Conflict of Interest

None disclosed.

Supplementary material

11606_2010_1253_MOESM1_ESM.doc (43 kb)
ESM 1 (DOC 43 kb)


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Copyright information

© Society of General Internal Medicine 2010

Authors and Affiliations

  • Michael A. Fischer
    • 1
    Email author
  • Margaret R. Stedman
    • 1
  • Joyce Lii
    • 1
  • Christine Vogeli
    • 2
  • William H. Shrank
    • 1
  • M. Alan Brookhart
    • 1
  • Joel S. Weissman
    • 2
  1. 1.Division of Pharmacoepidemiology and PharmacoeconomicsBrigham and Women’s HospitalBostonUSA
  2. 2.Institute for Health PolicyMassachusetts General HospitalBostonUSA

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