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. 2011 Sep-Oct;18(5):717-20.
doi: 10.1136/amiajnl-2011-000151. Epub 2011 May 25.

Improving the validity of determining medication adherence from electronic health record medications orders

Nikki M Carroll  1 , Jennifer L Ellis, Capp F Luckett, Marsha A Raebel
Affiliations

Improving the validity of determining medication adherence from electronic health record medications orders

Nikki M Carroll et al. J Am Med Inform Assoc. 2011 Sep-Oct.

Erratum in

  • Erratum.
    [No authors listed] [No authors listed] J Am Med Inform Assoc. 2017 Apr 1;24(e1):e216. doi: 10.1093/jamia/ocw147. J Am Med Inform Assoc. 2017. PMID: 28395060 Free PMC article. No abstract available.

Abstract

We developed an accurate and valid medication order algorithm to identify from electronic health records the definitive medication order intended for dispensing and applied this process to identify a cohort of patients and to stratify them into one of three medication adherence groups: early non-persistence, primary non-adherence, or ongoing adherence. We identified medication order data from electronic health record tables, obtained the orders, and linked the orders to dispensings. These steps were then used to identify patients newly prescribed antihypertensive, antidiabetic, or antihyperlipidemic medications and to determine the adherence group of each patient. Record review validated each process step, thus increasing the accuracy of group assignment as well as the criteria used to select patients. This work is an important first step to accurately identify study-specific patient adherence cohorts and allow more comprehensive estimates of population medication adherence.

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Conflict of interest statement

Competing interests: None.

Figures

Figure 1
Figure 1
Electronic health record schema to identify medication order data.
Figure 2
Figure 2
Selection of patients for adherence cohorts. EHR, electronic health record; KPCO, Kaiser Permanente Colorado; Rx, prescription.
See this image and copyright information in PMC

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