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An algorithm to identify medication nonpersistence using electronic pharmacy databases.

Melissa M Parker1, Howard H Moffet2, Alyce Adams2

  • 1Kaiser Permanente, Division of Research, Oakland, California, USA Melissa.parker@kp.org.

Journal of the American Medical Informatics Association : JAMIA
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PubMed
Summary

A new algorithm accurately identifies medication nonpersistence in patients using electronic pharmacy records. This method helps healthcare systems better understand medication underutilization and improve patient care.

Keywords:
computerized medical record systemsdiabetes mellituselectronic health recordmedication adherencemedication nonpersistence

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Area of Science:

  • Health Informatics
  • Pharmacoeconomics
  • Clinical Pharmacy

Background:

  • Identifying patients who are medication nonpersistent is crucial for healthcare operations and research.
  • Current methods for detecting nonpersistence using electronic pharmacy records lack consistency.
  • A validated algorithm is needed to accurately identify medication nonpersistence in chronically used medications.

Purpose of the Study:

  • To develop and validate a novel algorithm for detecting medication nonpersistence.
  • To assess the algorithm's performance across various grace periods and stockpile considerations.
  • To provide a consistent method for identifying medication nonpersistence in electronic pharmacy records.

Main Methods:

  • The study analyzed refill patterns of 14,349 adult diabetes patients on cardiometabolic therapies.
  • Various grace periods (30-300 days) were evaluated to define medication nonpersistence.
  • Nonpersistence rates were compared between algorithms that accounted for and ignored medication stockpiles.

Main Results:

  • The novel nonpersistence algorithm yielded consistent results regardless of stockpile data when grace periods were at least 100 days.
  • Agreement between algorithms with and without stockpile considerations improved with longer grace periods (Kappa coefficients from 0.63 to 0.98).
  • Longer grace periods enhance the reliability of nonpersistence detection.

Conclusions:

  • The developed algorithm is useful for healthcare operations and research in both new and ongoing patient cohorts.
  • This method identifies patients with inadequate medication-taking behavior missed by other adherence measures.
  • Comprehensive identification of medication underutilization requires assessing primary nonadherence, secondary nonadherence, and nonpersistence.