Original Report

Instantaneous detection of nonadherence: quality, strength, and weakness of an electronic prescription database

  1. P. Harbig1,*,
  2. I. Barat1,
  3. P. Lund Nielsen2,
  4. E. M. Damsgaard1

Article first published online: 4 JAN 2012

DOI: 10.1002/pds.2351

Issue

Pharmacoepidemiology and Drug Safety

Pharmacoepidemiology and Drug Safety

Volume 21, Issue 3, pages 323–328, March 2012

Additional Information

How to Cite

Harbig, P., Barat, I., Lund Nielsen, P. and Damsgaard, E. M. (2012), Instantaneous detection of nonadherence: quality, strength, and weakness of an electronic prescription database. Pharmacoepidem. Drug Safe., 21: 323–328. doi: 10.1002/pds.2351

Author Information

  1. 1

    Department of Medicine/Geriatrics, Aarhus University Hospital, Aarhus, Denmark

  2. 2

    Vejlby Pharmacy, Risskov, Denmark

*P. Harbig, Department of Medicine/Geriatrics, Research Division G, Aarhus University Hospital, Tage Hansens Gade 2, Building 3C, DK-8000 Aarhus C, Denmark. E-mail: philharbig@gmail.com

Publication History

  1. Issue published online: 8 MAR 2012
  2. Article first published online: 4 JAN 2012
  3. Manuscript Accepted: 15 NOV 2011
  4. Manuscript Revised: 14 NOV 2011
  5. Manuscript Received: 19 MAY 2011

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Keywords:

  • adherence;
  • prescription;
  • database;
  • medication;
  • PEM;
  • elderly

ABSTRACT

Background

The “personal electronic medicine profile” (PEM) is a Web-based tool for electronic prescription and monitoring of purchased medicine. It is based on the National Prescription Database and contains data on all prescriptions in Denmark. It includes information on time of drug purchase, number of tablets, and prescribed daily dosage. This allows calculation of the expected time for new purchases.

Purpose

To study the accuracy of the PEM as a tool for monitoring drug nonadherence as compared with pill counts (PCs).

Methods

Five hundred eighty-three randomly selected elderly Danish citizens older than 65 years taking more than four drugs were studied. They were visited three times by a nurse who counted their medicine supply. Contingency table analysis was used to compare drug nonadherence calculated from PC with that revealed by PEM. For PC and PEM, an adherence level of at least 80% was defined as acceptable.

Results

PEM could not accurately process (non)adherence in 44% of all drugs. The probability of identifying drug nonadherence with PEM was low (negative predictive value 23%). Incomplete prescription information (34%) and inaccurate dosage registration (10%) were the major sources of error.

Conclusion

PEM is inferior to PC for accurate monitoring of drug nonadherence. The inaccuracy is due to erroneous prescription information. PEM could be a powerful tool for electronic monitoring of drug nonadherence if prescription information was recorded uniformly and correctly. To increase the accuracy, we recommend informal free-text dosing instruction to be translated into a formal one by use of appropriate software such as library of phrases. Copyright © 2012 John Wiley & Sons, Ltd.

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