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Related Experiment Videos

Automated quality checks on repeat prescribing.

Jeremy E Rogers1, Christopher J Wroe, Angus Roberts

  • 1Medical Informatics Group, Department of Computer Science, Oxford Road, University of Manchester, Manchester M13 9PL. jeremy@cs.man.ac.uk

The British Journal of General Practice : the Journal of the Royal College of General Practitioners
|January 2, 2004
PubMed
Summary
This summary is machine-generated.

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Computerising repeat prescription checks in UK primary care is feasible but requires improved electronic health record data consistency. The software identified issues, but alert accuracy was limited by data quality and the system's inability to infer diagnoses.

Area of Science:

  • Health Informatics
  • Primary Care Medicine
  • Clinical Decision Support Systems

Background:

  • Periodic review of repeat prescriptions is essential for good clinical practice in primary care.
  • Manual checking of all repeat prescriptions against quality markers is impractical.
  • Computerising these quality checks on electronic patient records (EPRs) offers a potential solution.

Discussion:

  • A software tool was tested in UK primary care to automate repeat prescribing quality checks.
  • The system flagged repeat prescriptions lacking a valid indication in the EPR.
  • Clinician feedback indicated support for the software's objectives.

Key Insights:

  • 14.8% of repeat prescriptions lacked a valid indication in the EPR.
  • The software generated a high rate of incorrect alerts (62%).

Related Experiment Videos

  • Alert inaccuracies stemmed from the drug information resource, clinical coding, use of the British National Formulary (BNF) as a gold standard, and the system's inability to infer diagnoses.

    • Outlook:

    • Improving the accuracy of automated prescribing checks requires more consistent electronic clinical data collection.
    • Future systems need to reconcile the need for clinicians to infer 'obvious' diagnoses with machine limitations.
    • Enhanced EPR data quality is crucial for effective secondary decision support.