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A rules based algorithm to generate problem lists using emergency department medication reconciliation.

Joshua W Joseph1, David T Chiu1, Larry A Nathanson1

  • 1Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, One Deaconess Road, 2nd Floor, Boston, MA 02215, United States.

International Journal of Medical Informatics
|August 31, 2016
PubMed
Summary
This summary is machine-generated.

An automated algorithm demonstrated higher sensitivity in identifying patient conditions from emergency department medication reconciliation compared to clinician-generated lists. Further research is needed for complex conditions.

Keywords:
Clinical decision supportEmergency medicineMeaningful useQuality assurance

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Area of Science:

  • Medical Informatics
  • Clinical Decision Support

Background:

  • Accurate patient problem lists are crucial for effective care transitions.
  • Manual problem list generation can be time-consuming and prone to omissions.

Purpose of the Study:

  • To evaluate the sensitivity and specificity of an automated problem list generated from emergency department (ED) medication reconciliation.

Main Methods:

  • A retrospective cohort study of 603 patients admitted via the ED.
  • An algorithm grouped medications using First Databank ontology and applied clinical rules to predict five conditions.
  • Predictions were compared against existing electronic health record (EHR) and attending physician problem lists.

Main Results:

  • The algorithm showed superior sensitivity for all five conditions (obstructive lung disease, hypertension, diabetes, thromboembolism risk, congestive heart failure) compared to both attending and EHR problem lists.
  • The attending and EHR problem lists demonstrated higher specificity for hypertension and congestive heart failure.

Conclusions:

  • Automated algorithms can enhance sensitivity for identifying critical conditions from medication data.
  • Further investigation is needed to improve specificity for complex conditions and incorporate diverse data sources.