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Developing a decision rule to optimise clinical pharmacist resources for medication reconciliation in the emergency department
  1. Sabrina De Winter1,
  2. Peter Vanbrabant2,
  3. Pieter Laeremans3,
  4. Veerle Foulon4,
  5. Ludo Willems1,
  6. Sandra Verelst5,
  7. Isabel Spriet1
  1. 1 Department of Pharmaceutical and Pharmacological Sciences, Clinical Pharmacology and Pharmacotherapy, KU Leuven, University of Leuven, University Hospitals Leuven, Leuven, Belgium
  2. 2 Department of General Internal Medicine, University Hospitals Leuven, Leuven, Belgium
  3. 3 Department of Information Technology, University Hospitals Leuven, Leuven, Belgium
  4. 4 Department of Pharmaceutical and Pharmacological Sciences, Clinical Pharmacology and Pharmacotherapy, KU Leuven, University of Leuven, Leuven, Belgium
  5. 5 Department of Emergency Medicine, University Hospitals Leuven, Leuven, Belgium
  1. Correspondence to Dr Sabrina De Winter, Department of Pharmaceutical and Pharmacological Sciences, Clinical Pharmacology and Pharmacotherapy, KU Leuven, University of Leuven, University Hospitals Leuven, Herestraat 49, B-3000 Leuven, Belgium; sabrina.dewinter{at}uzleuven.be

Abstract

Background The process of obtaining a complete medication history for patients admitted to the hospital from the ED at hospital admission, without discrepancies, is error prone and time consuming.

Objectives The goal of this study was the development of a clinical decision rule (CDR) with a high positive predictive value in detecting ED patients admitted to hospital at risk of at least one discrepancy during regular medication history acquisition, along with favourable feasibility considering time and budget constraints.

Methods Data were based on a previous prospective study conducted at the ED in Belgium, describing discrepancies in 3592 medication histories. Data were split into a training and a validation set. A model predicting the number of discrepancies was derived from the training set with negative binomial regression and was validated on the validation set. The performance of the model was assessed. Several CDRs were constructed and evaluated on positive predictive value and alert rate.

Results The following variables were retained in the prediction model: (1) age, (2) gender, (3) medical discipline for which the patient was admitted, (4) degree of physician training, (5) season of admission, (6) type of care before admission, number of (7) drugs, (8) high-risk drugs, (9) drugs acting on alimentary tract and metabolism, (10) antithrombotics, antihaemorrhagics and antianaemic preparations, (11) cardiovascular drugs, (12) drugs acting on musculoskeletal system and (13) drugs acting on the nervous system; all recorded by the ED physician on admission. The final CDR resulted in an alert rate of 29% with a positive predictive value of 74%.

Conclusion The final CDR allows identification of the majority of patients with a potential discrepancy within a feasible workload for the pharmacy staff. Our CDR is a first step towards a rule that could be incorporated into electronic medical records or a scoring system.

  • medication reconciliation – emergency service
  • hospital – clinical pharmacy service – clinical prediction rule – medication systems
  • hospital

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Footnotes

  • Contributors SDW: conception and design, data acquisition, analysis, interpretation of data, drafting of the manuscript. PVB, LW, IS: conception and design, interpretation of data, critical revision of manuscript. PL: design, analysis, interpretation of data, critical revision of manuscript. VF, SV: data acquisition, interpretation of data and critical revision of manuscript.

  • Competing interests None declared.

  • Ethics approval No additional patients were included in this study. We developed a clinical decision rule using data from a previously published study for which we had approval of the Institutional Board.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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