Medication Review: Human Factors Study Aiming at Helping an Acute Geriatric Unit to Sustain and Systematize the

Clément Wawrzyniak1, Marie-Catherine Beuscart-Zephir1, Romaric Marcilly1

  • 1INSERM CIC-IT 1403; CHU Lille; F-59000 Lille, France.

Abstract

Insights

Medication Review (MRev) in an Acute Geriatric Unit (AGU) proved effective despite lacking Computerized Physician Order Entry (CPOE) and sufficient staff. Recommendations focus on role-specific training and process improvements for sustained patient safety.

Area of Science:

  • Geriatric Medicine
  • Health Systems Pharmacy
  • Human Factors Engineering

Background:

  • Medication Review (MRev) is crucial for patient safety but challenging to implement without key resources.
  • This study examines an MRev process in an Acute Geriatric Unit (AGU) lacking Computerized Physician Order Entry (CPOE) and adequate staffing.
  • The AGU's MRev process faced implementation hurdles due to resource limitations.

Purpose of the Study:

  • To describe the existing MRev process in the AGU, focusing on the preparatory meeting phase.
  • To identify challenges and propose recommendations for maintaining and optimizing the MRev process.
  • To analyze the effectiveness of MRev under suboptimal conditions.

Main Methods:

  • Human Factors experts conducted data collection from April to October 2014 at Lille University Hospital.
  • Methods included interviews, shadowing observations, and video recording of the MRev process.
  • Analysis focused on actors, tools, outcomes, and difficulties within the MRev phases.

Main Results:

  • The MRev process involves three phases: meeting preparation, the MRev meeting, and patient discharge.
  • Seven main tasks were identified, with actors, supports, outcomes, and difficulties documented for each.
  • While a full-time pharmacist would be ideal, practical recommendations include targeted training and process enhancements.

Conclusions:

  • The AGU demonstrated an efficient Medication Review process despite operating under less than ideal conditions.
  • Well-designed tools and established processes contributed to the effectiveness of the MRev.
  • Optimizing actor training and refining specific task processes are key recommendations for sustained MRev success.

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