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Development of a computerised alert system, ADEAS, to identify patients at risk for an adverse drug event
  1. M K Rommers1,
  2. M H Zegers1,
  3. P A De Clercq2,
  4. M L Bouvy3,
  5. P H E M de Meijer4,
  6. I M Teepe-Twiss1,
  7. H-J Guchelaar1
  1. 1Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands
  2. 2Medecs, Eindhoven, The Netherlands
  3. 3SIR Institute for Pharmacy Practice and Policy, Utrecht/Leiden, The Netherlands
  4. 4Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
  1. Correspondence to Mirjam K Rommers, Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands; m.k.rommers{at}lumc.nl

Abstract

Introduction Adverse drug events (ADEs) are frequent and pose an important risk for patients treated with drugs. Fortunately, a substantial part of ADEs is preventable, and computerised physician order entry with a sophisticated clinical decision support system may be used to reach this goal.

Objective To develop a new automated system that could improve the quality of medication surveillance. The system should focus on detecting patients at risk for an ADE by combining data from the hospital information system and computerised physician order entry (drug prescription data, drug–drug interaction alerts, clinical chemical laboratory parameters, demographic features), using clinical rules.

Methods The clinical rules were formulated in a multidisciplinary team, based on seven risk categories. The new system was composed in a guideline-based decision support framework consisting of both a guideline development module and a decision support module. A total of 121 clinical rules were built into the system. Validation of the system and a proof of principle test were performed.

Results The adverse drug event alerting system (ADEAS) was developed and validated successfully. The proof of principle test showed that ADEAS has potential clinical usefulness. ADEAS generated alerts and detected additional potential risk situations, which were not generated by the conventional medication surveillance.

Conclusion We developed a pharmacy decision support system ADEAS that focusses on the detection of situations prone to lead to an ADE and might help clinicians to take timely corrective interventions and thereby can prevent patient harm.

  • Adverse drug events
  • clinical decision support system
  • computerised physician order entry
  • clinical rules
  • computerised alert system
  • hospital pharmacy
  • combining drug and laboratory data
  • medication safety

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Footnotes

  • Funding This study was financially supported by the Dutch Society of Hospital Pharmacists (NVZA), the Dutch Society of Hospital Physicians (OMS) and the Dutch Ministry of Health, Welfare and Sports.

  • Competing interests None.

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

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