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Medication related computerized decision support system (CDSS): make it a clinicians' partner!

Romaric Marcilly1, Nicolas Leroy, Michel Luyckx

  • 1INSERM CIC-IT, Lille, CHU Lille; UDSL EA 2694; Univ Lille Nord de France; F-59000 Lille, France. romaric.marcilly@univ-lille2.fr

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Computerized Decision Support Systems (CDSS) can prevent adverse drug events but face usability challenges. A human factors approach and a new model improve CDSS integration and context awareness, enhancing clinical workflow and reducing errors.

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Area of Science:

  • Health Informatics
  • Human Factors Engineering
  • Clinical Decision Support

Background:

  • Medication-related Computerized Decision Support Systems (CDSS) show promise in preventing Adverse Drug Events (ADE).
  • However, widespread adoption is hindered by issues such as over-alerting and poor usability within Electronic Health Record (EHR) and Computerized Physician Order Entry (CPOE) systems.
  • Existing systems often fail to adequately integrate into clinical workflows and consider user cognitive processes.

Purpose of the Study:

  • To apply a human factors approach to design and integrate Clinical Decision Support (CDS) functions into EHR/CPOE systems.
  • To develop a model that enhances CDSS by incorporating patient monitoring and clinical context for ADE risk assessment.
  • To improve CDSS acceptance and effectiveness by aligning with user needs and clinical workflows.

Main Methods:

  • Employed ethnographic observations and semi-structured interviews to analyze existing clinical work situations and processes.
  • Utilized the SHEL (Software, Hardware, Environment & Liveware) formalism for structured description of the work system and human error classification.
  • Proposed a Unified Modelling Language (UML) model to characterize drug monitoring and patient clinical context relevant to ADE risk.

Main Results:

  • The proposed UML model effectively integrates lab test orders, validity, and normality of results (e.g., kalemia for potassium).
  • The model captures crucial monitoring context (e.g., drug-specific labs) and clinical context (e.g., renal insufficiency, patient's potassium levels).
  • This context-aware approach enables CDSS to recognize ongoing healthcare actions and adapt information delivery.

Conclusions:

  • Designing CDSS with a human factors approach and incorporating contextual information significantly improves usability and workflow integration.
  • The developed UML model allows CDSS to adapt alerts and information based on real-time monitoring and patient clinical status.
  • This makes CDSS a more effective partner for clinicians, nurses, and pharmacists, ultimately enhancing ADE prevention and patient safety.