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Optimizing a decision support system for damage-control resuscitation using mixed methods human factors analysis.

Daniela Schmulevich1, Pamela Z Cacchione, Sara Holland

  • 1From the Division of Traumatology, Surgical Critical Care & Emergency Surgery (D.S., K.Q., J.W.C.), Penn Acute Research Collaboration (PARC) (D.S., B.S.A., J.W.C.), Perelman School of Medicine at the University of Pennsylvania; Department of Nursing (P.Z.C., S.H., A.H.), Penn Presbyterian Medical Center, Penn Medicine; University of Pennsylvania School of Nursing Philadelphia (P.Z.C.); Leonard Davis Institute of Health Economics (P.Z.C., J.W.C.), University of Pennsylvania, Philadelphia, Pennsylvania; Arcos, Inc. (C.M.), Missouri City, Texas; Department of Emergency Medicine (B.S.A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; and Department of Surgery (J.W.C.), Uniformed Services University of the Health Sciences, Bethesda, Maryland.

The Journal of Trauma and Acute Care Surgery
|April 14, 2021
PubMed
Summary
This summary is machine-generated.

A new clinical decision support system was developed to improve adherence to damage-control resuscitation (DCR) principles in trauma care. Human factors testing confirmed its usability and integration into clinical workflows, paving the way for pilot studies.

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Area of Science:

  • Trauma Care
  • Clinical Decision Support Systems
  • Human Factors Engineering

Background:

  • Damage-control resuscitation (DCR) is vital for trauma survival but faces adherence challenges across care phases.
  • Clinical decision support systems (CDSS) offer a potential solution to enhance DCR adherence.
  • This study focused on developing and evaluating a CDSS for DCR using an iterative, human factors-centered approach.

Purpose of the Study:

  • To design and evaluate a novel decision support system for damage-control resuscitation.
  • To assess the system's usability and integration into clinical trauma care workflows.
  • To refine the system through iterative development and human factors testing.

Main Methods:

  • Employed an iterative development process with human factors testing across three phases: needs assessment/prototype design, simulated resuscitations, and initial clinical use.
  • Involved multidisciplinary trauma teams, including surgeons, anesthesia providers, and nurses, in providing feedback.
  • Utilized qualitative and quantitative data, including surveys and Likert scale feedback on system performance and workflow integration.

Main Results:

  • The decision support system was perceived as usable and well-integrated into clinical workflows by trauma team members.
  • Phase 1 simulations showed high task completion rates (78.1%) with the system.
  • Phase 2 real-world evaluations confirmed ease of use (median Likert score 4) and workflow integration (median Likert score 3).

Conclusions:

  • An iterative development and human factors testing approach successfully yielded a clinically usable DCR decision support system.
  • The developed system demonstrates potential for improving adherence to DCR principles in trauma care.
  • Further studies are warranted to determine the system's applicability in military and civilian trauma settings.