Participatory design of probability-based decision support tools for in-hospital nurses

J Am Med Inform Assoc. 2017 Nov 1;24(6):1102-1110. doi: 10.1093/jamia/ocx060.

Abstract

Objective: To describe nurses' preferences for the design of a probability-based clinical decision support (PB-CDS) tool for in-hospital clinical deterioration.

Methods: A convenience sample of bedside nurses, charge nurses, and rapid response nurses (n = 20) from adult and pediatric hospitals completed participatory design sessions with researchers in a simulation laboratory to elicit preferred design considerations for a PB-CDS tool. Following theme-based content analysis, we shared findings with user interface designers and created a low-fidelity prototype.

Results: Three major themes and several considerations for design elements of a PB-CDS tool surfaced from end users. Themes focused on "painting a picture" of the patient condition over time, promoting empowerment, and aligning probability information with what a nurse already believes about the patient. The most notable design element consideration included visualizing a temporal trend of the predicted probability of the outcome along with user-selected overlapping depictions of vital signs, laboratory values, and outcome-related treatments and interventions. Participants expressed that the prototype adequately operationalized requests from the design sessions.

Conclusions: Participatory design served as a valuable method in taking the first step toward developing PB-CDS tools for nurses. This information about preferred design elements of tools that support, rather than interrupt, nurses' cognitive workflows can benefit future studies in this field as well as nurses' practice.

Keywords: clinical decision-making; computer-assisted; decision support systems; models; nursing; statistical.

MeSH terms

  • Clinical Decision-Making*
  • Decision Support Systems, Clinical*
  • Decision Support Techniques*
  • Humans
  • Models, Statistical
  • Nursing Methodology Research
  • Nursing Staff, Hospital*
  • Patient Simulation
  • Probability
  • User-Computer Interface