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Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study
  1. Pascale Carayon1,
  2. Peter Hoonakker2,
  3. Ann Schoofs Hundt2,
  4. Megan Salwei1,
  5. Douglas Wiegmann1,
  6. Roger L Brown3,
  7. Peter Kleinschmidt4,
  8. Clair Novak5,
  9. Michael Pulia6,
  10. Yudi Wang4,
  11. Emily Wirkus7,
  12. Brian Patterson6
  1. 1 Department of Industrial and Systems Engineering, Wisconsin Institute for Healthcare Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
  2. 2 Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, Wisconsin, USA
  3. 3 School of Nursing, University of Wisconsin-Madison, Madison, Wisconsin, USA
  4. 4 Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
  5. 5 UW Health, Madison, Wisconsin, USA
  6. 6 Department of Emergency Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
  7. 7 Department of Population Health Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
  1. Correspondence to Dr Pascale Carayon, Department of Industrial and Systems Engineering, Wisconsin Institute for Healthcare Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; pcarayon{at}


Objective In this study, we used human factors (HF) methods and principles to design a clinical decision support (CDS) that provides cognitive support to the pulmonary embolism (PE) diagnostic decision-making process in the emergency department. We hypothesised that the application of HF methods and principles will produce a more usable CDS that improves PE diagnostic decision-making, in particular decision about appropriate clinical pathway.

Materials and methods We conducted a scenario-based simulation study to compare a HF-based CDS (the so-called CDS for PE diagnosis (PE-Dx CDS)) with a web-based CDS (MDCalc); 32 emergency physicians performed various tasks using both CDS. PE-Dx integrated HF design principles such as automating information acquisition and analysis, and minimising workload. We assessed all three dimensions of usability using both objective and subjective measures: effectiveness (eg, appropriate decision regarding the PE diagnostic pathway), efficiency (eg, time spent, perceived workload) and satisfaction (perceived usability of CDS).

Results Emergency physicians made more appropriate diagnostic decisions (94% with PE-Dx; 84% with web-based CDS; p<0.01) and performed experimental tasks faster with the PE-Dx CDS (on average 96 s per scenario with PE-Dx; 117 s with web-based CDS; p<0.001). They also reported lower workload (p<0.001) and higher satisfaction (p<0.001) with PE-Dx.

Conclusions This simulation study shows that HF methods and principles can improve usability of CDS and diagnostic decision-making. Aspects of the HF-based CDS that provided cognitive support to emergency physicians and improved diagnostic performance included automation of information acquisition (eg, auto-populating risk scoring algorithms), minimisation of workload and support of decision selection (eg, recommending a clinical pathway). These HF design principles can be applied to the design of other CDS technologies to improve diagnostic safety.

  • diagnostic errors
  • human factors
  • decision support, clinical
  • emergency department
  • decision making

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  • Correction notice The article has been corrected since it was published online first. Some minor changes have been done in table 3.

  • Contributors PC, ASH, PH, BP and DW designed the study. All authors were involved in the human-centered design process, data collection and data analysis. All authors reviewed the manuscript before submission.

  • Funding This research was made possible by funding from the Agency for Healthcare Research and Quality (AHRQ), Grant Numbers: R01HS022086-Principal Investigator: Pascale Carayon, and K08HS024558-Principal Investigator: Brian Patterson; and was supported by the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS), Grant Number: 1UL1TR002373. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ or NIH.

  • Competing interests None declared.

  • Patient consent for publication Not required.

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

  • Data availability statement No data are available.