Cognitive Engineering for Complex Decisionmaking & Problem Solving in Acute Care (Maryland)

Project Final Report (PDF, 366.73 KB) Disclaimer

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Cognitive Engineering for Complex Decisionmaking & Problem Solving in Acute Care - Final Report

Citation:
Hettinger A. Cognitive Engineering for Complex Decisionmaking & Problem Solving in Acute Care - Final Report. (Prepared by MedStar Health Research Institute under Grant No. R01 HS022542). Rockville, MD: Agency for Healthcare Research and Quality, 2020. (PDF, 366.73 KB)

The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
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A National Web Conference on Clinical Decision Support Efforts that Assist Clinical Cognitive Processes

Event Details

  • Date: October 19, 2021
  • Time: 1:00pm to 2:30pm

AHRQ is hosting a Web conference during which panelists will describe their research efforts and methods to align the design and implementation of clinical decision support (CDS) tools that support the cognitive needs of the clinician.

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The use of cognitive engineering systems methodology helps to better understand the interactions of the cognitive and workflow processes of frontline emergency medicine providers by affording new strategies for the design of health information technology (IT) solutions. These strategies not only strive to improve the effectiveness of clinical work in high-intensity medical environments, but also provide a guide to improve health IT implementations in the future.

Project Details - Ended

Summary:

Healthcare is a complex, high-risk, sociotechnical work environment. As complexity increases and technology advances, there is an increasing need for “intelligent” design of health information technology (IT) to better support the work of healthcare providers. An improved understanding of how to effectively integrate these systems into the workflow of clinical providers and non-clinical staff is critical to fully utilize health IT’s potential. Future designs for health IT systems must support healthcare providers’ cognitive work, workflow, and decision-making needs, rather than requiring them to adapt their cognitive work and workflow to meet the requirements of the system.

The key to successful use of health IT is presenting the right information at the right time, visualized in a format that facilitates insight into patterns and management strategies that will help the user carry out work effectively and safely. This study applied cognitive systems engineering (CSE) to understand and support complex cognition and work activities in the emergency department (ED). The ED was an ideal setting for this research because it has some of the most challenging conditions for cognitive work—including high risk, time pressure, and uncertainty—and, therefore, provided findings that can be generalized to other complex healthcare environments.

The specific aims of this study were as follows:

  • Perform a cognitive engineering analysis of emergency medicine across diverse healthcare environments, including rural, inner-city, teaching, and community healthcare settings. 
  • Define design guidance and solutions based on identified needs, and develop testable prototypes that integrate these design criteria. 
  • Evaluate prototypes with usability methods and realistic simulated tasks set in a clinical simulation center. 
  • Disseminate results to health IT developers, researchers, and scientists to allow integration of the findings into future designs of technology solutions. 

The research team used a mixed methods approach, including focus groups, interviews, observations, and electronic health record data analysis to develop a deep understanding of the cognitive needs of emergency medicine staff. These findings were used to iteratively develop tools and models based on those needs. The research team not only identified gaps and challenges related to existing health IT but also strategies for improving the methods used for the development and testing before implementation. As such, the results from this research provide solutions to guide future innovations in health IT. The research also produced strategies and prototypes that advanced the understanding of the time-sensitive and high-acuity environment of the ED. These findings and prototype interfaces represent a step forward in using CSE to support the needs of frontline ED providers with a goal of reducing burden and increasing safety.