Designing Intelligent Systems to Support Cognitive Work of Clinical Providers in Emergency Departments
Designing Intelligent Systems to Support Cognitive Work of Clinical Providers in Emergency Departments
The use of cognitive engineering systems methodology helps to better understand the interactions of the cognitive and workflow processes of frontline emergency medicine providers. Its use also can inform the design of health IT solutions to improve effectiveness of clinical work in high-intensity healthcare environments.
Emergency departments are complex environments with a high potential for error
Emergency departments (EDs) in hospitals are complex environments and home to some of the most challenging cognitive work conditions for providers: high risk, time pressure, and uncertainty. These environments always have many people moving around and working within time-sensitive and demanding situations. With multiple physicians, advance practice providers, nurses, and other staff attempting to coordinate and provide appropriate care quickly, there is a greater risk for errors, inefficiencies, and suboptimal workflow.
As complexity increases and technology advances, the value of “intelligent” design of health information technology (IT) to better support the work of emergency medicine (EM) providers is more apparent. An improved understanding of how to effectively integrate systems into the workflow of EM providers and nonclinical staff is critical to fully utilize the potential of technology, while also keeping patients safe. Health IT systems should support providers’ cognitive work, workflow, and decision making needs. In contrast, when providers find themselves adapting their cognitive work and workflow to meet the system’s requirements, mistakes and errors in patient care occur.
Understanding cognitive needs of ED providers to improve workflow and health IT systems design
To address these issues, Dr. Aaron (Zach) Hettinger and team from the MedStar Health Research Institute, in association with the University at Buffalo-State University of New York and other collaborators, used cognitive systems engineering (CSE) approaches to understand and support complex cognition and work activities in the ED. They also developed models and solutions to some of the biggest challenges in practicing medicine in this complex environment. They knew the ED could benefit from decision aids, visualizations, and other supportive tools and approached the addition of these tools from the perspective of joint cognitive systems, distributed across people, roles, and time.
The team used a mixed methods approach, including focus groups, interviews, observations, and electronic health record (EHR) data analysis to develop a deep understanding of the cognitive needs of emergency medicine staff, which informed the development of several tools and prototypes. These included a Workload Monitoring Prototype—an embedded workload display tool in the EHR that visually quantifies the individual work associated with a patient while monitoring the distribution of work across providers; a Patient-Centered Display interface, used to incorporate information needs and communication strategies across physicians and nurses to facilitate a holistic view of the patient and communication between these providers; and a Clinical Timeline Chart Review Tool—a timeline-based platform to review the events of an individual’s patient care.
Supporting providers’ needs to reduce burden and increase patient safety
The research team stresses that 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; this clearly-presented information will help providers carry out work effectively and safely. This study’s findings and prototype interfaces represent a step forward in using CSE to support the needs of frontline EM providers with a goal of reducing burden and increasing safety. 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.