Sepsis is present or develops in approximately one of every 23 hospital admissions and accounts for nearly half of all hospital deaths. Awareness of sepsis is low; many septic patients are not diagnosed at an early stage when aggressive treatment has the potential to reverse the course of infection. Currently, clinicians must rely on clues to sepsis and its progression by ?hunting and gathering? in the electronic medical record (EMR), searching a patient?s medical history, vital signs, and clinical lab values. Alert and warning complexity is prevalent in health care information technology (HIT). Time pressure, competing demands, and ambiguous alert design reduce a user?s opportunity to detect signals in the face of workplace ?noise? and also may lead to inadvertent confirmation bias. However, there is little consensus on how alerts and warning should be generated and displayed to clinicians. The proposed research will assess how information display and how the level of content provided impacts providers? action in the identification and treatment of sepsis by evaluating the impact of alternative display formats on physician and nurse performance and preference. The ultimate goal of the proposed research is to create an evidence-based framework to inform and advance the complexity of clinical decision support. From a human factors perspective, our objective is to determine the best way to provide a sepsis alert to improve decision making in the dynamic, fragmented health care work environment. We hypothesize that the design of the alert plays a significant role in provider recognition and response. Our methodology leverages clinical, HIT, human factors, and industrial design expertise with a unique usability testing approach supported by strong experimental design. We will use a crossover experimental design consisting of a series of tests of simulated cases derived from de-identified cases integrated into actual EMRs to examine the effects of different visual display models on sepsis identification and management. In addition, the level of clinical severity (early stage, moderate, severe sepsis) will be tested to identify how clinical severity level impacts the outcomes of interest on pictorial and text-based models compared to baseline, representing the usual care of fragmented, non-directed information gathering. The alternative displays and alerts will be evaluated on a diverse set of physicians and nurses from medical/surgical units in a broad range of hospitals. The experiments are designed to better understand the decision making process to promote situational awareness and measure diagnosis- and treatment- related decisions and actions. Usability testing results measuring clinician preference and performance will provide evidence-based guiding principles for accelerating the development and adoption of clinical decision support systems for sepsis including actionable design recommendations. We also will evaluate standard data collection processes to improve the fidelity of the study and better understand the complete system. Our research has the potential to improve the EMR system and significantly impact the design of clinical care and practice, both for sepsis and more generally.

Public Health Relevance

The current structure of Electronic Health Record systems provides clinicians access to raw patient data without imputation of its significance or utilization of validated illness-severity scoring systems. Focusing on sepsis detection and treatment, this research will develop and test multiple enhanced visual display models that integrate patient data into validated sepsis staging scores with the primary objective of informing the development of future real-time clinical decision-support tools. Results from usability testing of physicians and nurses measuring their preference and performance will provide evidence-based principles and actionable design recommendations to develop a relevant clinical decision support system for sepsis.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
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Sim, Hua-Chuan
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Medstar Health Research Institute
United States
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Miller, Kristen; Mosby, Danielle; Capan, Muge et al. (2018) Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support. J Am Med Inform Assoc 25:585-592
Capan, Muge; Hoover, Stephen; Miller, Kristen E et al. (2018) Data-driven approach to Early Warning Score-based alert management. BMJ Open Qual 7:e000088
Miller, Kristen; Capan, Muge; Weldon, Danielle et al. (2018) The design of decisions: Matching clinical decision support recommendations to Nielsen's design heuristics. Int J Med Inform 117:19-25
Long, Devida; Capan, Muge; Mascioli, Susan et al. (2018) Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis. Crit Care Nurse 38:46-54