Critically injured patients have a four-fold higher risk of death from medical errors than other hospitalized patients, with nearly half of preventable deaths related to errors during the initial resuscitation phase. Although protocols, simulation, and leadership training improve team performance in this setting, as many as 12 protocol deviations per resuscitation have been observed, even with experienced teams. Given adverse outcomes that can result from performance gaps, there is a critical need to establish novel approaches for applying real-time decision support in critical-care settings. The long-term goal is to implement decision support for trauma resuscitation and other fast-paced, high-risk critical care settings that improves performance, reduces errors, and prevents adverse outcomes. The overall objective for this renewal is to vertically advance what was achieved during the first funding period by designing, implementing and testing an intention-aware recommender system that (1) recognizes and tracks current goals using sensor data, the output from patient monitors, and data captured from digital devices, (2) derives recommendations that support adherence to goal- based protocols, and (3) displays these recommendations in real time on wall displays. The central hypothesis is that decision support aligning with intentions (?intended? or ?current? goals) will enhance protocol compliance, leading to improved outcomes related to trauma resuscitation. The rationale for this renewal is that recommendations supporting protocol compliance that are aligned with team intentions are more likely to be adopted by being less distracting and associated with lower cognitive load. Guided by preliminary data, the central hypothesis will be tested by pursuing two specific aims: 1) design and implement an automated real- time approach for predicting and monitoring the assessment and treatment goals of trauma resuscitation; and 2) generate and display a recommended plan of activities that supports current goal pursuit during trauma resuscitation. For the first Aim, machine learning approaches will be applied for recognizing goals using data obtained from sensors and other digital data sources. Under the second Aim, a machine learning strategy will be implemented and tested that generates recommendations responsive to team intentions. The proposed research is innovative because it focuses on development of real-time methods that integrate goals as an input for making recommendations that meet the most current and relevant information needs. The proposed research is significant because it is expected to improve the care of severely injured and other critically ill patients by promoting timely and appropriate achievement of critical assessment and treatment goals in settings that remain at high-risk for medical errors. The results of this research continuum are expected to have an important positive impact on the outcome by addressing the mismatch between complex decision-making and human vulnerability to error that remain in critical care settings.

Public Health Relevance

The proposed project is relevant to public health because it focuses on the design and implementation of a novel real-time decision support system that will reduce errors associated with adverse patient outcomes by providing recommendations that support the current information needs of multidisciplinary trauma teams. The proposed project is relevant to the part of the NLM's mission related to development of biomedical communications systems, methods, and technologies, and information dissemination and utilization among health professionals.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
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Children's Research Institute
United States
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