Abstract
BackgroundThe early identification of clinical deterioration in patients in hospital units can decrease mortality rates and improve other patient outcomes; yet, this remains a challenge in busy hospital settings. Artificial intelligence (AI), in the form of predictive models, is increasingly being explored for its potential to assist clinicians in predicting clinical deterioration. ObjectiveUsing the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model, this study aims to assess whether an AI-enabled work system improves clinical outcomes, describe how the clinical deterioration index (CDI) predictive model and associated work processes are implemented, and define the emergent properties of the AI-enabled work system that mediate the observed clinical outcomes. MethodsThis study will use a mixed methods approach that is informed by the SEIPS 2.0 model to assess both processes and outcomes and focus on how physician-nurse clinical teams are affected by the presence of AI. The intervention will be implemented in hospital medicine units based on a modified stepped wedge design featuring three stages over 11 months—stage 0 represents a baseline period 10 months before the implementation of the intervention; stage 1 introduces the CDI predictions to physicians only and triggers a physician-driven workflow; and stage 2 introduces the CDI predictions to the multidisciplinary team, which includes physicians and nurses, and triggers a nurse-driven workflow. Quantitative data will be collected from the electronic health record for the clinical processes and outcomes. Interviews will be conducted with members of the multidisciplinary team to understand how the intervention changes the existing work system and processes. The SEIPS 2.0 model will provide an analytic framework for a mixed methods analysis. ResultsA pilot period for the study began in December 2020, and the results are expected in mid-2022. ConclusionsThis protocol paper proposes an approach to evaluation that recognizes the importance of assessing both processes and outcomes to understand how a multifaceted AI-enabled intervention affects the complex team-based work of identifying and managing clinical deterioration.International Registered Report Identifier (IRRID)PRR1-10.2196/27532
Highlights
BackgroundThe timely identification of hospitalized patients who are clinically deteriorating is critical for facilitating prompt clinical interventions to improve patient outcomes but remains challenging for hospital systems to perform consistently
Our evaluation will assess if and how Artificial intelligence (AI) predictions, when incorporated into a team-based workflow, may help to reduce these barriers to effective team performance. This approach is a significant addition to the existing evaluations of AI interventions that primarily focus only on the effect of AI on clinical outcomes and do not sufficiently examine how the observed effects are mediated by AI and the associated work processes
The application of complexity and sociotechnical systems thinking in our evaluation allows for the generation of insights into how AI can be effectively incorporated into human work systems to deliver the desired improvements in processes and outcomes
Summary
BackgroundThe timely identification of hospitalized patients who are clinically deteriorating is critical for facilitating prompt clinical interventions to improve patient outcomes but remains challenging for hospital systems to perform consistently. A recently reported multisite prospective study of an intervention that used a model to predict inpatient clinical deterioration demonstrated improved clinical outcomes such as mortality rate and ICU length of stay in the intervention cohort, but there were no observed differences in the process measures, and it remains unclear which features of the implementation mediated the observed clinical benefit [7]. This particular intervention did demonstrate a clinical benefit at the participating study sites, there remains a pressing need for a deeper understanding of how such interventions can be effectively designed and implemented using AI to successfully disseminate them across other health care systems.
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