Using AI for Detecting Clinical Deterioration: Insights and Responses from the Care Team

<h3>Context:</h3> An AI clinical deterioration model-enabled intervention implemented at a large academic medical center in 2021 was recently reported to have decreased the absolute risk of care escalations among inpatients by 10.4% over two years. This study aims to evaluate the experiences of nurses and physicians participating in this AI-enabled workflow. <h3>Objective:</h3> Assess the care team’s experiences and gather feedback on the AI-enabled clinical deterioration pathway, including both the prediction tool and collaborative workflows that follow. <h3>Study Design and Analysis:</h3> From September 2023 to February 2024, our team distributed a comprehensive survey to the care team. We analyzed the survey results using statistical methods and free text data was thematically analyzed using Stanford’s secure instance of GPT-4. <h3>Setting or Dataset:</h3> An academic medical center (Stanford Health Care). <h3>Population Studied:</h3> Attending physicians, residents, interns, bedside nurses, resource nurses, and float nurses from general surgery and hospital medicine inpatient units who had access to the AI-enabled clinical deterioration pathway. The survey analysis cohort consisted of 125 participants. <h3>Intervention/Instrument:</h3> AI-enabled mobile alerts, and EHR best practice alerts based on Epic Deterioration Index prediction model scores, followed by a standardized, multidisciplinary bedside huddle. <h3>Outcome Measures:</h3> SEIPS 2.0 model and RE-AIM frameworks were used to evaluate the implementation from the perspectives of nurses and physicians. One sample t-test and the Kruskal-Wallis test were used. <h3>Results:</h3> Both physicians and nurses often agreed with the tool’s determination of patients at high risk (M = 2.608 (SD = 0.69), p &lt; 0.001). However, there were significant differences between physicians (n = 44) and nurses (n = 77) across various aspects, including knowledge gain, patient outcomes, satisfaction levels, perceived pathway value, and sustainability (p &lt; .001). Nurses consistently showed higher satisfaction than physicians, who were neutral about the tool, workflow, and impact on patient care. Most participants expressed interest in obtaining more information about the model and suggested modifications to the workflow, documentation, and team huddle. <h3>Conclusions:</h3> Both physicians and nurses generally concurred with the tool’s identification of high-risk patients. However, nurses displayed more favorable attitudes, engagement, and outlook in the intervention.