Optimizing Clinical Decision Support Alerts Using Explainable Artificial Intelligence (XAI)

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PROJECT SUMMARY Over the past decade, the federal government has spent more than $34 billion on the meaningful use of elec- tronic health records (EHRs). However, the acceptance rate for clinical decision support (CDS) alerts, a critical component of EHRs, is less than 10%. The large number of low relevance alerts (e.g. a weight loss alert during a cardiac resuscitation) not only increases the burden on clinicians, but can lead to the onset of alert fatigue, resulting in the neglect of important alerts and posing a serious threat to patient safety. Currently, alerts are improved primarily through manual review and by collecting user feedback. However, these methods are labor- intensive and do not allow for a timely analysis of user responses to alerts from a comprehensive aspect. The amount of alert log data is large, Vanderbilt University Medical Center generated over 3 million alert firings in 2020. There is an urgent need to utilize the data from the alert log and EHR to develop a data-driven process to generate suggestions for refining alert logic or improving clinical processes. To address this gap, I propose to use explainable artificial intelligence (XAI) combined with bias mitigation tech- niques to build predictive models that comprehensively learn user responses to alerts and in turn automatically generate responsible suggestions to improve the original logic of alerts. This project is divided into two phases with three specific aims. In the K99 phase, I will be mentored by a multidisciplinary team of experts to learn the latest XAI and bias mitigation techniques, as well as CDS evaluation and management, and to achieve the following two aims: Aim 1) Develop a standard-based taxonomy of features that affect user response to CDS alerts and Aim 2) Develop a data-driven process to generate suggestions for improving alert criteria using XAI approaches. I will then transition to the independent research phase R00 to achieve Aim 3) Evaluate generated suggestions using a mixed-methods design. Throughout this research, I expect to provide a standards-based taxonomy of features that affect user response to alerts, an innovative data-driven process capable of generating suggestions to improve alerts. I will also produce a set of expert-validated suggestions. This study could signif- icantly contribute to the CDS management and clinical processes improvements. My career development plan and the proposed research are aligned with my current skills and experiences in CDS and machine learning. Based on complementary expertise from my mentor team, I will develop competen- cies in four areas: CDS, informatics methods, implementation science, and career development and profession- alism to transfer to an independent researcher. Overall, this project can help me launch an independent research career in developing explainable, intelligent CDS tools to improve patient safety, provide standardized care, and promote an equitable and efficient healthcare system.