Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. Over the past decade, the federal government has spent more than $34 billion on the meaningful use of electronic 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 techniques 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. In the K99 Phase, I developed a standards-based taxonomy of features that affect user response to CDS alerts (Aim 1) and a data-driven process to generate suggestions for improving alert criteria using XAI approaches (Aim 2). In this R00 phase, I will evaluate generated suggestions using a mixed-methods design (Aim 3). I will use XAI approaches developed in Aim 2 to generate suggestions. I will ask CDS experts to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts and rate the suggestions for their usefulness, acceptance, relevance, understanding, work- flow, bias, inversion, and redundancy. Throughout this research, I expect to produce a set of expert-validated suggestions based on the XAI approaches. This study could significantly contribute to the improvement of CDS management and clinical processes. My career development plan and the proposed research are aligned with my current skills and experiences in CDS and machine learning. 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.