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

> **NIH NIH K99** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $89,644

## Abstract

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 syst...

## Key facts

- **NIH application ID:** 10505752
- **Project number:** 1K99LM014097-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Siru Liu
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $89,644
- **Award type:** 1
- **Project period:** 2022-08-10 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10505752

## Citation

> US National Institutes of Health, RePORTER application 10505752, Optimizing Clinical Decision Support Alerts Using Explainable Artificial Intelligence (XAI) (1K99LM014097-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10505752. Licensed CC0.

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