Leveraging explainable artificial intelligence to optimize clinical decision support.

OBJECTIVE: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches. METHODS: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful. RESULTS: The final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues. CONCLUSION: We developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues.

Clinician collaboration to improve clinical decision support: the Clickbusters initiative.

OBJECTIVE: We describe the Clickbusters initiative implemented at Vanderbilt University Medical Center (VUMC), which was designed to improve safety and quality and reduce burnout through the optimization of clinical decision support (CDS) alerts. MATERIALS AND METHODS: We developed a 10-step Clickbusting process and implemented a program that included a curriculum, CDS alert inventory, oversight process, and gamification. We carried out two 3-month rounds of the Clickbusters program at VUMC. We completed descriptive analyses of the changes made to alerts during the process, and of alert firing rates before and after the program. RESULTS: Prior to Clickbusters, VUMC had 419 CDS alerts in production, with 488 425 firings (42 982 interruptive) each week. After 2 rounds, the Clickbusters program resulted in detailed, comprehensive reviews of 84 CDS alerts and reduced the number of weekly alert firings by more than 70 000 (15.43%). In addition to the direct improvements in CDS, the initiative also increased user engagement and involvement in CDS. CONCLUSIONS: At VUMC, the Clickbusters program was successful in optimizing CDS alerts by reducing alert firings and resulting clicks. The program also involved more users in the process of evaluating and improving CDS and helped build a culture of continuous evaluation and improvement of clinical content in the electronic health record.

The Need for Biochemical Testing for Alcohol in Integrated Addiction Treatment Settings During the Opioid Epidemic.

As the opioid crisis drives expansion of integrated opioid use disorder (OUD) treatment programs in generalist settings, these programs will contend with significant rates of co-occurring alcohol use. The authors present a brief literature review and commentary regarding nondisordered and disordered alcohol use in OUD treatment settings and biochemical detection techniques. Biochemical testing for alcohol in integrated OUD treatment settings is both important for detecting alcohol use disorder and feasible. Breathalyzer testing may assist with management of acutely intoxicated patients. Biochemical testing for alcohol is an important part of integrated OUD treatment. More research is needed on the impact of alcohol use on OUD treatment outcomes and the role of breathalyzer testing in management of intoxicated patients in the outpatient setting.

Electroconvulsive therapy in a cochlear implant patient.

OBJECTIVE: To report the first use of electroconvulsive therapy (ECT) in a patient with a cochlear implant (CI). STUDY DESIGN: Clinical Capsule Report. SETTING: University hospital. PATIENT: : A 17-year-old boy who underwent Nucleus 22 cochlear implantation in 1995 presented with delirious mania in 2009. Aggressive pharmacologic management was ineffective, and ECT was recommended due to the potentially lethal nature of his psychiatric illness. INTERVENTIONS: After careful consideration by a multidisciplinary team, unilateral ECT on the side opposite the CI without removal of the device was recommended. Electroconvulsive therapy was performed on hospital Days 20 and 21. Integrity testing of the CI was performed on hospital Day 38. MAIN OUTCOME MEASURES: Subjective and objective assessment of cochlear implant functioning and response to ECT. RESULTS: Electroconvulsive therapy was well tolerated and contributed to alleviation of presenting symptoms. The patient used his CI without subjective degradation of performance. Integrity testing of the CI after ECT confirmed proper functioning of the device. CONCLUSION: This is the first report of ECT in a patient with CI. Unilateral ECT was performed contralateral to the CI without subjective or objective decline in performance. This Clinical Report motivates further study regarding the use of ECT in CI patients.