Effectiveness of an Emergency Department Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls among Older Adults: A Protocol Paper for a Quasi-experimental Study (Preprint)

BACKGROUND Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first healthcare providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients. OBJECTIVE The goal of this paper to is describe a research protocol for evaluating effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of machine learning and artificial intelligence to augment medical decision making. METHODS To assess effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rate at three different EDs. We use a quasi-experimental design known as a sharp regression discontinuity with regards to intent-to-treat, since the intervention is administered to patients whose risk score falls above some threshold. A conditional logistic regression model will be built to describe six-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of return visit for fall and its 95% confidence interval will be estimated comparing those that identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile. RESULTS The ML-CDS tool under study has been implemented at two of the three EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 45 (15%) patients have scheduled an appointment with the clinic. CONCLUSIONS This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end dataset allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multi-site implementation plan will demonstrate applicability to a broad population and possibility to adapt intervention to other emergency departments and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and staggered implementation strategy allows for the identification of secular trends that could affect causal association and mitigate as necessary. CLINICALTRIAL This observational quasi-experiment is registered with clinicaltrials.gov as NCT05810064