Quality Improvement Study Using a Machine Learning Mortality Risk Prediction Model Notification System on Advance Care Planning in High-Risk Patients

Abstract

Background: Advance care planning (ACP) is an important aspect of patient care that is underutilized. Machine learning (ML) models can help identify patients appropriate for ACP. The objective was to evaluate the impact of using provider notifications based on an ML model on the rate of ACP documentation and patient outcomes. Methods: This was a pre-post QI intervention study at a tertiary academic hospital. Adult patients admitted to general medicine teams identified to be at elevated risk of mortality using an ML model were included in the study. The intervention consisted of notifying a provider by email and page for a patient identified by the ML model. Results: A total of 479 encounters were analyzed of which 282 encounters occurred post-intervention. The covariate-adjusted proportion of higher-risk patients with documented ACP rose from 6.0% at baseline to 56.5% (Risk Ratio (RR)= 9.42, 95% CI: 4.90 - 18.11). Patients with ACP were more than twice as likely to have code status reduced when ACP was documented (29.0% vs. 10.8% RR=2.69, 95% CI: 1.64 – 4.27). Additionally, patients with ACP had twice the odds of hospice referral (22.2% vs. 12.6% Odds Ratio=2.16, 95% CI: 1.16 – 4.01). However, patients with ACP documented had a longer mean LOS (9.7 vs. 7.6 days, Event time ratio = 1.29, 95% CI: 1.10 - 1.53). Conclusion: Provider notifications using an ML model can lead to an increase in completion of ACP documentation by frontline clinicians in the inpatient setting.