Abstract / Summary
OBJECTIVES: The objective of this study is to evaluate the effect of a continuous remote patient monitoring (CRPM) program on reducing 30-day readmissions in a case-versus-retrospective-propensity-matched-control study of heart failure (HF) patients. METHODS: The study was conducted at Endeavor Health (formerly known as NorthShore University HealthSystem), Evanston, IL, in three post-soft launch phases: Phase IIa, IIb, and Phase III. HF patients were monitored for 30 days postdischarge with wearable biosensors to collect continuous ambulatory physiological data and a study phone for capturing patient-reported outcomes via daily surveys. Sensor data were analyzed by rules-based and machine learning algorithms to alert on physiologic perturbation. Platform alerts and survey data were monitored by home health nurses (HHNs) who assessed patients and, following a structured escalation pathway, referred them to HF advanced practice providers (APPs) or physicians for further management. RESULTS: A total of 39 patients completed the study. Baseline characteristics were appropriately matched in all categories with the exception of the New York Heart Association (NYHA) classification (intervention vs. control, class II: 10.3 vs. 41.0%, class III: 76.9 vs. 53.8%, class IV: 12.8 vs. 5.1%; p = 0.006). The intervention group had more patients who received HF APP calls (66.7 vs. 7.7%; p ≤ 0.001), HF APP visits (43.6 vs. 5.1%; p < 0.001), HF physician visits (64.1 vs. 41.0%; p = 0.041), diuretic escalation (43.6 vs. 12.8%; p = 0.006), and laboratories within 30 days of discharge (76.9 vs. 43.6%; p = 0.005). There was no significant difference between the adjusted 30-day readmission rates for the intervention and control groups (0.31, 95% confidence interval [CI]: 0.06-1.38; p = 0.138). All-cause readmissions occurred, but none were due to HF in phases IIa, IIb, and III. CONCLUSION: Integrating CRPM directly into clinical workflows is feasible but challenging. Further research with larger patient cohorts and an adequately powered randomized trial is required to robustly evaluate the role of CRPM, machine learning algorithms, and cascading workflows in reducing 30-day readmissions.