Real-time Symptom Monitoring Using ePROs to Prevent Adverse Events During Care Transitions

This study aims to predict and minimize post-discharge adverse events (AEs) during care transitions through early identification and escalation of patient-reported symptoms to inpatient and ambulatory clinicians by way of predictive algorithms and clinically integrated digital health apps. We will (1) develop and prospectively validate a predictive model of post-discharge AEs for patients with multiple chronic conditions (MCC); (2) combine, adapt, extend, and iteratively refine our EHR-integrated digital health infrastructure in a series of design sessions with patient and clinician participants; (3) conduct a RCT to evaluate the impact of ePRO monitoring on post-discharge AEs for MCC patients discharged from the general medicine service across Brigham Health; and (4) use mixed methods to evaluate barriers and facilitators of implementation and use as we develop a plan for sustainability, scale, and dissemination.

This description comes directly from the study's public registry record.

Anuj Dalal, MD  ·  (617) 525-8891  ·  adalal1@bwh.harvard.edu

Savanna Plombon, MPH  ·  857-307-2668  ·  splombon@bwh.harvard.edu

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