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.

Study Overview

• Status: Recruiting
• Conditions: Adverse Event; Multiple Chronic Conditions
• Intervention / Treatment: Behavioral: ePRO Application

Detailed Description

Adverse events (AE) during care transitions range from 19-28% and may lead to readmissions, representing an ongoing threat to patient safety. Early identification and escalation of patient-reported symptoms to inpatient and ambulatory clinicians is critical, especially for patients with multiple chronic conditions (MCC). Clinically integrated digital health apps have the potential to more accurately predict post-discharge AEs and improve communication for patients, their caregivers, and the care team. Such tools can provide individualized risk assessments of AEs by systematically collecting relevant patient-reported outcomes (PROs) and leveraging standardized application programming interfaces (API) to combine them with electronic health record (EHR) data. While patient-reported outcomes (PROs) are increasingly used in ambulatory settings, their use for real-time symptom monitoring and escalation during transitions from the hospital is novel and potentially transformative-by both empowering patients to better understand their individualized risks of post-discharge AEs, and improving monitoring while transitioning out of the hospital. Our proposed intervention is grounded in evidence-based frameworks for care transitions, and scaling and spread of digital health tools. To inform our intervention, we propose developing and validating a predictive model of post-discharge AEs for 450 MCC patients using relevant PRO questionnaires and electronic health record (EHR) derived variables during our baseline pre-implementation period. Simultaneously, we will combine, adapt, extend, and refine our previously developed EHR-integrated hospital and ambulatory-focused digital health infrastructure to support MCC patients in real-time symptom monitoring using PROs when transitioning out of the hospital. Our intervention uses interoperable, data exchange standards and APIs to seamlessly integrate with existing vendor patient portal offerings, thereby addressing critical gaps and supporting the complete continuum of care. Our multidisciplinary team uses principles of user-centered design and agile software development to rapidly identify, design, develop, refine, and implement requirements from patients and clinicians. Our team will rigorously evaluate this intervention in a large-scale randomized controlled trial of 850 in which we compare our real-time symptom monitoring intervention (425) to usual care (425) for patients with MCCs transitioning out of the hospital. Finally, we will conduct a robust mixed methods evaluation to generate new knowledge and best practices for disseminating, implementing, and using this interoperable intervention at similar institutions with different EHR vendors

• Study Type: Interventional
• Enrollment (Estimated): 1300
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Anuj Dalal, MD; Phone Number: (617) 525-8891; Email: adalal1@bwh.harvard.edu
• Study Contact Backup: Name: Savanna Plombon, MPH; Phone Number: 857-307-2668; Email: splombon@bwh.harvard.edu

Study Locations

• United States
  • Massachusetts
    • Boston, Massachusetts, United States, 02115
      • Recruiting
      • Brigham and Women's Hospital
      • Principal Investigator: Anuj K Dalal, MD
    • Boston, Massachusetts, United States, 02115
      • Recruiting
      • Brigham and Women's Faulkner Hospital
      • Contact: Anuj K Dalal, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Adult (18 years or older)
• Hospitalized on the general medicine services at Brigham and Women's Hospital or Brigham and Women's Faulkner Hospital for at least 24 hours
• Have a discharge status of home, home with services, or facility
• English-speaking patients or their English-speaking legally designated healthcare proxy or next of kin (i.e., a family caregiver)
• Non-English-speaking patients who have an English-speaking legally designated healthcare proxy or next of kin (i.e., a family caregiver)
• Two or more chronic conditions: Anxiety, Asthma*, Arthritis (Osteoarthritis, Rheumatoid), Atrial Fibrillation, Cancer*, Cerebral vascular accident, Chronic kidney disease*, Chronic obstructive pulmonary disease (COPD)*, Cirrhosis, Coronary artery disease/Ischemic heart disease, Dementia, Depression, Diabetes mellitus*, End-stage renal disease*, Heart failure*, Hepatitis B, C*, HIV/AIDs, Hyperlipidemia, Hypertension, Inflammatory bowel disease, Osteoporosis, Sickle cell disease, Substance abuse (Alcohol/Opioid)

Exclusion Criteria:

• Less than 18 years of age
• Less than two chronic conditions
• Hospitalized less than 24 hours
• No identifiable healthcare proxy or next of kin (i.e., a family caregiver)

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Double

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: Usual Care (Arm 1); During the 18-month Baseline Period (Arm 1, n=450) patients will be enrolled and receive usual care to develop the initial predictive model.
No Intervention: Usual Care (Arm 2); During the 30-month Main Trial (RCT) Period, patients will be randomized to usual care (Arm 2, n=425). Data collection for post-discharge AE determination will occur during both periods.
Experimental: Intervention (Arm 3); During the 30-month Main Trial (RCT) Period, patients will be randomized to the intervention (Arm 3, n=425). Data collection for post-discharge AE determination will occur during both periods.	Behavioral: ePRO Application; The intervention consists of a patient portal, EHR-integrated web-app to communicate risk of post-discharge adverse events using patient-reported outcome questionnaires, discharge preparation checklist during hospitalization. After discharge, the intervention will provide real-time symptom monitoring using ePROs and facilitate communication with clinicians based on prediction model-informed ePRO score trends exceeding escalation thresholds.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Actual adverse events (AEs)	The number of actual AEs during the 30-day post-discharge period	Up to 30-days after discharge from index hospitalization
Actual preventable adverse events (AEs)	The number of actual AEs during the 30-day post-discharge period	Up to 30-days after discharge from index hospitalization

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Potential adverse events (AEs)	The number of new or worsening symptoms reported by the patient	Up to 30-days after discharge from index hospitalization
Post-discharge healthcare utilization events (hospital readmissions)	Hospital readmissions	Up to 30-days after discharge from index hospitalization
Post-discharge healthcare utilization (ambulatory events)	Composite of unanticipated ambulatory, urgent care, ED visits	Up to 30-days after discharge from index hospitalization

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Time to actual AE	The number of days until first AE detected	Up to 30-days after discharge from index hospitalization
Time to potential AE	The number of days until first potential AE detected	Up to 30-days after discharge from index hospitalization

Collaborators and Investigators

• Sponsor: Brigham and Women's Hospital
• Collaborators: RAND
• Investigators: Principal Investigator: Anuj Dalal, MD, Brigham and Women's Hospital

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2022
• Primary Completion (Estimated): September 15, 2026
• Study Completion (Estimated): October 15, 2026

Study Registration Dates

• First Submitted: February 11, 2022
• First Submitted That Met QC Criteria: March 7, 2022
• First Posted (Actual): March 16, 2022

Study Record Updates

• Last Update Posted (Actual): April 20, 2026
• Last Update Submitted That Met QC Criteria: April 15, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: Digital Health; Patient Reported Outcomes; Symptom Monitoring; Care Transitions; Predictive Model; Post Acute Care
• Additional Relevant MeSH Terms: Pathologic Processes; Chronic Disease; Disease Attributes; Pathological Conditions, Signs and Symptoms; Multiple Chronic Conditions
• Other Study ID Numbers: 2021P002593

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No