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

NCT ID: NCT05282654

Last Updated: 2023-12-07

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 1300 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-02-01
• Study Completion Date: 2026-10-15

Brief Summary

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.

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

Conditions

Multiple Chronic Conditions; Adverse Event

Keywords

Digital Health; Patient Reported Outcomes; Symptom Monitoring; Predictive Model; Care Transitions; Post Acute Care

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: PREVENTION
• Blinding Strategy: DOUBLE

Study Groups

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.

Group Type: NO_INTERVENTION

No interventions assigned to this group

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.

Group Type: NO_INTERVENTION

No interventions assigned to this group

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.

Group Type: EXPERIMENTAL

ePRO Application

Intervention Type: BEHAVIORAL

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.

Interventions

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.

Intervention Type: BEHAVIORAL

Eligibility Criteria

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)

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

RAND

OTHER

Sponsor Role: collaborator

Brigham and Women's Hospital

OTHER

Sponsor Role: lead

Responsible Party

Anuj K. Dalal, MD

Associate Physician

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Anuj Dalal, MD

Role: PRINCIPAL_INVESTIGATOR

Brigham and Women's Hospital

Locations

Brigham and Women's Faulkner Hospital

Boston, Massachusetts, United States

Site Status: RECRUITING

Brigham and Women's Hospital

Boston, Massachusetts, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Anuj Dalal, MD

Role: CONTACT

Phone: (617) 525-8891

Email: adalal1@bwh.harvard.edu

Savanna Plombon, MPH

Role: CONTACT

Phone: 857-307-2668

Email: splombon@bwh.harvard.edu

Facility Contacts

Anuj K Dalal, MD

Role: primary

Other Identifiers

2021P002593

Identifier Type: -

Identifier Source: org_study_id