Real-time Symptom Monitoring Using ePROs to Prevent Adverse Events During Care Transitions
Purpose
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.
Conditions
- Multiple Chronic Conditions
- Adverse Event
Eligibility
- Eligible Ages
- Over 18 Years
- Eligible Genders
- All
- Accepts Healthy Volunteers
- No
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 Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- Randomized
- Intervention Model
- Parallel Assignment
- Intervention Model Description
- Baseline, pre-implementation period (usual care arm 1) and main trial (RCT) period (usual care arm 2 and intervention/experimental arm 3)
- Primary Purpose
- Prevention
- Masking
- Double (Investigator, Outcomes Assessor)
- Masking Description
- During main trial (post-implementation period), study investigators, outcomes assessor will be masked to randomization status of all participants
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
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. |
|
Recruiting Locations
Boston, Massachusetts 02115
Anuj K Dalal, MD
Boston, Massachusetts 02115
More Details
- Status
- Recruiting
- Sponsor
- Brigham and Women's Hospital
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