ABSTRACT
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 hospitalized
MCC patients using relevant PRO questionnaires and electronic health record (EHR) derived variables. We
will then 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 in which we compare our real-time symptom monitoring intervention to
usual care 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.