NCT02983812

Developing Enhanced Prediction Models

Developing Enhanced Prediction Models to Identify Patients at Risk for Hospital Readmission by Collecting Patient-Generated Health Data

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 500 (actual)
• Sponsor: University of Pennsylvania · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

In this study, patients will be prospectively enrolled for data collection to design prediction models that integrate claims data (inpatient, outpatient, and pharmacy), electronic health record data (on clinical, social, and behavioral indicators), and patient-generated activity data. Patients will be randomized to use either a smartphone or a wearable activity tracking device to capture patient-generated health data.

Detailed description

Many hospital readmissions could be prevented if higher risk patients were identified and effective interventions then targeted towards these individuals. However, most existing claims-based predictive models perform poorly and do not provide timely and actionable information. In this study, researchers will prospectively enroll patients for data collection to design prediction models that integrate claims data (inpatient, outpatient, and pharmacy), electronic health record data (on clinical, social, and behavioral indicators), and use wearable devices or smartphones to collect patient-generated data (physical activity and sleep patterns). Patients will be randomized to use either a smartphone or a wearable activity tracking device to capture patient-generated health data.

Conditions

• Acute Myocardial Infraction or Chest Pain
• Chronic Obstructive Pulmonary Disease (COPD)
• Congestive Heart Failure (CHF)
• Diabetes
• Pneumonia

Timeline

• Start date: 2017-01-23
• Primary completion: 2019-06-07
• Completion: 2019-12-07
• First posted: 2016-12-06
• Last updated: 2020-02-06

Locations

1 site across 1 country: United States