Developing Enhanced Prediction Models
| Status: | Active, not recruiting |
|---|---|
| Conditions: | Angina, Chronic Obstructive Pulmonary Disease, Pneumonia, Cardiology, Pulmonary |
| Therapuetic Areas: | Cardiology / Vascular Diseases, Pulmonary / Respiratory Diseases |
| Healthy: | No |
| Age Range: | 18 - Any |
| Updated: | 2/21/2019 |
| Start Date: | January 23, 2017 |
| End Date: | June 2019 |
Developing Enhanced Prediction Models to Identify Patients at Risk for Hospital Readmission by Collecting Patient-Generated Health Data
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.
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.
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.
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.
Inclusion Criteria:
1. Be 18 years or older
2. Be able to provide informed consent
3. Be admitted to the Hospital of the University of Pennsylvania or Penn Presbyterian
Medical Center
4. Have a smartphone or tablet compatible with activity tracking devices
5. Plan to be discharged to home
Exclusion Criteria:
Have no medical condition which prohibits them from ambulating or plan for any medical
procedure over the next 6 months that would prohibit them from ambulating.
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