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Developing Enhanced Prediction Models

2016-12-06 16:53:21 | BioPortfolio

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.

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, sleep patterns, and heart rate). Patients will be randomized to use either a smartphone or a wearable activity tracking device to capture patient-generated health data.

Study Design

Observational Model: Cohort, Time Perspective: Prospective

Conditions

Acute Myocardial Infraction or Chest Pain

Status

Not yet recruiting

Source

University of Pennsylvania

Results (where available)

View Results

Links

Published on BioPortfolio: 2016-12-06T16:53:21-0500

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