Way to Health

PA Activity Study

Test different methods of collecting patient-generated health data including self-reported Survey administration, smartphones, and a wearable activity tracking device. The overall objective of the study is to develop algorithms for the dynamic and timely prediction of health care utilization using a multimodal, integrated dataset from insurer and pharmacy claims, electronic health records, and patient-generated health data.

Nov 1, 2016

Mitesh Patel, MD, MBA, MS Mitesh Patel, MD, MBA, MS
David Asch, MD, MBA David Asch, MD, MBA

Background

Hospital readmission prediction models often perform poorly. A critical limitation is that they use data collected up until the time of discharge but do not leverage information on patient behaviors at home after discharge.

Methods

PREDICT is a two-arm, randomized trial comparing ways to use remotely-monitored patient activity levels after hospital discharge to improve hospital readmission prediction models. Patients are randomly assigned to use a wearable device or smartphone application to track physical activity data. The study collects also validated assessments on patient characteristics as well as disparate data on credit scores and medication adherence. Patients are followed for 6 months. We evaluate whether these data sources can improve prediction compared to standard modeling approaches.

Conclusion

The PREDICT Trial tests a novel method of remotely-monitoring patient behaviors after hospital discharge. Findings from the trial could inform new ways to improve the identification of patients at high-risk for hospital readmission.

Publications and Press

  • Prediction using a randomized evaluation of data collection integrated through connected technologies (PREDICT): Design and rationale of a randomized trial of patients discharged from the hospital to home