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Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
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Aggregating multiple real-world data sources using a patient-centered health-data-sharing platform.

Sanket S Dhruva1, Joseph S Ross2,3,4, Joseph G Akar4,5

  • 11University of California, San Francisco School of Medicine, San Francisco, CA USA.

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|May 1, 2020
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A new patient-centered platform successfully aggregated diverse health data, including electronic health records and personal device information, from multiple sources for 60 patients. This demonstrates a feasible approach to integrating siloed real-world health data.

Keywords:
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Area of Science:

  • Health Informatics
  • Data Integration
  • Patient-Centered Technology

Background:

  • Real-world data (RWD) from electronic health records (EHRs) and personal digital devices are valuable but often siloed.
  • Integrating RWD is challenging for clinical, research, and regulatory applications.

Purpose of the Study:

  • To test the feasibility of a patient-centered health-data-sharing platform.
  • To aggregate health data from multiple sources for patients undergoing bariatric surgery or atrial fibrillation ablation.

Main Methods:

  • Prospective cohort study of 60 patients at two U.S. tertiary care hospitals.
  • Utilized a patient-centered platform to obtain and aggregate data.
  • Collected data from EHRs, pharmacies, personal digital devices (activity monitors, scales, ECGs), and patient-reported outcomes.

Main Results:

  • Successfully obtained EHR data from both study hospitals and ten additional health systems.
  • Aggregated pharmacy data (CVS, Walgreens), personal digital device data, and patient-reported outcome measures.
  • Demonstrated successful aggregation of multi-source health data via the platform.

Conclusions:

  • A patient-centered health-data-sharing platform is feasible for integrating siloed real-world health data.
  • The platform successfully aggregated diverse data types, including EHR, pharmacy, personal device, and patient-reported outcomes.
  • This approach facilitates comprehensive data aggregation for improved health insights.