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Sharing Patient-Generated Data in Clinical Practices: An Interview Study.

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Summary
This summary is machine-generated.

Patients generate vast personal health data via wearables and mobile health (mHealth) apps. Sharing this patient-generated data (PGD) in clinical practice faces technical, social, and organizational hurdles.

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Area of Science:

  • Digital Health
  • Health Informatics
  • Patient-Reported Outcomes

Background:

  • Growing volume of personal health data (PHD) generated by patients using wearable sensors and mobile health (mHealth) applications.
  • Potential utility of PHD for monitoring everyday behaviors and lifestyles, offering insights beyond traditional clinical settings.
  • Uncertainty regarding effective methods for integrating and leveraging PHD into routine clinical practice.

Purpose of the Study:

  • To investigate current practices and challenges in sharing patient-generated data (PGD) between patients and clinicians.
  • To identify barriers and enablers for the effective use of PGD in clinical care.

Main Methods:

  • Qualitative interview study design.
  • Exploration of patient and clinician perspectives on PGD sharing.
  • Analysis of shared experiences and identified challenges.

Main Results:

  • Identification of significant technical challenges in data sharing and integration.
  • Recognition of social and organizational barriers impacting PGD utilization.
  • Understanding of the complex interplay between technology, user behavior, and healthcare systems.

Conclusions:

  • Sharing and leveraging patient-generated data in clinical settings is complex.
  • Addressing technical, social, and organizational factors is crucial for successful PGD integration.
  • Findings offer insights into potential solutions for overcoming barriers in PGD sharing for improved healthcare outcomes.