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Information Quality Challenges of Patient-Generated Data in Clinical Practice.

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Summary

Clinicians perceive patient-generated data from self-tracking tools as variable in quality, impacting its use in clinical decisions. Further research is needed to establish standards for this digital health information.

Keywords:
clinical decision makinghealth informaticsinformation qualitypersonalized medicinequantified selfself-tracking

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Area of Science:

  • Digital Health
  • Human-Computer Interaction
  • Health Informatics

Background:

  • The proliferation of self-tracking tools has led to a significant increase in patient-generated data presented to clinicians.
  • While self-tracking practices are widely studied, the clinical utility and information quality of patient-generated data remain underexplored.

Purpose of the Study:

  • To review empirical studies on self-tracking tools and identify how clinicians perceive the quality of patient-generated data.
  • To explore the potential of patient-generated data in supporting clinical processes like diagnosis and treatment monitoring.

Main Methods:

  • A literature review of empirical studies focusing on self-tracking tools and their data.
  • Analysis of clinician perceptions regarding the information quality of self-tracked data.

Main Results:

  • Clinicians perceive several information quality characteristics related to accuracy, reliability, completeness, context, patient motivation, and data representation.
  • Identified challenges in integrating self-tracked data into clinical decision-making processes.

Conclusions:

  • Patient-generated data quality is a critical factor influencing its adoption in clinical practice.
  • Addressing perceived quality issues is essential for leveraging self-tracked data effectively in healthcare.