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Information Quality Challenges of Patient-Generated Data in Clinical Practice
Peter West1, Max Van Kleek2, Richard Giordano1
1Faculty of Health Sciences, University of Southampton, Southampton, United Kingdom.
Frontiers in Public Health
|December 7, 2017
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.
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.
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