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Advanced methods significantly improve real-world data reliability, enhancing accuracy, completeness, and traceability. This supports the use of real-world evidence in healthcare decision-making.

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Area of Science:

  • Health Informatics
  • Data Science
  • Real-World Evidence

Background:

  • Data quality is crucial for evidence validity, yet data reliability measurement is less understood.
  • Regulatory bodies like the FDA emphasize the need for robust real-world data (RWD).
  • The TRUST study aims to address these gaps by exploring data quality and informing regulatory decisions.

Purpose of the Study:

  • To report early findings from the TRUST study on practical data reliability measurement.
  • To develop a method for quantifying accuracy, completeness, and traceability of RWD.
  • To compare traditional versus advanced data approaches for RWD reliability.

Main Methods:

  • Quality improvement study involving 58 hospitals and over 1180 clinics in the US.
  • Analysis of RWD from asthma patients (2014-2022) using traditional (claims) and advanced (claims, EHRs, AI, registries) methods.
  • Accuracy assessed by F1 score, completeness by weighted mean, and traceability by proportion of data elements linked to source documentation.

Main Results:

  • Advanced approaches achieved 93.4% accuracy, 96.6% completeness, and 77.3% traceability.
  • Traditional approaches yielded significantly lower results: 59.5% accuracy, 46.1% completeness, and 11.5% traceability.
  • 120,616 patients met data requirements, with a mean age of 43.2 years.

Conclusions:

  • Practical measurement of data reliability is feasible using advanced methods and multiple data sources.
  • Increased RWD reliability can strengthen the foundation for using RWD in medication approval, reimbursement, and prescription.
  • Findings support the integration of data reliability standards in RWD generation for regulatory purposes.