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Developing real-world evidence from real-world data: Transforming raw data into analytical datasets.

Lisa Bastarache1, Jeffrey S Brown2, James J Cimino3

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Generating practice-based evidence from electronic health records (EHRs) requires understanding data quality and informatics. This ensures real-world data transforms into reliable real-world evidence for improved healthcare.

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Area of Science:

  • Clinical Informatics
  • Health Data Science
  • Evidence-Based Practice

Background:

  • Evidence-based practice relies on practice-based evidence derived from real-world data.
  • Electronic Health Records (EHRs) are rich sources of patient data, including measurements, diagnoses, and behaviors.
  • EHR data can inform risk stratification algorithms and uncover associations between exposures, interventions, and outcomes.

Purpose of the Study:

  • To explore the challenges and nuances in capturing, formatting, and standardizing EHR data.
  • To highlight the clinical and informatics competencies essential for transforming raw EHR data.
  • To facilitate the generation of high-quality, fit-for-purpose analytical datasets for real-world evidence.

Main Methods:

  • Analysis of data characteristics, including quality, detail, provenance, and organization within EHRs.
  • Examination of data variations across different institutional sites for multi-site analyses.
  • Discussion of the necessary clinical domain and informatics expertise for data transformation.

Main Results:

  • Identified idiosyncrasies in EHR data capture and formatting that impact data usability.
  • Highlighted the critical need for understanding data provenance and inter-site variability.
  • Emphasized that transforming raw EHR data into reliable real-world evidence necessitates specific analytical and data management skills.

Conclusions:

  • Transforming real-world data from EHRs into robust real-world evidence requires meticulous attention to data quality and standardization.
  • Clinical and informatics expertise are paramount for overcoming EHR data idiosyncrasies.
  • High-quality, fit-for-purpose analytical datasets are achievable through careful data curation, enabling reliable evidence generation.