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Evaluating the Impact of Data Standardization on Real-World Data.

Elizabeth M Garry1, Aidan Baglivo1, Priya Govil1

  • 1Aetion, Inc., New York, New York, USA.

Pharmacoepidemiology and Drug Safety
|August 5, 2025
PubMed
Summary
This summary is machine-generated.

Standardizing healthcare data to the Sentinel common data model refines data but may lower counts and distributions. Understanding these impacts is crucial for accurate cohort selection and analysis of COVID-19 patients.

Keywords:
CDMCOVID‐19administrative healthcare datacommon data modeldata standardizationsentinel

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Area of Science:

  • Health data science
  • Biostatistics
  • Epidemiology

Background:

  • Administrative healthcare data requires standardization for multi-site research.
  • The Sentinel Initiative uses a common data model for distributed research.
  • Standardization impacts cohort characteristics and outcome measures.

Purpose of the Study:

  • To evaluate the effect of standardizing administrative healthcare data to the Sentinel common data model.
  • To assess impacts on cohort selection and descriptive findings for COVID-19 patients.

Main Methods:

  • Compared patients with outpatient COVID-19 diagnoses (Jan 2021-Dec 2022) using native and standardized HealthVerity data.
  • Analyzed cohort attrition, sample size, patient demographics, and healthcare resource utilization.
  • Examined incidence of selected conditions post-COVID-19 diagnosis.

Main Results:

  • Standardized cohort was smaller (164,445 vs. 198,317) but had similar age and sex distributions.
  • Race distributions differed due to mapping 'Other' to 'Unknown/Missing'.
  • Comorbidities and SARS-CoV-2 test rates were slightly lower in the standardized cohort; encounter counts and incidence rates (e.g., hepatotoxicity) were notably lower.

Conclusions:

  • Data standardization refines data by reducing duplicates and errors, potentially lowering outliers.
  • Standardization can lead to lower distributions and counts for certain variables compared to native data.
  • Understanding standardization's impact is critical for ensuring data fitness for research use.