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Naturalistic Observations02:30

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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A User-friendly and Powerful R Analysis of Large-scale Datasets
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Published on: November 4, 2025

Routine data are not 'dirty' data: how to approach using routine data for research.

Grace Duffy1, Alice Cunningham2, Vishal Sharma2

  • 1Improvement Academy, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK grace.duffy@nhs.net.

BMJ Health & Care Informatics
|May 29, 2026
PubMed
Summary
This summary is machine-generated.

Routine healthcare data in NHS Secure Data Environments (SDEs) require contextual understanding for accurate research. Analyzing data with an awareness of its creation process prevents misinterpretation and drives healthcare improvements.

Keywords:
BMJ Health InformaticsData ScienceElectronic Health RecordsHealth Services ResearchInformatics

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Area of Science:

  • Health Informatics
  • Data Science in Healthcare
  • Clinical Research Methodology

Background:

  • The proliferation of electronic patient records generates vast amounts of routine healthcare data.
  • NHS Secure Data Environments (SDEs) are emerging to facilitate research using this data.
  • Routine data, generated for clinical care, differ significantly from research data, posing interpretation challenges.

Purpose of the Study:

  • To illustrate the complexities and potential misinterpretations of routine healthcare data in research settings.
  • To provide practical strategies for researchers analyzing routine data within SDEs.
  • To advocate for enhanced collaboration between data users and creators to ensure data integrity.

Main Methods:

  • Analysis of real-world examples demonstrating how routine data is shaped by its context.
  • Development of practical strategies for researchers, including source-level analysis and collaboration with data providers.
  • Conceptual framework emphasizing data provenance and accurate interpretation within SDEs.

Main Results:

  • Routine data's inherent characteristics can lead to misinterpretation if not understood within its production context.
  • Strategies like source-level analysis and close collaboration with data providers are crucial for accurate interpretation.
  • SDEs have the potential to improve healthcare if routine data is treated as meaningful records of healthcare processes.

Conclusions:

  • Understanding the context of routine data generation is paramount for valid healthcare research.
  • Secure Data Environments (SDEs) should prioritize fostering connections between data users and creators.
  • Approaching routine data as records of healthcare processes, rather than 'dirty' research data, unlocks its full potential for driving improvements.