Routine data are not 'dirty' data: how to approach using routine data for research.
Clinical Snapshot
PICO Framework
| P — Population | Researchers using routine healthcare data from electronic patient records |
| I — Intervention | Contextual understanding and appropriate analytical approaches for routine data |
| C — Comparator | Traditional 'dirty data' cleaning approaches |
| O — Outcomes | Accurate interpretation and meaningful research insights from routine healthcare data |
Bottom Line
This methodological guidance challenges the common perception of routine healthcare data as simply 'dirty' research data requiring cleaning. Instead, it advocates for understanding routine data as meaningful records of healthcare processes that require contextual interpretation. The authors provide practical strategies including analysis at source and close collaboration with data providers. For Australian researchers working with electronic health records, this approach could significantly improve research validity and reduce misinterpretation. The emphasis on preserving data provenance is particularly relevant as Australia expands its digital health infrastructure. While the paper lacks formal evaluation of its proposed methods, the conceptual framework offers valuable guidance for maximising the research potential of routine healthcare data while maintaining analytical rigour.
Key Findings
Effect Size: Not applicable - methodological guidance
Primary Outcome: Methodological framework for approaching routine healthcare data
Nnt Or Sensitivity: Not applicable - qualitative methodological guidance
Clinical Application
Highly feasible - requires training and collaboration rather than new technology Highly relevant to Australian Digital Health initiatives, My Health Record system, and state-based health data collections. Aligns with NHMRC guidelines on health data research and could inform Australian Institute of Health and Welfare data governance practices Healthcare researchers, data analysts, and clinicians using electronic health records for research
Abstract
The rise of electronic patient records has led to an abundance of routine healthcare data, and the development of NHS Secure Data Environments (SDEs) is transforming their potential for research. However, routine data differ fundamentally from research data; they are generated to support care processes rather than answer predefined research questions. This creates challenges when they are analysed without understanding the context in which they were produced.Using real examples from practice, this paper illustrates the variety of ways routine data can be shaped and how this leads to misinterpretation if treated as merely 'dirty' research data. These examples underpin practical strategies for researchers, including analysis at or by source and working closely with local data providers to understand how a dataset was created.We argue that SDEs must foster strong links between data users and creators to preserve data provenance and support accurate interpretation. By approaching routine data as meaningful records of healthcare processes-rather than flawed research datasets-researchers can fully realise their potential to drive healthcare improvement.
References
- 1.Duffy, G., Cunningham, A., Sharma, V., & Lawton, T. (2026). Routine data are not 'dirty' data: how to approach using routine data for research. BMJ Health & Care Informatics, 33, e102018. https://doi.org/10.1136/bmjhci-2025-102018
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