Article Text
Abstract
Electronic patient records (EPRs) are potentially valuable sources of data for service development or research but often contain large amounts of missing data. Using complete case analysis or imputation of missing data seem like simple solutions, and are increasingly easy to perform in software packages, but can easily distort data and give misleading results if used without an understanding of missingness. So, knowing about patterns of missingness, and when to get expert data science (data engineering and analytics) help, will be a fundamental future skill for emergency physicians. This will maximise the good and minimise the harm of the easy availability of large patient datasets created by the introduction of EPRs.
- Data Interpretation, Statistical
- Routinely Collected Health Data
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Footnotes
Permission Part of this paper is reproduced with permission from a previous article from conference proceedings: N. Suzen, E. M. Mirkes, D. Roland, J. Levesley, A. N. Gorban and T. J. Coats, "What is Hiding in Medicine’s Dark Matter? Learning with Missing Data in Medical Practices," 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 4979-4986, doi: 10.1109/BigData59044.2023.10386194. Published out of sequence, the conference proceedings are a practical application of the concepts developed by Coats and Mirkes in this paper.
Handling editor Richard Body
X @TJCoats
Contributors TJC and EMM contributed equally to the concept, drafting and reviewing of this work and have agreed to the final manuscript. TJC is the guarantor of the work. The EMJ editors and reviewers of the manuscript made comments assisting in the revisions.
Funding This work was supported by the Health Foundation (Grant No 1747259).
Competing interests None declared.
Provenance and peer review Not commissioned; internally peer reviewed.