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Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis
Brett K Beaulieu-Jones1,2, Daniel R Lavage3, John W Snyder3
1Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
JMIR Medical Informatics
|February 25, 2018
Summary
Addressing missing data in electronic health records (EHRs) is crucial. This study demonstrates imputation methods for EHR laboratory results, offering practical guidance for researchers to minimize bias and improve data analysis.
Area of Science:
- Biostatistics
- Health Informatics
- Data Science
Background:
- Missing data in electronic health record (EHR) analyses can lead to biased results.
- Implementing sophisticated imputation methods for EHR data presents significant challenges for researchers.
- Detailed procedures are provided for the imputation of EHR laboratory results.
Purpose of the Study:
- Assess mechanisms of missingness in EHR data.
- Evaluate the performance of various imputation methods.
- Describe common challenges encountered during data imputation.
Main Methods:
- Analysis of clinical laboratory measures from 602,366 patients at Geisinger Health System.
- Simulation of missing data based on four mechanisms (MCAR, MNAR, MAR, real data).
- Assessment of 12 different imputation methods, including Multivariate Imputation by Chained Equations (MICE) and softImpute.
Main Results:
- Several methods, including MICE variations and softImpute, demonstrated consistent low-error imputation of missing values.
- Only a subset of MICE methods proved suitable for multiple imputation.
- Performance varied across methods, highlighting the need for careful selection.
Conclusions:
- The study outlines considerations for handling missing EHR data and characterizing missingness.
- Provides an evaluation of imputation methods applicable to clinical data.
- The described process is generalizable to structured EHR data types, with publicly available methods and code.
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