EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes

Abstract

ABSTRACTThe adoption of electronic health records (EHRs) has created opportunities to analyze historical data for predicting clinical outcomes and improving patient care. However, non-standardized data representations and anomalies pose major challenges to the use of EHRs in digital health research. To address these challenges, we have developed EHR-QC, a tool comprising two modules: the data standardization module and the preprocessing module. The data standardization module migrates source EHR data to a standard format using advanced concept mapping techniques, surpassing expert curation in benchmarking analysis. The preprocessing module includes several functions designed specifically to handle healthcare data subtleties. We provide automated detection of data anomalies and solutions to handle those anomalies. We believe that the development and adoption of tools like EHR-QC is critical for advancing digital health. Our ultimate goal is to accelerate clinical research by enabling rapid experimentation with data-driven observational research to generate robust, generalisable biomedical knowledge.HighlightsEHR-QC accepts EHR data from a relational database or as a flat file and provide an easy-to-use, customized, and comprehensive solution for data handling activities.It offers a modular standardization pipeline that can convert any EHR data to a standardized data model i.e. OMOP-CDM.It includes an innovative algorithmic solution for clinical concept mapping that surpasses the current expert curation process.We have demonstrated that the imputation performance depends on the nature and missing proportion, hence as part of EHR-QC we included a method that searches for the best imputation method for the given data.It also contains an end-to-end solution to handle other anomalies such as outliers, errors, and other inconsistencies in the EHR data.