Miao, ZhuqiSealey, Meghan D.Sathyanarayanan, ShrieraamDelen, DursunZhu, LanShepherd, Scott2022-11-072022-11-072023Miao, Z., Sealey, M. D., Sathyanarayanan, S., Delen, D., Zhu, L., & Shepherd, S. (2023). A data preparation framework for cleaning electronic health records and assessing cleaning outcomes for secondary analysis. Information Systems, 111 doi:10.1016/j.is.2022.102130https://doi.org/10.1016/j.is.2022.102130https://hdl.handle.net/20.500.12713/3247Even though data preparation constitutes a large proportion of the total effort involved in electronic health record (EHR) based secondary analysis, guidelines for EHR data preparation are still insufficient to date. This study proposes a data preparation framework that can guide and validate the cleaning of EHRs for secondary analysis. The developed framework consists of three core themes—workflow, assessment and cleaning methods, and cleaning evaluation scheme. To illustrate the viability of the proposed framework, we applied it to a hip-fracture readmission scenario using the underlying data extracted from a large EHR database. The case study demonstrated the effectiveness of the proposed framework in organizing and standardizing phases and processes within an EHR data preparation workflow. Furthermore, the cleaning evaluation scheme was found to be effective in validating EHR cleaning methods, especially for those used to handle complex issues that usually appear in patient demographics, longitudinal attributes of EHRs, and the application of filtering and imputation cleaning methods.eninfo:eu-repo/semantics/closedAccessData CleaningData QualityElectronic Health Records (EHR)HealthcareSecondary AnalysisArticle111WOS:0010293110000012-s2.0-85139288803Q210.1016/j.is.2022.102130N/A