Electronic health records epic8/12/2023 ![]() ![]() Via this process, Cleveland Clinic can do live population exploration as well as produce datasets for analysis faster than it takes most organizations to simply identify their base population.įor this data repository, we utilize Unified Medical Language System (UMLS) identifiers. Approximately 185 tables from different data sources are condensed into 18 research-ready tables in the data repository automatically on a weekly basis. The raw data are extracted from both the EHR and other disparate data sources, mapped to discrete ontologies, cleaned and standardized, and finally deposited into a clinical research data repository. To provide the cleanest and most robust datasets for statistical analysis, numerous statistical techniques including similarity calculations and fuzzy matching are used to clean, parse, map and validate the raw EHR data. The rest are identifiers, dates, and free-text entries. However, at Cleveland Clinic, less than 5% of the EHR data are codified variables. Extracting EHR data is a difficult, time consuming, and often a pragmatic process.Ĭleveland Clinic adopted Epic’s EHR system in 1995 in the laboratories, and expanded to include medications in 1998, Epic outpatient in 2000, surgical histories in 2002, and Epic inpatient in 2005. Intimate knowledge of the data structure of the EHR is necessary for even the simplest of queries. Many data points are duplicated and are reliant on upon a very small set of validation criteria shown to the data entry personnel. Working directly with EHR data for statistical analysis is a challenge in and of itself. Raw electronic health record (EHR) data are disorganized and full of uncodified variables. Keywords: Electronic health records (EHRs) Epic Unified Medical Language System (UMLS) ![]() Policy of Dealing with Allegations of Research Misconduct.Policy of Screening for Plagiarism Process. ![]()
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