Abstract
Research with structured Electronic Health Records (EHRs) is expanding as data becomes more accessible; analytic methods advance; and the scientific validity of such studies is increasingly accepted. However, data science methodology to enable the rapid searching/extraction, cleaning and analysis of these large, often complex, datasets is less well developed. In addition, commonly used software is inadequate, resulting in bottlenecks in research workflows and in obstacles to increased transparency and reproducibility of the research. Preparing a research-ready dataset from EHRs is a complex and time consuming task requiring substantial data science skills, even for simple designs. In addition, certain aspects of the workflow are computationally intensive, for example extraction of longitudinal data and matching controls to a large cohort, which may take days or even weeks to run using standard software. The rEHR package simplifies and accelerates the process of extracting ready-for-analysis datasets from EHR databases. It has a simple import function to a database backend that greatly accelerates data access times. A set of generic query functions allow users to extract data efficiently without needing detailed knowledge of SQL queries. Longitudinal data extractions can also be made in a single command, making use of parallel processing. The package also contains functions for cutting data by time-varying covariates, matching controls to cases, unit conversion and construction of clinical code lists. There are also functions to synthesise dummy EHR. The package has been tested with one for the largest primary care EHRs, the Clinical Practice Research Datalink (CPRD), but allows for a common interface to other EHRs. This simplified and accelerated work flow for EHR data extraction results in simpler, cleaner scripts that are more easily debugged, shared and reproduced.
Highlights
We present the R R [1] package rEHR for manipulating and analysing Electronic Health Record (EHR) data and demonstrate its use with rEHR-generated synthetic data. rEHR is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project. org/package=rEHR, and will work with R-3.3.2
Comprehensive anonymised extracts of these UK primary care records are made available for research—the main sources are: The Clinical Practice Research Datalink (CPRD, previously known as the General Practice Research Database, GPRD), The Health Improvement Network (THIN), QResearch, The Doctors’ Independent Network (DIN-LINK) and more recently, Research One
Once EHR data has been imported to the database, the rEHR package has a number of flexible built-in querying functions for extracting data
Summary
We present the R R [1] package rEHR for manipulating and analysing Electronic Health Record (EHR) data and demonstrate its use with rEHR-generated synthetic data. rEHR is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project. org/package=rEHR, and will work with R-3.3.2. PCDs such as CPRD store data in complex relational and nested structures, and preparing an analysis-ready dataset requires substantial data science skills, even for simple designs This complexity is an inevitable consequence of the wide range of information contained within these databases, which detail the primary care history for every patient, including coded data for all diagnoses, prescriptions, referrals and test results for all consultations. EHR data are stored as relational databases but are most commonly made available to researchers in the form of flat text files. This has the advantage of easier access for simple tasks and, for example, viewing the files in a spreadsheet. A regex argument should be supplied that is a regular expression to match a common prefix to the filenames, separated from the file type by an underscore
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