Abstract
BackgroundThere have been few studies describing how production EMR systems can be systematically queried to identify clinically-defined populations and limited studies utilising free-text in this process. The aim of this study is to provide a generalisable methodology for constructing clinically-defined EMR-derived patient cohorts using structured and unstructured data in EMRs.MethodsPatients with possible acute coronary syndrome (ACS) were used as an exemplar. Cardiologists defined clinical criteria for patients presenting with possible ACS. These were mapped to data tables within the production EMR system creating seven inclusion criteria comprised of structured data fields (orders and investigations, procedures, scanned electrocardiogram (ECG) images, and diagnostic codes) and unstructured clinical documentation. Data were extracted from two local health districts (LHD) in Sydney, Australia. Outcome measures included examination of the relative contribution of individual inclusion criteria to the identification of eligible encounters, comparisons between inclusion criterion and evaluation of consistency of data extracts across years and LHDs.ResultsAmong 802,742 encounters in a 5 year dataset (1/1/13–30/12/17), the presence of an ECG image (54.8% of encounters) and symptoms and keywords in clinical documentation (41.4–64.0%) were used most often to identify presentations of possible ACS. Orders and investigations (27.3%) and procedures (1.4%), were less often present for identified presentations. Relevant ICD-10/SNOMED CT codes were present for 3.7% of identified encounters. Similar trends were seen when the two LHDs were examined separately, and across years.ConclusionsClinically-defined EMR-derived cohorts combining structured and unstructured data during cohort identification is a necessary prerequisite for critical validation work required for development of real-time clinical decision support and learning health systems.
Highlights
There have been few studies describing how production electronic medical records (EMR) systems can be systematically queried to identify clinically-defined populations and limited studies utilising free-text in this process
To further examine the composition of inclusion criteria in eligible encounters, UpSet plots [27] were used to represent the frequency of each inclusion criterion and the numbers of encounters that met each combination of inclusion criteria in 2017, the most recent data in our 5-year data extract
Comparisons between diagnostic codes and other inclusion criteria In the 5-year extract, we examined the proportion of encounters that contained a relevant diagnostic code within each cohort of encounters met by each inclusion criterion
Summary
There have been few studies describing how production EMR systems can be systematically queried to identify clinically-defined populations and limited studies utilising free-text in this process. Tam et al BMC Med Inform Decis Mak (2021) 21:91 pragmatic clinical trials [1,2,3] Administrative coding systems such as International Classification of Diseases and Related Health Problems (ICD)-10 provide a translation of healthcare diagnoses, procedures, medical services, and medical equipment into universal codes [4], do not provide a granular view of a patient’s presentation, severity of disease and clinical sequence during an episode of care [4,5,6] and have variable accuracy [7]. Approaches to data extraction from production EMR systems have to consider whether there is ready access to production-level EMR environments (e.g. live systems, back-ups, copies of the production EMR etc.) which may limit the granularity and timeliness of information that is available for cohort identification as well as the opportunity for iteration during the cohort identification process
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