Among 802,742 encounters for possible ACS, combining structured and unstructured EMR data identified cases primarily via ECG images (54.8%) and clinical documentation keywords (41.4-64.0%), whereas diagnostic codes alone were present in only 3.7% of identified encounters.
Observational (n=802,742)
Yes
802,742 eligible encounters for possible acute coronary syndrome identified from a 5-year EMR dataset across two local health districts in Sydney, Australia.
Combining structured and unstructured EMR data vs Diagnostic codes alone (ICD-10/SNOMED CT)
Proportion of eligible encounters identified by different EMR data elements
BACKGROUND: 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. 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. METHODS: Patients 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. RESULTS: Among 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. CONCLUSIONS: Clinically-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.
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Charmaine S. Tam
The University of Sydney
Janice Gullick
The University of Sydney
Aldo Saavedra
The University of Sydney
BMC Medical Informatics and Decision Making
The University of Sydney
Westmead Hospital
Royal North Shore Hospital
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Tam et al. (Mon,) conducted a observational in Possible acute coronary syndrome (ACS) (n=802,742). Combining structured and unstructured EMR data vs. Diagnostic codes alone (ICD-10/SNOMED CT) was evaluated on Proportion of eligible encounters identified by different EMR data elements. Among 802,742 encounters for possible ACS, combining structured and unstructured EMR data identified cases primarily via ECG images (54.8%) and clinical documentation keywords (41.4-64.0%), whereas diagnostic codes alone were present in only 3.7% of identified encounters.
synapsesocial.com/papers/6a200f9dc1b320180d0dde98 — DOI: https://doi.org/10.1186/s12911-021-01441-w