- Title
- Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts
- Creator
- Tam, Charmaine S.; Gullick, Janice; Saavedra, Aaldo; Vernon, Stephen T.; Figtree, Gemma A.; Chow, Clara K.; Cretikos, Michelle; Morris, Richard W.; William, Maged; Morris, Jonathan; Brieger, David
- Relation
- BMC Medical Informatics and Decision Making Vol. 21, Issue 1, no. 91
- Publisher Link
- http://dx.doi.org/10.1186/s12911-021-01441-w
- Publisher
- BioMed Central
- Resource Type
- journal article
- Date
- 2021
- Description
- 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.
- Subject
- Electronic medical record; Cohort identification; Electronic phenotype; Acute coronary syndrome
- Identifier
- http://hdl.handle.net/1959.13/1447540
- Identifier
- uon:43182
- Identifier
- ISSN:1472-6947
- Rights
- Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
- Language
- eng
- Reviewed
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