Evidence map›Paper›PMID 33685456›Full record

ArticleBMC medical informatics and decision making2021

Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts.

Charmaine S Tam, Janice Gullick, Aldo Saavedra, Stephen T Vernon, Gemma A Figtree, Clara K Chow, Michelle Cretikos, Richard W Morris, Maged William, Jonathan Morris and 1 more

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In one paragraph

Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Charmaine S TamCentre for Translational Data Science, The University of Sydney, Sydney, Australia. charmaine.tam@sydney.edu.au.ORCID 0000-0002-4034-9923
Janice GullickSusan Wakil School of Nursing and Midwifery, The University of Sydney, Sydney, Australia.
Aldo SaavedraCentre for Translational Data Science, The University of Sydney, Sydney, Australia.
Stephen T VernonCardiothoracic and Vascular Health, Kolling Institute of Medical Research and Department of Cardiology, Royal North Shore Hospital, Northern Sydney Local Health District, Sydney, Australia.
Gemma A FigtreeNorthern Clinical School, The University of Sydney, Sydney, Australia.
Clara K ChowWestmead Applied Research Centre, The University of Sydney, Sydney, Australia.
Michelle CretikosCentre for Population Health, NSW Ministry of Health, Sydney, Australia.
Richard W MorrisCentre for Translational Data Science, The University of Sydney, Sydney, Australia.
Maged WilliamDepartment of Cardiology, Central Coast Local Health District and University of Newcastle, Sydney, Australia.
Jonathan MorrisNorthern Clinical School, The University of Sydney, Sydney, Australia.
David BriegerDepartment of Cardiology, Concord Hospital, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Decision Support Systems, ClinicalElectronic Health RecordsAustraliaDocumentationHumansInternational Classification of DiseasesAcute coronary syndromeCohort identificationElectronic medical recordElectronic phenotype

Identifiers

PMID33685456
PMCPMC7938556

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.