Evidence map›Paper›PMID 32935127›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

Formal representation of patients' care context data: the path to improving the electronic health record.

Tiago K Colicchio, Pavithra I Dissanayake, James J Cimino

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Review
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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

3 authors.

Tiago K ColicchioInformatics Institute, University of Alabama at Birmingham, USA.
Pavithra I DissanayakeInformatics Institute, University of Alabama at Birmingham, USA.
James J CiminoInformatics Institute, University of Alabama at Birmingham, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop a collection of concept-relationship-concept tuples to formally represent patients' care context data to inform electronic health record (EHR) development. MATERIALS AND

methodsWe reviewed semantic relationships reported in the literature and developed a manual annotation schema. We used the initial schema to annotate sentences extracted from narrative note sections of cardiology, urology, and ear, nose, and throat (ENT) notes. We audio recorded ENT visits and annotated their parsed transcripts. We combined the results of each annotation into a consolidated set of concept-relationship-concept tuples. We then compared the tuples used within and across the multiple data sources.

resultsWe annotated a total of 626 sentences. Starting with 8 relationships from the literature, we annotated 182 sentences from 8 inpatient consult notes (initial set of tuples = 43). Next, we annotated 232 sentences from 10 outpatient visit notes (enhanced set of tuples = 75). Then, we annotated 212 sentences from transcripts of 5 outpatient visits (final set of tuples = 82). The tuples from the visit transcripts covered 103 (74%) concepts documented in the notes of their respective visits. There were 20 (24%) tuples used across all data sources, 10 (12%) used only in inpatient notes, 15 (18%) used only in visit notes, and 7 (9%) used only in the visit transcripts.

conclusionsWe produced a robust set of 82 tuples useful to represent patients' care context data. We propose several applications of our tuples to improve EHR navigation, data entry, learning health systems, and decision support.

Indexed as

Artificial IntelligenceCardiologyDecision Making, Computer-AssistedElectronic Health RecordsHumansNatural Language ProcessingOtolaryngologyclinical conceptsclinical decision supportclinical documentationelectronic health recordsknowledge representation

Identifiers

PMID32935127
PMCPMC7671623

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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.