Evidence map›Paper›PMID 31189204›Full record

ArticleApplied clinical informatics2019

Consensus Development of a Modern Ontology of Emergency Department Presenting Problems-The Hierarchical Presenting Problem Ontology (HaPPy).

Steven Horng, Nathaniel R Greenbaum, Larry A Nathanson, James C McClay, Foster R Goss, Jeffrey A Nielson

Abstract readConsensus Statement
In one paragraph

Article in Applied clinical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Why do we need quality measures in emergency medicine?Journal of the American College of Emergency Physicians open · 2024
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Steven HorngDivision of Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States.
Nathaniel R GreenbaumDivision of Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States.
Larry A NathansonDivision of Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States.
James C McClayDepartment of Emergency Medicine, College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska, United States.
Foster R GossDepartment of Emergency Medicine, University of Colorado Hospital, University of Colorado School of Medicine, Aurora, Colorado, United States.
Jeffrey A NielsonNortheastern Ohio Medical University, University Hospitals Samaritan Medical Center, Ashland, Ohio, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveNumerous attempts have been made to create a standardized "presenting problem" or "chief complaint" list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were not freely shareable or did not contain the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges. MATERIALS AND

methodsWe prospectively captured the presenting problems for 180,424 consecutive emergency department patient visits at an urban, academic, Level I trauma center in the Boston metro area. No patients were excluded. We used a consensus process to iteratively derive our system using real-world data. We used the first 70% of consecutive visits to derive our ontology, followed by a 6-month washout period, and the remaining 30% for validation. All concepts were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT).

resultsOur system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 nonvisible descriptions to correct misspellings and nonstandard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation data set. DISCUSSION AND

conclusionWe present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived and then iteratively validated by an expert consensus panel. HaPPy contains 692 presenting problem concepts, each concept being mapped to SNOMED CT. This freely sharable ontology can help to facilitate presenting problem-based quality metrics, research, and patient care.

Indexed as

Ambulatory CareBiological OntologiesEmergency Service, HospitalFemaleHumansMaleMiddle AgedReference Standards

Identifiers

PMID31189204
PMCPMC6561773

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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.