About: Using the Electronic Medical Record to Identify Community-Acquired Pneumonia: Toward a Replicable Automated Strategy

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Using the Electronic Medical Record to Identify Community-Acquired Pneumonia: Toward a Replicable Automated Strategy
Creator	Gundlapalli, Adi Samore, Matthew Deepak, Janaki Kim, Bernard Siddiqui, Tariq Delisle, Sylvain D'avolio, Leonard
source	PMC
abstract	BACKGROUND: Timely information about disease severity can be central to the detection and management of outbreaks of acute respiratory infections (ARI), including influenza. We asked if two resources: 1) free text, and 2) structured data from an electronic medical record (EMR) could complement each other to identify patients with pneumonia, an ARI severity landmark. METHODS: A manual EMR review of 2747 outpatient ARI visits with associated chest imaging identified x-ray reports that could support the diagnosis of pneumonia (kappa score = 0.88 (95% CI 0.82∶0.93)), along with attendant cases with Possible Pneumonia (adds either cough, sputum, fever/chills/night sweats, dyspnea or pleuritic chest pain) or with Pneumonia-in-Plan (adds pneumonia stated as a likely diagnosis by the provider). The x-ray reports served as a reference to develop a text classifier using machine-learning software that did not require custom coding. To identify pneumonia cases, the classifier was combined with EMR-based structured data and with text analyses aimed at ARI symptoms in clinical notes. RESULTS: 370 reference cases with Possible Pneumonia and 250 with Pneumonia-in-Plan were identified. The x-ray report text classifier increased the positive predictive value of otherwise identical EMR-based case-detection algorithms by 20–70%, while retaining sensitivities of 58–75%. These performance gains were independent of the case definitions and of whether patients were admitted to the hospital or sent home. Text analyses seeking ARI symptoms in clinical notes did not add further value. CONCLUSION: Specialized software development is not required for automated text analyses to help identify pneumonia patients. These results begin to map an efficient, replicable strategy through which EMR data can be used to stratify ARI severity.
has issue date	2013-08-13(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0070944
bibo:pmid	23967138
has license	cc0
sha1sum (hex)	a4e2f812c3232e60ac1af78fed10d292e28906d1
schema:url	https://doi.org/10.1371/journal.pone.0070944
resource representing a document's title	Using the Electronic Medical Record to Identify Community-Acquired Pneumonia: Toward a Replicable Automated Strategy
has PubMed Central identifier	PMC3742728
has PubMed identifier	23967138
schema:publication	PLoS One
resource representing a document's body	covid:a4e2f812c3232e60ac1af78fed10d292e28906d1#body_text
is schema:about of	named entity 'ELECTRONIC MEDICAL RECORD' named entity 'acute' named entity 'central' covid:arg/a4e2f812c3232e60ac1af78fed10d292e28906d1 named entity 'USING' named entity 'AUTOMATED' named entity 'OUTBREAKS' named entity 'INFLUENZA' named entity 'ELECTRONIC MEDICAL RECORD' named entity 'TO IDENTIFY' named entity 'STRUCTURED DATA' named entity 'BACKGROUND' named entity 'CENTRAL' named entity 'PNEUMONIA' named entity 'DETECTION' named entity 'TIMELY' named entity 'PATIENTS' named entity 'TO IDENTIFY' named entity 'STRATEGY' named entity 'COMMUNITY-ACQUIRED PNEUMONIA' named entity 'DISEASE SEVERITY' named entity 'MANAGEMENT' named entity 'RESOURCES' named entity 'ABOUT' named entity 'INFORMATION' named entity 'ARI' named entity 'FREE TEXT' named entity 'COMPLEMENT' named entity 'ACUTE RESPIRATORY INFECTIONS' named entity 'SEVERITY' named entity 'INCLUDING' named entity 'LANDMARK' named entity 'respiratory infections' named entity 'outbreaks' named entity 'influenza' named entity 'EMR' named entity 'structured data' named entity 'Pneumonia' named entity 'EMR' named entity 'chest imaging' named entity 'clinical trials' named entity 'EMR' named entity 'pneumonia' named entity 'machine-learning' named entity 'study population' named entity 'diagnostic code' named entity 'probabilistic classifier' named entity 'macrolides' named entity 'clinical signs' named entity 'pneumonia' named entity 'CDA' named entity 'cough' named entity 'chest imaging' named entity 'Redmond WA' named entity 'pneumonia' named entity 'chest imaging' named entity 'dyspnea' named entity 'CDA' named entity 'neural networks' named entity 'pneumonia' named entity 'ICD-9' named entity 'chest imaging' named entity 'pneumonia' named entity 'pneumonia' named entity 'PPV' named entity 'pneumonia' named entity 'EMR' named entity 'pneumonia' named entity 'Pneumonia' named entity 'plague' named entity 'Institutional Review Boards' named entity 'PPV' named entity 'chest imaging' named entity 'Pneumonia' named entity 'patient care'

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