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

Details

• Alternative Title:
  PLoS One
• Personal Author:
  DeLisle, Sylvain ; Kim, Bernard ; Deepak, Janaki ; Siddiqui, Tariq ; Gundlapalli, Adi ; Samore, Matthew ; D'Avolio, Leonard
• Description:
  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.
  
  
• Subjects:
  Adolescent Adult Aged Aged, 80 And Over Algorithms Community-Acquired Infections Electronic Health Records Female Humans Male Middle Aged Outpatients Pneumonia Reproducibility Of Results Software Young Adult

  More +
• Source:
  PLoS One. 2013; 8(8).
• Pubmed ID:
  23967138
• Pubmed Central ID:
  PMC3742728
• Document Type:
  Journal Article
• Funding:
  5U38HK000013-02/HK/PHITPO CDC HHS/United States ; R01 CI000098/CI/NCPDCID CDC HHS/United States
• Volume:
  8
• Issue:
  8
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:6a650de5b694ae62ac3e820228321a9eb362b7a672a57e61d7b3466b5082ddae
• Download URL:
  https://stacks.cdc.gov/view/cdc/22583/cdc_22583_DS1.pdf
• File Type:
  [PDF - 161.45 KB ]