DeLisle Sylvain

Information about disease severity could help with both detection and situational awareness during outbreaks of acute respiratory infections (ARI). In this work, we use data from the EMR to identify patients with pneumonia, a key landmark of ARI severity. We asked if computerized analysis of the free-text of clinical notes or imaging reports could complement structured EMR data to uncover pneumonia cases.

Objective

To improve the surveillance for pneumonia using the free-text of electronic medical records (EMR).

Effective responses to epidemics of infectious diseases hinge not only on early outbreak detection, but also on an assessment of disease severity. In recent work, we combined previously developed ARI case-detection algorithms (CDA) [1] with text analyses of chest imaging reports to identify ARI patients whose providers thought had pneumonia. In this work, we asked if a surveillance system aimed at patients with pneumonia would outperform one that monitors the full severity spectrum of ARI.

Objective

To determine if influenza surveillance should target all patients with acute respiratory infections (ARI) or only track pneumonia cases.

 

With economic pressures to shift the care of community-acquired pneumonia (CAP) to the ambulatory setting, there is a need to ensure safety of outpatients with CAP. The use of claims data alone remains the primary strategy for identifying these patients, but billing information often does not match the clinical diagnosis and does not have the ability to find unrecognized cases. In our previous work, an automated pneumonia case detection algorithm (CDA) was able to detect cases of CAP with positive predictive value of 71%. For this study, we begin to illustrate how this type of surveillance system may assist in evaluating the quality of outpatient care for CAP.