Epidemic Surveillance Using an Electronic Medical Record: An Empiric Approach to Performance Improvement Hongzhang Zheng 1,2 , Holly Gaff 3 , Gary Smith 4 , Sylvain DeLisle 1,2 * 1 Veterans Affairs Maryland Health Care System, Baltimore, Maryland, United States of America, 2 School of Medicine, University of Maryland, Baltimore, Maryland, United States of America, 3 Department of Biological Sciences, Old Dominion University, Norfolk, Virginia, United States of America, 4 School of Veterinary Medicine, University of Pennsylvania, Kennett Square, Pennsylvania, United States of America Abstract Backgrounds: Electronic medical records (EMR) form a rich repository of information that could benefit public health. We asked how structured and free-text narrative EMR data should be combined to improve epidemic surveillance for acute respiratory infections (ARI). Methods: Eight previously characterized ARI case detection algorithms (CDA) were applied to historical EMR entries to create authentic time series of daily ARI case counts (background). An epidemic model simulated influenza cases (injection). From the time of the injection, cluster-detection statistics were applied daily on paired background+injection (combined) and background-only time series. This cycle was then repeated with the injection shifted to each week of the evaluation year. We computed: a) the time from injection to the first statistical alarm uniquely found in the combined dataset (Detection Delay); b) how often alarms originated in the background-only dataset (false-alarm rate, or FAR); and c) the number of cases found within these false alarms (Caseload). For each CDA, we plotted the Detection Delay as a function of FAR or Caseload, over a broad range of alarm thresholds. Results: CDAs that combined text analyses seeking ARI symptoms in clinical notes with provider-assigned diagnostic codes in order to maximize the precision rather than the sensitivity of case-detection lowered Detection Delay at any given FAR or Caseload. Conclusion: An empiric approach can guide the integration of EMR data into case-detection methods that improve both the timeliness and efficiency of epidemic detection. Citation: Zheng H, Gaff H, Smith G, DeLisle S (2014) Epidemic Surveillance Using an Electronic Medical Record: An Empiric Approach to Performance Improvement. PLoS ONE 9(7): e100845. doi:10.1371/journal.pone.0100845 Editor: Gerardo Chowell, Arizona State University, United States of America Received December 13, 2013; Accepted May 30, 2014; Published July 9, 2014 This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Funding: This material is the result of work supported with resources and the use of facilities at the VA Maryland Health Care System. This work was also supported in part by grant IIR 06-119-1 from the Office of Research and Development, Health Services Research and Development Service, Department of Veterans Affairs. Development and validation of the ARI case-detection algorithm was supported by grants R01 CI000098 and 5U38HK000013-02 from the Centers for Disease Control and Prevention. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * Email: sdelisle@umaryland.edu Introduction Epidemics of acute respiratory infections (ARI), whether due to influenza [1,2], coronaviruses [3,4], or other pathogens [5,6], could overwhelm even the most developed health care systems. It is imperative to recognize these epidemics as early as possible, as the passage of time quickly degrades the effectiveness of mitigating measures [7]. Electronic data offer the opportunity for more timely and complete gathering of health information compared to what has historically been achieved through manual, paper-based reporting [8]. The increasingly rapid deployment of electronic medical records (EMR) [9] broadens the array of data that could be recruited for surveillance purposes [10,11]. EMR-based surveil- lance could improve our response to a serious outbreak of ARI not only by allowing earlier recognition, but also by offering an efficient conduit for the information necessary to manage actual patients and to keep abreast of the evolving epidemic [12–14]. At present, however, the tantalizing potential of EMR-based surveil- lance remains in the making [15–18]. To gain insight on the conduct of surveillance in an EMR environment, we previously evaluated how EMR entries should be assembled to discover individuals with ARI [19]. We found that computerized free-text analyses aimed at uncovering ARI symptoms documented in outpatient clinical notes could comple- ment diagnostic codes and other structured data to improve case detection. In this report, we asked if those EMR-enabled gains in case-detection could accelerate the discovery of ARI outbreaks. Using software to reconstitute a surveillance system operating prospectively on historical data sets, we compared alternative case- detection approaches for their ability to reduce the delay in detecting a modeled community outbreak of influenza. Our PLOS ONE | www.plosone.org 1 July 2014 | Volume 9 | Issue 7 | e100845