title: Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.
abstract: BACKGROUND
  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.
  
  
  CONCLUSIONS
  An empiric approach can guide the integration of EMR data into case-detection methods that improve both the timeliness and efficiency of epidemic detection.
authors.affiliation: Veterans Affairs Maryland Health Care System, Baltimore, Maryland, United States of America; School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
authors.forename: Hongzhang
authors.initials: H
authors.lastname: Zheng
authors.affiliation: Department of Biological Sciences, Old Dominion University, Norfolk, Virginia, United States of America.
authors.forename: Holly
authors.initials: H
authors.lastname: Gaff
authors.affiliation: School of Veterinary Medicine, University of Pennsylvania, Kennett Square, Pennsylvania, United States of America.
authors.forename: Gary
authors.initials: G
authors.lastname: Smith
authors.affiliation: Veterans Affairs Maryland Health Care System, Baltimore, Maryland, United States of America; School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
authors.forename: Sylvain
authors.initials: S
authors.lastname: DeLisle
mesh_terms: D000208:Acute Disease; D019540:Area Under Curve; D003198:Computer Simulation; D057286:Electronic Health Records; D058872:Epidemics; D062665:Epidemiological Monitoring; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D008396:Maryland / Q000453:epidemiology; D008954:Models, Biological; D058996:Quality Improvement; D012372:ROC Curve