Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections

The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI).A manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis.An adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52-68% and retained sensitivities of 69-73%.Structured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties

Epidemic Surveillance Using an Electronic Medical Record: An Empiric Approach to Performance Improvement

<div><p>Backgrounds</p><p>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).</p><p>Methods</p><p>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.</p><p>Results</p><p>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.</p><p>Conclusion</p><p>An empiric approach can guide the integration of EMR data into case-detection methods that improve both the timeliness and efficiency of epidemic detection.</p></div