BackgroundThe 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).MethodologyA 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.Principal FindingsAn 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%.ConclusionStructured 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.

BackgroundsOver 50% of antibiotics prescriptions are for outpatients with acute respiratory infections (ARI). Many of them are not needed and thus contribute both avoidable adverse events and pressures toward the development of bacterial resistance. Could a clinical decision support system (CDSS), interposed at the time of electronic prescription, adjust antibiotics utilization toward consensus treatment guidelines for ARI?MethodsThis is a retrospective comparison of pre- (2002) and post-intervention (2003–2006) periods at two comprehensive health care systems (intervention and control). The intervention was a CDSS that targeted fluoroquinolone and azithromycin; other antibiotics remained unrestricted. 7000 outpatients visits flagged by an ARI case-finding algorithm were reviewed for congruence with the guidelines (antibiotic prescribed-when-warranted or not-prescribed-when-unwarranted).Results3831 patients satisfied the case definitions for one or more ARI: pneumonia (537), bronchitis (2931), sinusitis (717) and non-specific ARI (145). All patients with pneumonia received antibiotics. The relative risk (RR) of congruent prescribing was 2.57 (95% CI = (1.865 to 3.540) in favor of the intervention site for the antibiotics targeted by the CDSS; congruence did not change for other antibiotics (adjusted RR = 1.18 (95% CI = (0.691 to 2.011)). The proportion of unwarranted prescriptions of the targeted antibiotics decreased from 22% to 3%, pre vs. post-intervention (p<0.0001).ConclusionsA CDSS interposed at the time of e-prescription nearly extinguished unwarranted use targeted antibiotics for ARI for four years. This intervention highlights a path toward sustainable antibiotics stewardship for outpatients with ARI.

BackgroundTimely 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.MethodsA 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.Results370 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.ConclusionSpecialized 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.