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Syndromic surveillance for influenza-like illness (ILI) is predominantly performed in the outpatient setting. The objective of this study was to compare patterns of ILI activity in outpatient, emergency department (ED), and inpatient settings using an electronic syndromic surveillance algorithm.
Retrospective cohort study over 7.5 years.
A large community health system comprised of 5 hospitals and >50 clinics.
We applied an electronic syndromic surveillance algorithm for ILI to all primary-care outpatient visits, inpatient encounters, and ED encounters at our health system. Comparisons of ILI activity over time were performed using Spearman’s rank correlation coefficient. Cross correlation was used to compare the timing of ILI activity among treatment settings.
Overall, 4,447,769 patient encounters occurred during the study period; 152,607 of these (3.4%) were consistent with ILI. The correlation coefficient for ILI activity in the outpatient versus ED setting was 0.877, and for the outpatient versus inpatient setting, the correlation coefficient was 0.699. ILI activity among outpatients preceded ILI activity among inpatients by 1 week. ILI activity among children in the outpatient setting preceded ILI activity among adults in all 3 settings by 1 week.
Syndromic surveillance for ILI in the outpatient setting yields similar results to surveillance in the ED setting, but it produces less similar results than ILI surveillance in the inpatient setting. ILI activity in the pediatric outpatient population is a potential predictor of future ILI activity in the general population.
Healthcare providers need a better empiric antibiotic prescribing aid than the traditional antibiogram, which supplies no information on the relative frequency of organisms recovered in a given infection and which is uninformative in situations where multiple antimicrobials are used or multiple organisms are anticipated. We aimed to develop and demonstrate a novel empiric prescribing decision aid.
This is a demonstration involving more than 9,000 unique encounters for abdominal-biliary infection (ABI) and urinary tract infection (UTI) to a large healthcare system with a fully integrated electronic health record (EHR).
We developed a novel method of displaying microbiology data called the weighted-incidence syndromic combination antibiogram (WISCA) for 2 clinical syndromes, ABI and UTI. The WISCA combines simple diagnosis and microbiology data from the EHR to (1) classify patients by syndrome and (2) determine, for each patient with a given syndrome, whether a given regimen (1 or more agents) would have covered all the organisms recovered for their infection. This allows data to be presented such that clinicians can see the probability that a particular regimen will cover a particular infection rather than the probability that a single drug will cover a single organism.
There were 997 encounters for ABI and 8,232 for UTI. A WISCA was created for each syndrome and compared with a traditional antibiogram for the same period.
Novel approaches to data compilation and display can overcome limitations to the utility of the traditional antibiogram in helping providers choose empiric antibiotics.
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