Evidence map›Paper›PMID 32851617›Full record

ArticleApplied clinical informatics2020

Development and Evaluation of a Fully Automated Surveillance System for Influenza-Associated Hospitalization at a Multihospital Health System in Northeast Ohio.

Patrick C Burke, Rachel Benish Shirley, Jacob Raciniewski, James F Simon, Robert Wyllie, Thomas G Fraser

Abstract readEvaluation Study
In one paragraph

Article in Applied clinical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Patrick C BurkeDepartment of Infection Prevention, Enterprise Quality and Patient Safety, Cleveland Clinic, Cleveland, Ohio, United States.
Rachel Benish ShirleyEnterprise Quality and Patient Safety, Cleveland Clinic, Cleveland, Ohio, United States.
Jacob RaciniewskiDepartment of Enterprise Analytics, Cleveland Clinic, Cleveland, Ohio, United States.
James F SimonMedical Operations Department, Cleveland Clinic, Cleveland, Ohio, United States.
Robert WyllieMedical Operations Department, Cleveland Clinic, Cleveland, Ohio, United States.
Thomas G FraserDepartment of Infectious Diseases, Cleveland Clinic, Cleveland, Ohio, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPerforming high-quality surveillance for influenza-associated hospitalization (IAH) is challenging, time-consuming, and essential.

objectivesOur objectives were to develop a fully automated surveillance system for laboratory-confirmed IAH at our multihospital health system, to evaluate the performance of the automated system during the 2018 to 2019 influenza season at eight hospitals by comparing its sensitivity and positive predictive value to that of manual surveillance, and to estimate the time and cost savings associated with reliance on the automated surveillance system.

methodsInfection preventionists (IPs) perform manual surveillance for IAH by reviewing laboratory records and making a determination about each result. For automated surveillance, we programmed a query against our Enterprise Data Vault (EDV) for cases of IAH. The EDV query was established as a dynamic data source to feed our data visualization software, automatically updating every 24 hours.To establish a gold standard of cases of IAH against which to evaluate the performance of manual and automated surveillance systems, we generated a master list of possible IAH by querying four independent information systems. We reviewed medical records and adjudicated whether each possible case represented a true case of IAH.

resultsWe found 844 true cases of IAH, 577 (68.4%) of which were detected by the manual system and 774 (91.7%) of which were detected by the automated system. The positive predictive values of the manual and automated systems were 89.3 and 88.3%, respectively.Relying on the automated surveillance system for IAH resulted in an average recoup of 82 minutes per day for each IP and an estimated system-wide payroll redirection of $32,880 over the four heaviest weeks of influenza activity.

conclusionSurveillance for IAH can be entirely automated at multihospital health systems, saving time, and money while improving case detection.

Indexed as

Electronic Health RecordsEpidemiological MonitoringAutomationData MiningHospitalizationHumansInfluenza, HumanLaboratoriesOhioSoftware

Identifiers

PMID32851617
PMCPMC7449789

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