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Appl Clin Inform 2020; 11(04): 564-569
DOI: 10.1055/s-0040-1715651
Research Article

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

Patrick C. Burke
1   Department of Infection Prevention, Enterprise Quality and Patient Safety, Cleveland Clinic, Cleveland, Ohio, United States
,
Rachel Benish Shirley
2   Enterprise Quality and Patient Safety, Cleveland Clinic, Cleveland, Ohio, United States
,
Jacob Raciniewski
3   Department of Enterprise Analytics, Cleveland Clinic, Cleveland, Ohio, United States
,
James F. Simon
4   Medical Operations Department, Cleveland Clinic, Cleveland, Ohio, United States
,
Robert Wyllie
4   Medical Operations Department, Cleveland Clinic, Cleveland, Ohio, United States
,
Thomas G. Fraser
5   Department of Infectious Diseases, Cleveland Clinic, Cleveland, Ohio, United States
› Author Affiliations Funding None.
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Abstract

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

Objectives Our 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.

Methods Infection 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.

Results We 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.

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

Keywords

monitoring and surveillance - public health - patient records - influenza - human - automation

Protection of Human and Animal Subjects

Our health care system Institutional Review Board exempted this project from full review, considering it minimal risk research involving secondary data collected as part of normal health care operations.




Publication History

Received: 02 March 2020

Accepted: 14 July 2020

Article published online:
26 August 2020

© 2020. Thieme. All rights reserved.

Georg Thieme Verlag KG
Stuttgart · New York

 

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