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CC BY 4.0 · ACI open 2019; 03(02): e71-e77
DOI: 10.1055/s-0039-1693651
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Novel Visualization of Clostridium difficile Infections in Intensive Care Units

Sean C. Yu
1   Institute for Informatics, Washington University School of Medicine in St Louis, St Louis, Missouri, United States
2   Department of Biomedical Engineering, Washington University in St Louis, St Louis, Missouri, United States
,
Albert M. Lai
1   Institute for Informatics, Washington University School of Medicine in St Louis, St Louis, Missouri, United States
,
Justin Smyer
3   Department of Clinical Epidemiology, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States
,
Jennifer Flaherty
3   Department of Clinical Epidemiology, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States
,
Julie E. Mangino
4   Division of Infectious Diseases, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States
,
Ann Scheck McAlearney
5   Department of Family Medicine, College of Medicine, The Ohio State University, Columbus, Ohio, United States
6   CATALYST, Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University, Columbus, Ohio, United States
,
Po-Yin Yen
1   Institute for Informatics, Washington University School of Medicine in St Louis, St Louis, Missouri, United States
,
Susan Moffatt-Bruce
6   CATALYST, Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University, Columbus, Ohio, United States
7   Department of Surgery, College of Medicine, The Ohio State University, Columbus, Ohio, United States
,
Courtney L. Hebert
8   Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, Ohio, United States
› Author Affiliations Funding This project was supported by the Institute for the Design of Environments Aligned for Patient Safety (IDEA4PS) at The Ohio State University which is sponsored by the Agency for Healthcare Research & Quality (AHRQ) (P30HS024379). The opinions expressed in this document are those of the authors and do not reflect the official position of AHRQ.
Further Information

Publication History

19 October 2018

04 June 2019

Publication Date:
21 August 2019 (online)

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Abstract

Background Accurate and timely surveillance and diagnosis of health care facility onset Clostridium difficile infection (HO-CDI) is vital to controlling infections within the hospital, but there are limited tools to assist with timely outbreak investigations.

Objectives The objective of this study was to integrate spatiotemporal factors with HO-CDI cases and to develop a map-based dashboard to support infection preventionists (IPs) in performing surveillance and outbreak investigations for HO-CDI.

Methods Clinical laboratory results and Admit-Transfer-Discharge data for admitted patients over 2 years were extracted from the information warehouse of a large academic medical center (AMC) and processed according to the Center for Disease Control National Healthcare Safety Network definitions to classify CDI cases by onset date. Results were validated against the internal infection surveillance database maintained by IPs in Clinical Epidemiology of this AMC. Hospital floor plans were combined with HO-CDI case data, to create a dashboard of intensive care units. Usability testing was performed with a think-aloud session and a survey.

Results The simple classification algorithm identified all 265 HO-CDI cases from January 1, 2015 to November 30, 2015 with a positive predictive value (PPV) of 96.3%. When applied to data from 2014, the PPV was 94.6%. All users “strongly agreed” that the dashboard would be a positive addition to Clinical Epidemiology and would enable them to present hospital-acquired infection information to others more efficiently.

Conclusion The CDI dashboard demonstrates the feasibility of mapping clinical data to hospital patient care units for more efficient surveillance and potential outbreak investigations.

Keywords

electronic health records and systems - biosurveillance and case reporting - data visualization

Protection of Human and Animal Subjects

All research activities reported in this publication were reviewed and approved by the AMC's institutional review board.


 

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