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Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks

Published online by Cambridge University Press:  18 February 2019

Alexander J. Sundermann ,
James K. Miller ,
Jane W. Marsh ,
Melissa I. Saul ,
Kathleen A. Shutt ,
Marissa Pacey ,
Mustapha M. Mustapha ,
Ashley Ayres ,
A. William Pasculle  and
Jieshi Chen
...Show all authors
Alexander J. Sundermann
Affiliation:
The Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania Department of Infection Prevention and Control, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
James K. Miller
Affiliation:
Anton Laboratory, Carnegie Mellon University, Pittsburgh, Pennsylvania
Jane W. Marsh
Affiliation:
The Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania
Melissa I. Saul
Affiliation:
Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
Kathleen A. Shutt
Affiliation:
The Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania
Marissa Pacey
Affiliation:
The Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania
Mustapha M. Mustapha
Affiliation:
The Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania
Ashley Ayres
Affiliation:
Department of Infection Prevention and Control, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
A. William Pasculle
Affiliation:
Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania
Jieshi Chen
Affiliation:
Anton Laboratory, Carnegie Mellon University, Pittsburgh, Pennsylvania
Graham M. Snyder
Affiliation:
Department of Infection Prevention and Control, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
Artur W. Dubrawski
Affiliation:
Anton Laboratory, Carnegie Mellon University, Pittsburgh, Pennsylvania
Lee H. Harrison*
Affiliation:
The Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania
*
Author for correspondence: Lee H. Harrison, Email: [email protected]
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Abstract

Background:

Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak.

Methods:

We retrospectively analyzed 9 hospital outbreaks that occurred during 2011–2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isolates. We determined (1) the ability of data mining of the EHR to identify the correct route of transmission, (2) how early the correct route was identified during the timeline of the outbreak, and (3) how many cases in the outbreaks could have been prevented had the system been running in real time.

Results:

Correct routes were identified for all outbreaks at the second patient, except for one outbreak involving >1 transmission route that was detected at the eighth patient. Up to 40 or 34 infections (78% or 66% of possible preventable infections, respectively) could have been prevented if data mining had been implemented in real time, assuming the initiation of an effective intervention within 7 or 14 days of identification of the transmission route, respectively.

Conclusions:

Data mining of the EHR was accurate for identifying routes of transmission among patients who were part of the outbreak. Prospective validation of this approach using routine whole-genome sequencing and data mining of the EHR for both outbreak detection and route attribution is ongoing.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 40 , Issue 3 , March 2019 , pp. 314 - 319
Copyright
© 2019 by The Society for Healthcare Epidemiology of America. All rights reserved. 

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References

Magill, SS, Edwards, JR, Bamberg, W, et al. Multistate point-prevalence survey of health care–associated infections. N Engl J Med 2014;370:1198–1208.CrossRefGoogle ScholarPubMed
Scott, RD. The direct medical costs of healthcare-associated infections in US hospitals and the benefits of prevention, 2009. Centers for Disease Control and Prevention website. http://www.cdc.gov/HAI/pdfs/hai/Scott_CostPaper.pdf. Published 2009. Accessed August 13, 2018.Google Scholar
Marsh, JW, Krauland, MG, Nelson, JS, et al. Genomic epidemiology of an endoscope-associated outbreak of Klebsiella pneumoniae carbapenemase (KPC)–producing K. pneumoniae. PLoS One 2015;10:e0144310. doi: 10.1371/journal.pone.0144310.CrossRefGoogle ScholarPubMed
Sood, G, Perl, TM. Outbreaks in health care settings. Infect Dis Clin N Am 2016;30:661–687.CrossRefGoogle ScholarPubMed
Vonberg, RP, Weitzel-Kage, D, Behnke, M, et al. Worldwide outbreak database: the largest collection of nosocomial outbreaks. Infection 2011;39:29–34.CrossRefGoogle ScholarPubMed
Peacock, SJ, Parkhill, J, Brown, NM. Changing the paradigm for hospital outbreak detection by leading with genomic surveillance of nosocomial pathogens. Microbiology 2018;164:1213–1219. doi: 10.1099/mic.0.000700.CrossRefGoogle ScholarPubMed
Heinrichs, A, Argudin, MA, De Mendonca, R, et al. An outpatient clinic as a potential site of transmission for an outbreak of NDM-producing Klebsiella pneumonia ST716: a study using whole-genome sequencing. Clin Infect Dis 2018. doi: 10.1093/cid/ciy581.Google Scholar
Domman, D, Chowdhury, F, Khan, Al, et al. Defining endemic cholera at three levels of spatiotemporal resolution within Bangladesh. Nat Genet 2018;50:951–955.CrossRefGoogle ScholarPubMed
Pak, TR, Kasarskis, A. How next-generation sequencing and multiscale data analysis will transform infectious disease management. Clin Infect Dis 2015;61:1695–1702.Google ScholarPubMed
Yount, RJ, Vries, JK, Councill, CD. The medical archival retrieval system: an information retrieval system based on distributed parallel processing. Inform Process Manag 1991;27:1–11.CrossRefGoogle Scholar
Miller, JK, Chen, C, Sundermann, AJ, Marsh, JW, Saul, MI, Shutt, KA, Pacey, M, Mustapha, MM, Harrison, LH, Dubrawski, A. Statistical outbreak detection by joining medical records and pathogen similarity. Accepted manuscript for J Biomed Inform.Google Scholar
Parr, A, Querry, A, Pasculle, A, Morgan, D, Muto, C. Carbapenem-resistant Klebsiella pneumoniae cluster associated with gastroscope exposure among surgical intensive care unit patients at University of Pittsburgh Medical Center. Open Forum Infect Dis 2016:3:248.CrossRefGoogle Scholar