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Sundermann AJ, Miller JK, Marsh JW, et al. Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks. Infect Control Hosp Epidemiol. 2019;40(3):314-319doi: 10.1017/ice.2018.343.
Sundermann, A. J., Miller, J. K., Marsh, J. W., Saul, M. I., Shutt, K. A., Pacey, M., Mustapha, M. M., Ayres, A., Pasculle, A. W., Chen, J., Snyder, G. M., Dubrawski, A. W., & Harrison, L. H. (2019). Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks. Infection control and hospital epidemiology, 40(3), 314-319. https://doi.org/10.1017/ice.2018.343
Sundermann, Alexander J, et al. "Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks." Infection control and hospital epidemiology vol. 40,3 (2019): 314-319. doi: https://doi.org/10.1017/ice.2018.343
Sundermann AJ, Miller JK, Marsh JW, Saul MI, Shutt KA, Pacey M, Mustapha MM, Ayres A, Pasculle AW, Chen J, Snyder GM, Dubrawski AW, Harrison LH. Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks. Infect Control Hosp Epidemiol. 2019 Mar;40(3):314-319. doi: 10.1017/ice.2018.343. Epub 2019 Feb 18. PMID: 30773168; PMCID: PMC8189294.
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Infect Control Hosp Epidemiol. 2019 Mar;40(3):314-319. doi: 10.1017/ice.2018.343. Epub 2019 Feb 18.

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

Infection control and hospital epidemiology

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, Jieshi Chen, Graham M Snyder, Artur W Dubrawski, Lee H Harrison

Affiliations

  1. The Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit,University of Pittsburgh School of Medicine and Graduate School of Public Health,Pittsburgh, Pennsylvania.
  2. Anton Laboratory,Carnegie Mellon University,Pittsburgh, Pennsylvania.
  3. Department of Medicine,University of Pittsburgh School of Medicine,Pittsburgh, Pennsylvania.
  4. Department of Infection Prevention and Control,University of Pittsburgh Medical Center,Pittsburgh, Pennsylvania.
  5. Department of Pathology,University of Pittsburgh,Pittsburgh, Pennsylvania.

PMID: 30773168 PMCID: PMC8189294 DOI: 10.1017/ice.2018.343

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.

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