Prediction of recurrent Clostridium difficile infection using comprehensive electronic medical records in an integrated healthcare delivery system

Authors

Gabriel J. Escobar, Kaiser Permanente Division of Research
Jennifer M. Baker, Contra Costa Public Health Clinic Services
Patricia Kipnis, Kaiser Permanente Northern California
John D. Greene, Kaiser Permanente Division of Research
T. Christopher Mast, Merck Research Laboratories
Swati B. Gupta, Merck Vaccines
Nicole Cossrow, Merck Research Laboratories
Vinay Mehta, Merck Research Laboratories
Vincent Liu, Kaiser Permanente Northern California
Erik R. Dubberke, Washington University School of Medicine in St. Louis

Journal

Infection Control & Hospital Epidemiology

Publication Date

2017

Volume

38

Issue

10

Inclusive Pages

1196-1203

Document Type

Open Access Publication

DOI

10.1017/ice.2017.176

Rights and Permissions

Escobar, G., Baker, J., Kipnis, P., Greene, J., Mast, T., Gupta, S., . . . Dubberke, E. (2017). Prediction of Recurrent Clostridium Difficile Infection Using Comprehensive Electronic Medical Records in an Integrated Healthcare Delivery System. Infection Control & Hospital Epidemiology, 38(10), 1196-1203. doi:10.1017/ice.2017.176. © 2017 by The Society for Healthcare Epidemiology of America. All rights reserved. http://journals.cambridge.org

Recommended Citation

Escobar, Gabriel J.; Baker, Jennifer M.; Kipnis, Patricia; Greene, John D.; Mast, T. Christopher; Gupta, Swati B.; Cossrow, Nicole; Mehta, Vinay; Liu, Vincent; and Dubberke, Erik R., "Prediction of recurrent Clostridium difficile infection using comprehensive electronic medical records in an integrated healthcare delivery system." Infection Control & Hospital Epidemiology. 38, 10. 1196-1203. (2017).
https://digitalcommons.wustl.edu/open_access_pubs/6222