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A Simple Microsoft Excel Method to Predict Antibiotic Outbreaks and Underutilization

Published online by Cambridge University Press:  03 May 2017

Cristina Miglis ,
Nathaniel J. Rhodes ,
Sean N. Avedissian ,
Teresa R. Zembower ,
Michael Postelnick ,
Richard G. Wunderink ,
Sarah H. Sutton  and
Marc H. Scheetz
Cristina Miglis
Affiliation:
Department of Pharmacy Practice, Midwestern University Chicago College of Pharmacy, Downers Grove, Illinois Department of Pharmacy, Northwestern Memorial Hospital, Chicago, Illinois
Nathaniel J. Rhodes
Affiliation:
Department of Pharmacy Practice, Midwestern University Chicago College of Pharmacy, Downers Grove, Illinois Department of Pharmacy, Northwestern Memorial Hospital, Chicago, Illinois
Sean N. Avedissian
Affiliation:
Department of Pharmacy Practice, Midwestern University Chicago College of Pharmacy, Downers Grove, Illinois Department of Pharmacy, Northwestern Memorial Hospital, Chicago, Illinois
Teresa R. Zembower
Affiliation:
Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, Illinois
Michael Postelnick
Affiliation:
Department of Pharmacy, Northwestern Memorial Hospital, Chicago, Illinois
Richard G. Wunderink
Affiliation:
Division of Pulmonary Critical Care, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Sarah H. Sutton
Affiliation:
Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, Illinois
Marc H. Scheetz*
Affiliation:
Department of Pharmacy Practice, Midwestern University Chicago College of Pharmacy, Downers Grove, Illinois Department of Pharmacy, Northwestern Memorial Hospital, Chicago, Illinois
*
Address correspondence to Marc H. Scheetz, PharmD, MSc, Associate Professor of Pharmacy Practice, Midwestern University Chicago College of Pharmacy; 555 31st St, Downers Grove, IL 60515 (mschee@midwestern.edu).
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Abstract

Benchmarking strategies are needed to promote the appropriate use of antibiotics. We have adapted a simple regressive method in Microsoft Excel that is easily implementable and creates predictive indices. This method trends consumption over time and can identify periods of over- and underuse at the hospital level.

Infect Control Hosp Epidemiol 2017;38:860–862

Type
Concise Communications
Information
Infection Control & Hospital Epidemiology , Volume 38 , Issue 7 , July 2017 , pp. 860 - 862
Copyright
© 2017 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

REFERENCES

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