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Deriving Measures of Intensive Care Unit Antimicrobial Use from Computerized Pharmacy Data: Methods, Validation, and Overcoming Barriers

Published online by Cambridge University Press:  02 January 2015

David N. Schwartz ,
R. Scott Evans ,
Bernard C. Camins ,
Yosef M. Khan ,
James F. Lloyd ,
Nadine Shehab ,
Kurt Stevenson  and
Centers for Disease Control and Prevention Epicenter Program
David N. Schwartz*
Affiliation:
John H. Stroger, Jr., Hospital of Cook County and Rush Medical College, Chicago, Illinois
R. Scott Evans
Affiliation:
LDS Hospital/Intermountain Healthcare, Salt Lake City, Utah Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah
Bernard C. Camins
Affiliation:
Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri
Yosef M. Khan
Affiliation:
Ohio State University Medical Center and College of Medicine, Ohio State University, Columbus, Ohio
James F. Lloyd
Affiliation:
LDS Hospital/Intermountain Healthcare, Salt Lake City, Utah
Nadine Shehab
Affiliation:
Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
Kurt Stevenson
Affiliation:
Ohio State University Medical Center and College of Medicine, Ohio State University, Columbus, Ohio
*
Division of Infectious Diseases, John H. Stroger, Jr., Hospital of Cook County, 1901 West Harrison Street, Chicago, IL 60612 ([email protected])
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Abstract

Objective.

To outline methods for deriving and validating intensive care unit (ICU) antimicrobial utilization (AU) measures from computerized data and to describe programming problems that emerged.

Design.

Retrospective evaluation of computerized pharmacy and administrative data.

Setting.

ICUs from 4 academic medical centers over 36 months.

Interventions.

Investigators separately developed and validated programming code to report AU measures in selected ICUs. Use of antibacterial and antifungal drugs for systemic administration was categorized and expressed as antimicrobial-days (each day that each antimicrobial drug was given to each patient) and patient-days receiving antimicrobials (each day that any antimicrobial drug was given to each patient). Monthly rates were compiled and analyzed centrally, with ICU patient-days as the denominator. Results were validated against data collected from manual review of medical records. Frequent discussion among investigators aided identification and correction of programming problems.

Results.

AU data were successfully programmed though a reiterative process of computer code revision. After identifying and resolving major programming errors, comparison of computerized patient-level data with data collected by manual review of medical records revealed discrepancies in antimicrobial-days and patient-days receiving antimicrobials that ranged from less than 1% to 17.7%. The hospital from which numerator data were derived from electronic records of medication administration had the least discrepant results.

Conclusions.

Computerized AU measures can be derived feasibly, but threats to validity must be sought out and corrected. The magnitude of discrepancies between computerized AU data and a gold standard based on manual review of medical records varies, with electronic records of medication administration providing maximal accuracy.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 32 , Issue 5 , May 2011 , pp. 472 - 480
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2011

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