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Administrative Data Fail to Accurately Identify Cases of Healthcare-Associated Infection

Published online by Cambridge University Press:  21 June 2016

Eileen R. Sherman ,
Kateri H. Heydon ,
Keith H. St. John ,
Eva Teszner ,
Susan L. Rettig ,
Sharon K. Alexander ,
Theoklis Z. Zaoutis  and
Susan E. Coffin
Eileen R. Sherman
Affiliation:
Department of Infection Prevention and Control, Children's Hospital of Philadelphia, Philadelphia
Kateri H. Heydon
Affiliation:
Division of Infectious Diseases, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia
Keith H. St. John
Affiliation:
Department of Infection Prevention and Control, Children's Hospital of Philadelphia, Philadelphia
Eva Teszner
Affiliation:
Department of Infection Prevention and Control, Children's Hospital of Philadelphia, Philadelphia
Susan L. Rettig
Affiliation:
Department of Infection Prevention and Control, Children's Hospital of Philadelphia, Philadelphia
Sharon K. Alexander
Affiliation:
Department of Infection Prevention and Control, Children's Hospital of Philadelphia, Philadelphia
Theoklis Z. Zaoutis
Affiliation:
Division of Infectious Diseases, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia
Susan E. Coffin*
Affiliation:
Department of Infection Prevention and Control, Children's Hospital of Philadelphia, Philadelphia Division of Infectious Diseases, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia
*
Department of Infection Prevention and Control, Children's Hospital of Philadelphia, Philadelphia, PA 19104 (coffin@email.chop.edu)
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Abstract

Objective.

Some policy makers have embraced public reporting of healthcare-associated infections (HAIs) as a strategy for improving patient safety and reducing healthcare costs. We compared the accuracy of 2 methods of identifying cases of HAI: review of administrative data and targeted active surveillance.

Design, Setting, and Participants.

A cross-sectional prospective study was performed during a 9-month period in 2004 at the Children's Hospital of Philadelphia, a 418-bed academic pediatric hospital. “True HAI” cases were defined as those that met the definitions of the National Nosocomial Infections Surveillance System and that were detected by a trained infection control professional on review of the medical record. We examined the sensitivity and the positive and negative predictive values of identifying HAI cases by review of administrative data and by targeted active surveillance.

Results.

We found similar sensitivities for identification of HAI cases by review of administrative data (61%) and by targeted active surveillance (76%). However, the positive predictive value of identifying HAI cases by review of administrative data was poor (20%), whereas that of targeted active surveillance was 100%.

Conclusions.

The positive predictive value of identifying HAI cases by targeted active surveillance is very high. Additional investigation is needed to define the optimal detection method for institutions that provide HAI data for comparative analysis.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 27 , Issue 4 , April 2006 , pp. 332 - 337
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2006

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