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.
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.
Authors: B M Zagorski; W E Trick; D N Schwartz; M F Wisniewski; R C Hershow; S K Fridkin; R A Weinstein Journal: Clin Infect Dis Date: 2002-12-02 Impact factor: 9.079
Authors: Mary F Wisniewski; Piotr Kieszkowski; Brandon M Zagorski; William E Trick; Michael Sommers; Robert A Weinstein Journal: J Am Med Inform Assoc Date: 2003-06-04 Impact factor: 4.497
Authors: Timothy H Dellit; Robert C Owens; John E McGowan; Dale N Gerding; Robert A Weinstein; John P Burke; W Charles Huskins; David L Paterson; Neil O Fishman; Christopher F Carpenter; P J Brennan; Marianne Billeter; Thomas M Hooton Journal: Clin Infect Dis Date: 2006-12-13 Impact factor: 9.079
Authors: D M Shlaes; D N Gerding; J F John; W A Craig; D L Bornstein; R A Duncan; M R Eckman; W E Farrer; W H Greene; V Lorian; S Levy; J E McGowan; S M Paul; J Ruskin; F C Tenover; C Watanakunakorn Journal: Clin Infect Dis Date: 1997-09 Impact factor: 9.079
Authors: R S Evans; R A Larsen; J P Burke; R M Gardner; F A Meier; J A Jacobson; M T Conti; J T Jacobson; R K Hulse Journal: JAMA Date: 1986 Aug 22-29 Impact factor: 56.272
Authors: Gail S Itokazu; Robert C Glowacki; David N Schwartz; Mary F Wisniewski; Robert J Rydman; Robert A Weinstein Journal: Infect Control Hosp Epidemiol Date: 2005-04 Impact factor: 3.254
Authors: R S Evans; J A Olson; E Stenehjem; W R Buckel; E A Thorell; S Howe; X Wu; P S Jones; J F Lloyd Journal: Appl Clin Inform Date: 2015-03-03 Impact factor: 2.342