Surgeons’ perspectives on surgical wound infection rate data in Queensland, Australia.

Abstract

Background The results of the Study on the Efficacy of Nosocomial Infection Control (SENIC) project demonstrated that hospitals with active infection control programs had lower rates of nosocomial infection than those without such programs. A key component of these programs was the inclusion of a systematic method for monitoring nosocomial infection and reporting these infections to clinicians. Objectives To identify the perspectives of surgeons in Queensland, Australia, regarding infection rate data in terms of its accuracy and usefulness as well as their perceptions regarding acceptable infection rates for surgical procedures classified as "clean" or "contaminated." Methods A postal survey was conducted, with a convenience sample of 510 surgeons. Results More than 40% (n = 88) of respondents believed that the acceptable infection rate associated with clean surgical procedures should be less than 1%, a rate much lower than the threshold of 1.4% to 4.1% set by the Australian Council on Healthcare Standards (ACHS). Almost 30% (n = 55) of respondents reported that they would accept infection rates of 10% or higher for contaminated surgical procedures, which is higher than the ACHS threshold of 1.4% to 7.9%. Respondents identified failure to include postdischarge infections in the data and difficulties standardizing criteria for diagnosis of infection as the major impediments to the accuracy and usefulness of data provided. Conclusion The results of this study have significant implications in relation to the preparation of surgical site infection reports, especially in relation to the inclusion of postdischarge surveillance data and information regarding pathogens, antibiotic sensitivities, and comorbidities of patients developing surgical site infection. Surgeons also identified the need to include information regarding the use of standardized definitions in the diagnosis of wound infection and parameters that allow comparison of infection rates to improve their perceptions regarding data accuracy and usefulness.