Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates
1 Channing Laboratory, Department of Medicine Brigham and Women's Hospital Boston, MA, USA
2 Department of Ambulatory Care and Prevention Harvard Medical School and Harvard Pilgrim Healthcare Boston, MA, USA
3 Center for Health Care Policy and Evaluation Eden Prairie, MN, USA
4 GlaxoSmithKline Mississauga, ON, Canada
5 Clinical Innovations Center Humana, Incorporated Louisville, KY, USA
6 Division of Research Joint Commission on Accreditation of Healthcare Organizations Oakbrook Terrace, Illinois, USA
BMC Medical Research Methodology 2007, 7:20 doi:10.1186/1471-2288-7-20Published: 6 June 2007
Claims data provide rapid indicators of SSIs for coronary artery bypass surgery and have been shown to successfully rank hospitals by SSI rates. We now operationalize this method for use by payers without transfer of protected health information, or any insurer data, to external analytic centers.
We performed a descriptive study testing the operationalization of software for payers to routinely assess surgical infection rates among hospitals where enrollees receive cardiac procedures. We developed five SAS programs and a user manual for direct use by health plans and payers. The manual and programs were refined following provision to two national insurers who applied the programs to claims databases, following instructions on data preparation, data validation, analysis, and verification and interpretation of program output.
A final set of programs and user manual successfully guided health plan programmer analysts to apply SSI algorithms to claims databases. Validation steps identified common problems such as incomplete preparation of data, missing data, insufficient sample size, and other issues that might result in program failure. Several user prompts enabled health plans to select time windows, strata such as insurance type, and the threshold number of procedures performed by a hospital before inclusion in regression models assessing relative SSI rates among hospitals. No health plan data was transferred to outside entities.
Programs, on default settings, provided descriptive tables of SSI indicators stratified by hospital, insurer type, SSI indicator (inpatient, outpatient, antibiotic), and six-month period. Regression models provided rankings of hospital SSI indicator rates by quartiles, adjusted for comorbidities. Programs are publicly available without charge.
We describe a free, user-friendly software package that enables payers to routinely assess and identify hospitals with potentially high SSI rates complicating cardiac procedures.