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Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates

BACKGROUND: 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...

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Detalles Bibliográficos
Autores principales: Huang, Susan S, Livingston, James M, Rawson, Nigel SB, Schmaltz, Steven, Platt, Richard
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1896175/
https://www.ncbi.nlm.nih.gov/pubmed/17553168
http://dx.doi.org/10.1186/1471-2288-7-20
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author Huang, Susan S
Livingston, James M
Rawson, Nigel SB
Schmaltz, Steven
Platt, Richard
author_facet Huang, Susan S
Livingston, James M
Rawson, Nigel SB
Schmaltz, Steven
Platt, Richard
author_sort Huang, Susan S
collection PubMed
description BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.
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spelling pubmed-18961752007-06-23 Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates Huang, Susan S Livingston, James M Rawson, Nigel SB Schmaltz, Steven Platt, Richard BMC Med Res Methodol Software BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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. BioMed Central 2007-06-06 /pmc/articles/PMC1896175/ /pubmed/17553168 http://dx.doi.org/10.1186/1471-2288-7-20 Text en Copyright © 2007 Huang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
Huang, Susan S
Livingston, James M
Rawson, Nigel SB
Schmaltz, Steven
Platt, Richard
Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates
title Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates
title_full Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates
title_fullStr Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates
title_full_unstemmed Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates
title_short Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates
title_sort developing algorithms for healthcare insurers to systematically monitor surgical site infection rates
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1896175/
https://www.ncbi.nlm.nih.gov/pubmed/17553168
http://dx.doi.org/10.1186/1471-2288-7-20
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