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Problematic Risk Adjustment in National Healthcare Safety Network Measures.

ABSTRACT: This article reviews the risk-adjustment models underpinning the National Healthcare Safety Network (NHSN) standardized infection ratios. After first describing the models, the authors focus on hospital intensive care unit (ICU) designation as a variable employed across the various risk models. The risk-adjusted frequency with which ICU services are reported in Medicare fee-for-service claims data was compared as a proxy for determining whether reporting of ICU days is similar across hospitals. Extreme variation was found in the reporting of ICU utilization among admissions for congestive heart failure, ranging from 25% in the lowest admission hospital quartile to 95% in the highest. The across-hospital variation in reported ICU utilization was found to be unrelated to patient severity. Given that such extreme variation appears in a designation of ICU versus non-ICU utilization, the NHSN risk-adjustment models' dependence on nursing unit designation should be a cause for concern.

SUBMITTER: Fuller RL

PROVIDER: S-EPMC7281913 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models. METHODS: A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented. RESULTS: Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status. CONCLUSIONS: Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.

Project description:OBJECTIVE To describe pathogen distribution and antimicrobial resistance patterns for healthcare-associated infections (HAIs) reported to the National Healthcare Safety Network (NHSN) from pediatric locations during 2011-2014. METHODS Device-associated infection data were analyzed for central line-associated bloodstream infection (CLABSI), catheter-associated urinary tract infections (CAUTI), ventilator-associated pneumonia (VAP), and surgical site infection (SSI). Pooled mean percentage resistance was calculated for a variety of pathogen-antimicrobial resistance pattern combinations and was stratified by location for device-associated infections (neonatal intensive care units [NICUs], pediatric intensive care units [PICUs], pediatric oncology and pediatric wards) and by surgery type for SSIs. RESULTS From 2011 to 2014, 1,003 hospitals reported 20,390 pediatric HAIs and 22,323 associated pathogens to the NHSN. Among all HAIs, the following pathogens accounted for more than 60% of those reported: Staphylococcus aureus (17%), coagulase-negative staphylococci (17%), Escherichia coli (11%), Klebsiella pneumoniae and/or oxytoca (9%), and Enterococcus faecalis (8%). Among device-associated infections, resistance was generally lower in NICUs than in other locations. For several pathogens, resistance was greater in pediatric wards than in PICUs. The proportion of organisms resistant to carbapenems was low overall but reached approximately 20% for Pseudomonas aeruginosa from CLABSIs and CAUTIs in some locations. Among SSIs, antimicrobial resistance patterns were similar across surgical procedure types for most pathogens. CONCLUSION This report is the first pediatric-specific description of antimicrobial resistance data reported to the NHSN. Reporting of pediatric-specific HAIs and antimicrobial resistance data will help identify priority targets for infection control and antimicrobial stewardship activities in facilities that provide care for children. Infect Control Hosp Epidemiol 2018;39:1-11.

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Problematic Risk Adjustment in National Healthcare Safety Network Measures.

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