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Financial and Mortality Modeling as a Tool to Present Infection Prevention Data: What a SIR of 1.2 Means for the Hospital

Published online by Cambridge University Press:  02 November 2020

Vidya Mony
Affiliation:
Santa Clara Valley Medical Center
Kevin Hultquist
Affiliation:
Santa Clara Valley Medical Center
Supriya Narasimhan
Affiliation:
Santa Clara Valley Medical Center
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Abstract

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Background: Presenting to hospital leadership is an annual requirement of many infection prevention (IP) programs. Most presentations include current statistical data of hospital-acquired infections (HAIs) and whether the hospital has met its goals according to the National Healthcare Safety Network (NHSN) criteria. We presented HAI data in a novel way, with financial and mortality modeling, to show the impact of IP interventions to leadership not attuned to NHSN metrics. Method: We looked at 4 HAIs, their trends, and their effect on our hospital, Santa Clara Valley Medical Center (SCVMC). To estimate the impact of specific HAIs, we used 2 metrics derived from a meta-analysis by the US Department of Health and Human Services (HHS): excess mortality and excess cost. Excess mortality is defined as the difference between the underlying population mortality and the affected population mortality expressed as deaths per 1,000 population. Excess cost is defined as the additional cost introduced per patient with a specific HAI versus a similarly admitted patient without that HAI. HHS data were multiplied by the number of HAI events at SCVMC to generate estimates. Result: In our presentation, we elucidated a previously unseen cost savings and decreased mortality with 2 HAIs, central-line–associated blood stream infections (CLABSIs) and catheter associated urinary tract infections (CAUTIs), which were below NHSN targets due to IP-led interventions. We then showed 2 other HAIs, Clostridium difficile infection (CDI) and surgical site infections (SSIs), which did not meet our expected NHSN and institutional goals and were estimated to increase costs and potential mortalities in the upcoming year. We argued that proactive monies directed toward expanding our IP program and HAI mitigation efforts would cost a fraction of the impending healthcare expenditures as predicted by the model. Conclusion: By applying financial and mortality modeling, we helped our leadership perceive the concrete effect of IP-led interventions versus presenting abstract NHSN metrics. We also emphasized that without proactive leadership investment, we would continue to overspend healthcare dollars while not meeting our goals. This format of presentation gave us critical leverage to advocate for and successfully expand our IP department. Further SHEA-led cost-analysis modeling and education are needed to help IP departments promote their efforts in an effective manner.

Funding: None

Disclosures: None

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