Elevating Sterile Processing Businesses Through Tailored Consulting Excellence
nature of the HAI problem .”
The 2008 systematic review found substantial but inconsistent evidence that better nurse staffing is related to lower rates of HAIs . Among the reasons for the inconsistent findings were reliance on highly aggregated measures of staffing ; inability to determine the staffing level individual patients experienced ; lack of adequate controls for patient-level risk ; use of International Classification Diagnosis ( ICD ) codes in discharge data that may be incomplete and do not match the CDC ’ s National Healthcare Safety Network ( NHSN ) definition for HAIs ; and failure to consider the incubation period for the development of an HAI .
Since that 2008 review , as we will see , there have been numerous studies – including a review by Knighton , et al . ( 2024 ) that will be explored in this article – to gain a better understanding of IP & C-related staffing needs . Much more recently , a new study found that nearly 4 in 5 hospitals are not staffed at sufficient level to keep patients safe from preventable infections . This study by Bartles , et al . ( 2024 ), conducted by
the APIC Center for Research , Practice & Innovation , summarizes a pilot project to evaluate a new online calculator aimed at providing facility-specific recommendations for infection prevention staffing .
Infection prevention and control ( IP & C ) programs with staffing levels that are below expected levels demonstrated significantly higher rates of central line-associated bloodstream infections ( CLABSI ), catheterassociated urinary tract infections ( CAUTI ), Clostridioides difficile infections , and colon surgical site infections . For example , 25 percent of facilities staffed at lower-thanexpected staffing levels had elevated CAUTI rates , as compared to only 7 percent of hospitals with expected or above-expected staffing levels .
A total of 390 acute-care hospitals participated in the first version of the staffing calculator , which identified that 79.2 percent had insufficient staffing levels in this area . Most of the hospitals had an intensive care unit ( ICU ) ( n = 355 , 91 percent ), emergency department ( ED ) ( n = 386 , 99 percent ), perform surgery ( n = 385 , 98.7 percent ), and were a part of a system ( n = 329 , 84.4 percent ). The case mix index ( CMI ) increased by hospital size with hospitals with 101 to 200 beds lower than hospitals with more than 750 beds . For the hospitals that submitted data to the staffing calculator , the median IP-FTE to bed ratio was 1 IP-FTE to 121.0 beds . The IP-FTE-to-bed ratio decreased as the hospital bed size increased , with larger programs having less IP-FTE per bed . The ratio of IP-FTE to beds in hospitals of fewer than 25 beds and 25 to 50 beds was greater ( more IP FTE per bed ) than the ratio in the larger hospitals which ranged from 1 IP-FTE to 99 beds ( 51-100 beds ) to 1 IP-FTE to 161 beds ( 301-400 beds ). For hospitals that submitted data to the staffing calculator , the median facility-specific recommended IP-FTE to bed ratio of 65.0 was significantly higher than the current IP-FTE to bed ratio of 1 to 121.0 .
According to Bartles , et al . ( 2024 ), the staffing calculator deemed 79.2 percent ( n = 309 ) of the respondent hospital staffing as below expected staffing , 3.3 percent as expected ( n = 13 ) and 17.4 percent ( n = 68 ) above expected . Almost 90 percent
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Elevating Sterile Processing Businesses Through Tailored Consulting Excellence
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