This facilityspecific calculator for infection prevention and control staffing levels is a completely novel tool for our community and one that is clearly needed to help hospitals advocate for adequate resources to keep patients and healthcare workers safe .”
— Rebecca Crapanzano-
Sigafoos
( n = 277 ) of the hospitals with more than 100 beds were considered to have below expected staffing while 54.8 percent ( n = 51 ) of hospitals with fewer than 100 beds were considered to have above expected staffing .
Historically , hospitals utilized benchmarks that relied on a ratio of infection preventionists ( IPs ) per inpatient bed ( ranging from 69 to 100 beds in the last decade ). This traditional “ one size fits all ” method for calculating appropriate IP staffing levels is inadequate and outdated in the modern complex healthcare environment , experts say .
Recognizing the need for a more customized recommendation for IP staffing , APIC developed an online staffing calculator with a predictive algorithm that allows users to enter information about their hospital facility and receive an assessment of staffing needs specific to them . The calculator adjusts optimal staffing ratios based on factors such as the complexity of services provided or the presence of an emergency department , burn unit , stem cell transplant unit , or inpatient rehabilitation unit .
“ This facility-specific calculator for infection prevention and control staffing levels is a completely novel tool for our community and one that is clearly needed to help hospitals advocate for adequate resources to keep patients and healthcare workers safe ,” says Rebecca Crapanzano-Sigafoos ( formerly Bartles ), executive director of the Center for Research , Practice & Innovation at APIC and lead author of this study . “ We look forward to updating the calculator soon with new categories and more granularity based on our experience with the beta tool and to making it available to the IP & C and broader healthcare communities .” Additional details from the study include :
• Hospitals included ranged in size from eight beds to more than 2,000 beds
• Staffing levels ranged from 1 IP per 40 beds at small hospitals ( fewer than 25 beds ) to a peak of 1 IP per 161 beds in hospitals with 301 to 400 beds
• More than 85 percent of respondents who believed their staff levels were inadequate came from hospitals found to have lower than expected IP staffing .
As Bartles , et al . ( 2024 ) observe , “ The standard IP-FTE to bed ratio is inadequate for today ’ s complex healthcare environments , revealing that many facilities overlook these crucial characteristics . For example , as hospital sizes increased , so did the complexity of patients and services , emphasizing the need for a facility-specific risk and facility-specific IP-FTE-to-bed ratio . The relationship between hospital size and high-risk patient population indicates an increased need for infection prevention time and attention . Although CMI also increased as bed size increased , this measure is one of the few hospital-specific indices of complexity , which supports its continued use as an adjustment factor in future iterations of the staffing calculator .”
Crapanzano-Sigafoos acknowledges the previous staffing studies in the medical literature but points to the uniqueness of the pilot study that beta-tested the staffing calculator .
“ Although there is a significant body of literature that describes staffing and infection prevention using survey data and how we ’ re staffing , there has not been anything that demonstrates at scale a statistically
significant relationship between infection prevention staffing and a decrease or increase in the rate of infection ,” Crapanzano-Sigafoos says . “ So that ’ s new and novel , and without having that direct connection , infection preventionists and IP & C program leaders have struggled to be able to make the case over time for ensuring that their departments are adequately staffed . So , this is a significant step toward proving the value of IP & C and hopefully encouraging those who are in the C-suite to continue to support and grow IP & C programs .”
Crapanzano-Sigafoos says she was pleased by how well the perception of the person entering the information about their facility aligned with what the staffing calculator actually calculated . “ Infection preventionists know when their program is understaffed , and they know when their program is adequately staffed , so this finding supports the knowledge and expertise of the people doing this work .”
She also acknowledges that the calculator has been a long time in coming since the SENIC study .
“ It ’ s taken not only those 40 years , but a global pandemic for us to get to the place where we are finally able to prioritize the work of IPs ,” Crapanzano-Sigafoos says . “ I think the reason for that is a lot of what we do is invisible , in that it ’ s risk mitigation and avoidance of infection , and that ’ s a lot harder to see or to display to others . We ’ re not revenue-generating departments , we do have downstream cost avoidance , but even that in itself can be difficult to calculate because of the complexity of costs associated with the patient stay in the healthcare facility .”
There seem to be so many inherent variables or challenges related to trying to aggregate measures of staffing . For example , Weinstein , et al . ( 2008 ) criticized the methodological flaws they identified in the body of research they studied . In many of the larger multi-site studies , the research teams used administrative data sets to examine both staffing and infection variables . They detected limitations in many of the studies , including that in large data sets , the staffing measures are often determined using the hospitals ’ reported full-time equivalents . This method is not precise , is not unit specific , and is likely to introduce measurement error , researchers say . Another limitation in many of these studies is the identification of infections with use of diagnoses and procedure codes recorded in administrative data . They explain that previous studies found that ICD codes and other hospital administrative databases did not accurately identify patients who had a central line – associated BSI , as defined by the CDC . They also identified poor sensitivity and low positive predictive value or administrative data as a means of identifying HAIs has been reported .
As Weinstein , et al . ( 2008 ) observe , “ When these issues are considered , it is likely that variability in the measurement of both staffing and HAI each contribute to the mixed results found in these studies . Indeed , because of the limitations of ICD codes and the identification of HAI , others may not have considered these studies for review . However , we chose to include this body of evidence , because these studies are often cited and often impact policy , such as the legislated minimum nurse-to-patient ratios in California hospitals . More-precise measurements of staffing and HAI may
14 • www . healthcarehygienemagazine . com • november 2024