The development of a customized staffing calculator for infection prevention programs is a novel approach not previously attempted . This facilityspecific approach allows each facility to tailor staffing based on its size , patient population , and infection risk ." care provided — they ’ re all risk factors and what the calculator helped us do in our beta version was to better understand the role of each . We asked a lot of questions and we were able to really understand what are the risk factors that are most closely correlated with staffing a program effectively and it ’ s allowing us then for version two of the calculator , which will be released in early 2025 to find the sweet spots . One of the things I was surprised by is the size of beds in the hospital matters in that hospitals that have 50 beds or less needed a completely different staffing ratio than hospitals that are larger . When we talk to folks about it , we learn there ’ s a lot of ‘ keeping-the-lights-on ’ work that happens in a facility and there ’ s a minimum , a baseline number of hours that are required to do those , regardless of how many beds . So , our new updated calculator will take that into consideration as well .”
The knowledge established by the beta calculator is paving the way for evolving work on the next iteration of the calculator going forward .
As Bartles , et al . ( 2024 ) note , “ Following the calculator ’ s launch , end users suggested optimizations for the second version . New metrics included select community disease incidence rates , state-level staffing requirements if available , IP oversight for employee health , antimicrobial stewardship programs , and regulatory responsibilities and outcomes from surveys . Structural improvements will break down FTE recommendations by roles ( such as IP , medical director ) and allow the grouping of multiple facilities for staffing calculations .”
“ We received a lot of feedback before we released the beta version ,” Crapanzano-Sigafoos confirms . “ We tested it on a pretty large group of IPs and they ‘ broke ’ it many times . For example , we asked for standardized infection ratios , but critical-access hospitals often don ’ t have those because their denominators are too small . So , then we had to pivot to be able to account for that and to measure something different , and in those care settings to try to understand that relationship between staffing and outcomes . The new version not only takes that into consideration , but we ’ re expanding the scope of the recommendations themselves . We ’ ve been partnering with the Society for Healthcare Epidemiologists of America ( SHEA ) to develop recommendations for medical directors of infection prevention . We ’ ll be including that in the next version of the calculator as well . There ’ s almost nothing in the literature on that topic , so I think that ’ s going to be really valuable .”
Because the staffing calculator is only accessible to APIC members , there is potential for the data to reflect this demographic only , even though the algorithm has been released in the open-access paper that was published earlier this month .
“ The data that we used for our analysis is based on facilities that have infection preventionists who are APIC members ,” Crapanzano-Sigafoos confirms . “ Certainly , the likelihood is that the data has some level of bias in that we received more data from facilities that are better staffed than facilities that are understaffed because typically those are the same facilities that support APIC membership for their IPs , and it ’ s the same facilities whose IPs and IP & C leaders have enough time to utilize the calculator . So , there ’ s
possibly some sample bias there , which I am unsure how we would eliminate over time , but this doesn ’ t negate the data or the results . We did release in this open-access paper the algorithm for the calculator and so it allows anyone to utilize the calculations to determine their staffing ratios without the use of the calculator . So , the information is out there .”
Crapanzano-Sigafoos says the data from the calculator will be leveraged by APIC but is also something that facilities can use to make the business case for improved staffing and resourcing .
“ In the long term , we ’ d love to see a scenario where this calculator become at least a strong recommendation for the minimum staffing of an infection prevention program in conjunction with a detailed facility-level needs assessment ,” she says . “ Until that time , it at least provides IPs with the information they need to be able to have a crucial conversation with members of their facility ’ s C-suite and to be able to say , ‘ Compared with everyone else in the country , here ’ s how our program is staffed and whether it might be staffed at a percentage lower than what is expected given the size and complexity of our organization .’ It doesn ’ t capture everything related to size and complexity , but it gives IPs what they need to have this important conversation . As we continue to grow the calculator and obtain more data , I think it will only strengthen the confidence that we have in the information that we ’ re disseminating . I do expect that at some point the calculator will become the standardized method for how we determine IP staffing . The other thing I should mention is the hospital calculator is part of a multiple-calculator series that includes ambulatory and long-term care . For those other two calculators , we ’ re in the process of analyzing the data sets ; they ’ re smaller data sets but they will also be updated based on our findings and user feedback . We will also be including the option for facilities to include their ambulatory clinic with their hospital , and then ultimately bringing all of that together into a system-level calculation that provides alternate scenarios on how to utilize staffing more efficiently in a system-wide model .”
As Bartles , et al . ( 2024 ) note , “ The development of a customized staffing calculator for infection prevention programs is a novel approach not previously attempted . This facility-specific approach allows each facility to tailor staffing based on its size , patient population , and infection risk . The roles and responsibilities of an IP have evolved tremendously over the last decade and recommended staffing levels are not aligned . Appropriate staffing levels would allow IPs to meet the requirements of the role and ultimately prevent infections rather than control them . Ideally , hospitals and health care systems would use this calculator to determine the number of IPs needed to align with their complexity and culture . Further research is needed to understand the magnitude of the impact of staffing on outcomes including healthcare-associated infections , regulatory implications , IP retention rates , and mental health .”
This most recent AJIC study contributes to what is becoming a more sizable body of evidence in the medical literature . Let ’ s explore some of the pertinent findings .
16 • www . healthcarehygienemagazine . com • november 2024