By Kelly M. Pyrek
Long seen as a cost center by healthcare administrators, infection prevention programs have taken a beating when hospital budgets are cut. Infection preventionists are fighting back by making the business case for their programs to fend off additional cost-cutting as well as to demonstrate return on investment (ROI). While infection prevention has always been about patient and healthcare worker safety, it increasingly must show economic dividends -- it's a departure from the purely clinical path that infection preventionists have always taken, but essential for the future viability.
"Making the business case for infection prevention is a very important skill to master," says Patricia Stone, PhD, RN, FAAN, director of the Center for Health Policy as well as co-director of the PhD Program at Columbia University School of Nursing in New York. "I think it is a constant endeavor because things are always changing in the field and within healthcare institutions. It should also be part of the curriculum for the professional development of new infection prevention personnel, especially since people come and go in the infection control department. Even for the individuals who already understand the concepts, doing it once is not enough, as making the business case is an ongoing process -- you are always going to have to make the case for new programs or new interventions or additional personnel to your institution's leadership. The good news is that infection preventionists and their programs have more visibility than ever before because of mandatory reporting, healthcare reform and pay for performance-related issues that have the attention of hospital board trustees."
Key to a successful business case is understanding the current forces that are driving healthcare economics. There are numerous treatises on healthcare economics in the literature that can help provide the basic concepts. One of the more notable developments, of course, is the Oct. 1, 2008 action by the Centers for Medicare & Medicaid Services (CMS) to halt reimbursement to hospitals for the cost of treating certain healthcare-acquired infections such as catheter-associated urinary tract infections, vascular catheter-associated bloodstream infections, and certain surgical site infections. This regulation was triggered by the 2006 Deficit Reduction Act that called for a reduction in the increases in Medicare and Medicaid spending by stopping payments for conditions that result in the assignment of a higher-cost diagnosis related group and, according to regulators, are reasonably preventable by the application of evidence-based guidelines. (Graves and McGowan, 2008) Also figuring strongly in the healthcare picture in addition to pay-for-performance is mandatory public reporting of infections.
Infection preventionists must make their individual business cases within this greater context of healthcare economics. Graves (2004) sums up the economic rationale for preventing hospital-acquired infections: "Hospital-acquired infections take up scarce health sector resources by prolonging patients hospital stay; effective infection control strategies release these resources for alternative uses. If these resources have a value in an alternative use, then the infection control programs can be credited with generating cost savings; these infection control programs are costly themselves, so the expense of infection control should be compared to the savings." Additionally, Leatherman, et al. (2003) note that a business case exists if the entity that invests in the intervention realizes a financial return on its investment in a reasonable time frame. This ROI can be achieved through profit, loss reduction, or cost avoidance.
To put it simply, infection prevention programs must be positioned as saving the healthcare institution more money than it costs to fund the program. As Perencevich, et al. (2007) explain, "The purpose is to look purely at the dollar costs and benefits of an infection control intervention or an entire infection control program to justify its existence to hospital administrators."
But this process is not without its challenges. As Perencevich, et al. (2007) observe, "Unfortunately, one current perception is that investments to improve quality might actually financially penalize the hospitals that make these improvements. Because infection control programs are often seen as cost centers and not as revenue generators, they are often identified as potential areas for budget cuts. In fact, many infection control programs have faced downsizing in recent years. Demonstrating value to administrators is increasingly important as healthcare executives are faced with the need to support many initiatives with limited resources."
Infection preventionists don't have to go it alone, however, as Perencevich, et al. (2007) note: "The difficulty in making a business case cannot be overlooked, because many infection control programs often lack the economic expertise necessary to complete such an analysis on their own. Anyone considering a business-case analysis should contact their local institutions finance administrators for assistance in using the available local cost data."
Stone concurs. "Infection preventionists should definitely work with their hospital's finance experts," she says. "It's one thing to go to the literature and say that an infection costs X amount of dollars, but be prepared for your facility's chief financial officer to question you and you will have to defend that number. I would definitely get your institution's finance people on your side ahead of time. Show him or her the literature and ask what he or she thinks this costs at your facility, to be certain that you are incorporating all of the costs involved. You want them to understand the data you have before you make a presentation to institution leadership."
Stone also recommends that infection preventionists do their homework and be prepared in order to ward off presentation-related jitters. "I think it comes down to practice, and knowing your stuff. I remember the first presentation I had to do -- I practiced to no end. You do get better with time but there's nothing like good preparation to make you feel confident. I recommend practicing in front of other people, whether it's family members or colleagues from your local APIC chapter. Preparation and practice will ensure that you will be able to successfully articulate your message."
Looking to the Literature for Assistance
Stone (2005) says that "Economic evidence is needed to assess the burden of healthcare-associated infections (HAIs) and cost-effectiveness of interventions aimed at reducing related morbidity and mortality." That burgeoning evidence can be found in the medical literature, so let's take a quick look at some of the HAI-associated costs that investigators have calculated.
Anderson, et al. (2007) estimated the cost of healthcare-associated infections (HAIs) in a network of 28 community hospitals and to compared this sum to the amount budgeted for infection control programs at each institution and for the entire network. The researchers reviewed literature published since 1985 to estimate costs for specific HAIs. Using these estimates, we determined the costs attributable to specific HAIs in a network of 28 hospitals during a one-year period. Cost-saving models based on reductions in HAIs were calculated. The weight-adjusted mean cost estimates for HAIs were $25,072 per episode of ventilator-associated pneumonia, $23,242 per nosocomial blood stream infection, $10,443 per surgical site infection, and $758 per catheter-associated urinary tract infection. The median annual cost of HAIs per hospital was $594,683 (interquartile range [IQR], $299,057-$1,287,499). The total annual cost of HAIs for the 28 hospitals was greater than $26 million. Hospitals budgeted a median of $129,000 (IQR, $92,500-$200,000) for infection control; the median annual cost of HAIs was 4.6 (IQR, 3.4-8.0) times the amount budgeted for infection control. An annual reduction in HAIs of 25 percent could save each hospital a median of $148,667 (IQR, $74,763-$296,861) and could save the group of hospitals more than $6.5 million.
Roberts, et al. (2010): Our goals were to estimate the costs attributable to healthcare-acquired infection (HAI) and conduct a sensitivity analysis comparing analytic methods. A random sample of high-risk adults hospitalized in the year 2000 was selected. Measurements included total and variable medical costs, length of stay (LOS), HAI site, APACHE III score, antimicrobial resistance, and mortality. Medical costs were measured from the hospital perspective. Analytic methods included ordinary least squares linear regression and median quantile regression, Winsorizing, propensity score case matching, attributable LOS multiplied by mean daily cost, semi-log transformation, and generalized linear modeling. Three-state proportional hazards modeling was also used for LOS estimation. Among 1,253 patients, 159 (12.7 percent) developed HAI. Using different methods, attributable total costs ranged between $9,310 to $21,013, variable costs were $1,581 to $6,824, LOS was 5.9 to 9.6 days, and attributable mortality was 6.1%. The semi-log transformation regression indicated that HAI doubles hospital cost. The totals for 159 patients were $1.48 to $3.34 million in medical cost and $5.27 million for premature death. Excess LOS totaled 844 to 1373 hospital days.
Neidell, et al. (2012) compared differences in the hospital charges, length of hospital stay, and mortality between patients with healthcare- and community-associated bloodstream infections, urinary tract infections, and pneumonia due to antimicrobial-resistant versus -susceptible bacterial strains. A retrospective analysis of an electronic database compiled from laboratory, pharmacy, surgery, financial, and patient location and device utilization sources was undertaken on 5,699 inpatients who developed healthcare- or community-associated infections between 2006 and 2008 from four hospitals in Manhattan. The main outcome measures were hospital charges, length of stay, and mortality among patients with antimicrobial-resistant and -susceptible infections caused by Staphylococcus aureus, Enterococcus faecium, Enterococcus faecalis, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. Controlling for multiple confounders using linear regression and nearest neighbor matching based on propensity score estimates, resistant healthcare- and community-associated infections, when compared with susceptible strains of the same organism, were associated with significantly higher charges ($15,626; confidence interval [CI], $4,339$26,913 and $25,573; CI, $9,331$4,1816, respectively) and longer hospital stays for community-associated infections. Patients with resistant healthcare-associated infections also had a significantly higher death rate.
To understand costs is to understand three broad components of the socio-economic costs of HAIs: direct medical costs, the indirect costs related to productivity and non-medical costs, and intangible costs related to diminished quality of life, according to Scott (2009). Additionally, Scott (2009) says that an important consideration for any economic evaluation of resource use in hospitals is distinguishing between actual micro-costs (the expenditures the hospital makes for goods and services) and charges (what the hospital charges the patient): "Micro-costing provides more precise estimates of the economic value of the resources used in hospital care. However, the prospective payment system currently used by the CMS and other third-party payors to set reimbursement rates for hospitals for their services can lead to distortions in patient costs referred to as cost shifting. Here, hospitals will raise charges above the amount that would accurately reflect actual patient costs to payors with more generous reimbursement schedules which, in effect, subsidizes less generous payers as well as patients who cannot pay for their own care. Thus, the use of hospital charges to reflect the costs of patient care can overestimate the actual costs of resources consumed. Similarly, cost shifting can occur within the hospital when some services are reimbursed at a higher rate than others."
Scott (2009) provides estimates of the annual direct medical costs associated with five major sites of HAIs as calculated by taking estimates of the number of infections and then multiplying these estimates with both a low and a high average patient cost estimate from the published literature. The patient cost estimates can be adjusted for the rate of infection using two different inflation indexes: the Consumer Price Index (CPI) for all urban consumers (CPI-U) and the CPI for inpatient hospital services with all cost estimates adjusted to the most current dollar value. (An important reminder is that because studies in the literature were conducted at different points in time, cost estimates must be adjusted to 2012 dollars in order to make them comparable.) The CPI-U is constructed by the U.S. Bureau of Labor Statistics (BLS) and is a measure of the average change over time in the prices paid by all urban consumers for a market basket of consumer goods and services purchased for day-to-day living. As an estimate of the percent change in prices between any two price periods, the CPI-U is the most widely used measure of inflation and is used by federal and state governments to adjust government income payments or to make cost-of-living adjustments to wages. As Scott (2009) notes, because both indexes measure price changes for broadly defined expenditure groups, there is no research to date on which measure would be most appropriate to use to accurately adjust for inflation in the prices of the hospital resources used to treat HAIs. In his whitepaper, Scott (2009) adjusted all cost estimates using both indexes.
Scott (2009) points to systematic reviews of the published literature on the costs associated with various HAIs in hospitals; updating a previous review from 2002, Stone, et al. derived the following attributable cost estimates: $25,546 for SSI, $36,441 for BSI, $9,969 for VAP and $1,006 for CAUTI. These authors did note that there was considerable variation in the cost methodology used by the studies incorporated in their review which included results from vaccination studies as well as studies on community-acquired infections. Anderson et al. also developed estimates of the cost of HAIs from published studies but used a more stringent inclusion criterion by including only studies that estimated the attributable costs of getting an HAI.
Anderson et al. weighted the various cost results by giving higher weight to estimates from larger studies. The resulting attributable costs of various HAIs included: $10,443 for SSI, $23,242 for BSI, $25,072 for VAP, and $758 for CAUTI. Scott (2009) emphasizes that the results from both systematic studies have limitations and must be used with caution due to lack of consistency between locations, populations and cost information.
Scott (2009) calculated estimated ranges of the total annual costs associated with specific sites of HAI infection in U.S. hospitals adjusted by the two CPI indexes. The infection site with the largest range of annual costs is SSI ($3.2 billion to $8.6 billion using the CPI-U and $3.5 billion to $10 billion using the CPI for inpatient hospital services) while the site with the smallest annual cost is CAUTI ($340 million to $370 million using the CPI-U and $390 million to $450 million using the CPI for inpatient hospital services). The costs associated with the remaining infection sites are also significant with the direct medical cost of CLABSI, VAP, and CDI ranging from $590 million (adjusted by CPI-U) to $2.68 billion (adjusted by CPI for inpatient hospital services), $780 million (adjusted by CPI-U) to $1.5 billion (adjusted by CPI for inpatient hospital services), and $1.01 billion to $1.62 (adjusted by CPI for inpatient hospital services), respectively.
The Pitfalls of Cost Data Analysis
As we have seen from Scott (2009), there is wide variation in cost data, and Stone, et al. (2005) point to varying levels of quality in the economic evaluations related to HAI that synthesize the evidence. In fact, Stone, et al. (2005) recommend the use of guidelines for authors and editors on conducting an economic analysis, development of more sophisticated mathematical models, and training of infection control professionals in economic methods.
Cost data also can be a moving target. Stone (2009) points to a CDC health economist's calculation that the annual hospital costs of HAIs in the U.S. is between $28 billion and $45 billion annually. Stone (2009) notes, "The wide variation in these cost estimates reflects the range of estimates found in the literature for each type of infection and the assumptions made in the analyses. The variation in methods used in conducting the economic analysis include differences in patient populations studied, variations in study settings, inclusion of multiple drug-resistant infections and the perspective of the economic analysis."
Stone (2009) lists the following concepts to consider when determining the attributable costs of HAIs: adjustment for patients' underlying severity of illness and co-morbid conditions and length of stay in the hospital prior to acquiring the infection. "Failure to consider and adjust for these factors can result in biased estimates of attributable cost, usually making the infection seem more expensive."
As Perencevich, et al. (2007) note, "When completing a business-case analysis, it is important to make an honest assessment of the situation. Most hospital epidemiologists or infection control specialists want to increase the resources available for infection control activities, but it is important to avoid overestimating benefits or underestimating staff and time costs. Overestimation in an initial analysis may improve the situation in the short term, but it will hinder efforts and necessary trust in the long term after actual resource audits are performed."
It can be deceptively easy to raise false expectations when discussing cost savings related to infection control. As Graves, et al. (2010) caution, "Monetary valuations of the economic cost of healthcare-associated infections (HAIs) are important for decision making and should be estimated accurately. Erroneously high estimates of costs, designed to jolt decision makers into action, may do more harm than good in the struggle to attract funding for infection control. Expectations among policy makers might be raised, and then they are disappointed when the reduction in the number of HAIs does not yield the anticipated cost savings."
As Graves, et al. (2010) explain, "The aim is to encourage researchers to collect and then disseminate information that accurately guides decisions about the economic value of expanding or changing current infection control activities. Because healthcare resources are scarce, they should be allocated to programs that deliver quantifiable health benefits. A rule of thumb for decision-making is that the more benefit gained per dollar spent, the better. This applies to those working to reduce the number of HAIs. They should aim to allocate their budget across infection prevention strategies that deliver the largest possible health benefit. To demonstrate the 'biggest bang for your buck' argument, estimates of how health benefits (the bang) and costs (the buck) change with the adoption of novel infection control interventions are required. That increasing investment for infection control is economically justified is not questioned. HAI is a major problem that prolongs hospital stays, prevention is relatively cheap, and many prevention strategies are effective. Whether the economic argument has always been made in the best way and whether optimal analytic methods have been used to estimate the primary economic parameters are worth discussion."
These concepts are complex for the individual who is new to business-case calculations. Essentially, as Perencevich, et al. (2007) explain, "Important concepts to consider when determining the attributable costs and outcomes of nosocomial infection are adjustment for prior length of stay, severity of illness, and underlying co-morbid conditions. Failure to consider and adjust for these factors can result in biased estimates of attributable cost."
Deciding which costs to measure is a critical step. According to Perencevich, et al. (2007), "Potential approaches to evaluating the economic burden of nosocomial infections in an institution include the following measurements: hospital costs, hospital charges, resources used, and/or actual reimbursed charges. Hospital costs include daily operating costs (fixed costs) which do not vary based on patient volume, as well as the cost of drugs, tests, and other patient care-related activities (variable costs), which are dependent on the number of patients admitted or their length of hospitalization ... Hospital costs can be a useful outcome measure for an individual hospital because they best reflect the actual economic burden of the hospital. Although some institutions have implemented complex cost accounting systems that track resources used and assign costs, in most institutions, actual or true costs are difficult to retrieve. In contrast, hospital charges are less indicative of actual cost but are usually easy to retrieve from administrative databases and are consistent from patient to patient in most settings. Because hospital charges typically overestimate actual cost by 25 percent to 67 percent, adjustment can be performed by use of cost-to-charge ratios. Both hospital and departmental cost-to-charge ratios are determined annually on the basis of data submitted to the Centers for Medicare and Medicaid Services. Hospital cost-to-charge ratios may be a more accurate measure of costs for a cohort of patients in multiple diagnosis related groups, while departmental cost-to-charge ratios may be more accurate for a cohort of patients in the same diagnosis related groups."
Graves, et al. (2010) say that solid decision-making about infection prevention should emerge from cost-effectiveness research: "Many decisions about expanding infection control have been based on partial economic studies that show only the gross cost of an HAI. Costing studies may influence decision-makers because the estimated gross cost per HAI has been found to be very high, and the conclusion that the cost saved from expanding infection control will exceed the cost incurred is assumed to be true without rigorous analysis. To evaluate completely a new infection control strategy requires accurate estimates of the extra cost of implementing the strategy, the cost savings from the predicted number of prevented cases of HAI, and the clinical effectiveness and health benefits."
They emphasize that simple costing studies that merely show the gross costs of an HAI are partial evaluations and do not advance knowledge about making a comprehensive business case for infection prevention.
Graves, et al. (2010) add that new and emerging data "seriously question the validity of previously applied methods used to determine the cost of HAI." They explain: "The main cost of an HAI is the extra stay in hospital. Estimates of extra length of stay based on sounder statistical methods tend to show a shorter estimated extra stay, which means that the cost of an HAI may have previously been overestimated. Also problematic is the method used to attach monetary value to lost bed-days, which is often based on cost accounting practices and not economic principlesyet these two disciplines have quite different objectives. There may be serious problems with how the economic costs of an HAI have been estimated."
Assuming this, Graves, et al. (2010) encourage practitioners to consider three lines of inquiry: Why measure the cost of an HAI? What outcome should be used to measure the cost of an HAI? What is the best method for making this measurement? Let's look at these three questions more closely:
1. Why measure the cost of an HAI?
As Graves, et al. (2010) explain, "The primary reason to understand the cost of an HAI is to inform decisions about how to reduce the problem. Because healthcare resources are scarce, HAIs should be reduced by allocating resources only to efficient infection control programs. One approach is to maximize the amount of health gained from a defined pot of resources. This is called an extra welfarist view of economics and is used widely in health services decision-making." The extra welfarist approach uses the following rule: The change to cost from a decision to adopt a new health intervention (such as a novel infection control intervention) should be adequately compensated by the change to health benefit. Changes to cost are summarized in monetary terms, and changes to health benefit are normally described by means of quality-adjusted life-years (QALYs), which combine information on the quantity and quality of years of life gained. As Graves, et al. (2010) explain further, "The number of QALYs gained from infection control demonstrate improved quality of care, because lives are saved and events that reduce the quality of life for hospital patients are avoided."
2. What outcome should be used to measure the cost of an HAI?
A formula can be applied to this question. As Graves, et al. (2010) explain, "The number of bed-days lost to a case of HAI is an appropriate outcome to describe a large proportion of the cost, and this number can be represented by the letter 'q.' The marginal number of bed-days released by reducing the rate of HAIs may well have a positive economic value or price, which can be elicited from the appropriate source and represented by the letter 'p.' A large part of the cost of an HAI is, therefore, the quantity of bed-days saved (q) multiplied by their economic value or price (p) or 'pq.' The remainder of the costs of an HAI arise from consumable items used to treat the infection and from physician fees that are billed separately. The consumables saved can result in substantial cost savings. ... We propose that counting the number of bed-days saved first (q) and valuing them in dollar terms second (p) is a powerful method for describing much of the economic cost of an HAI."
3. What is the best method for making this measurement?
As Graves, et al. (2010) explain, "Because costs are strongly dependent on length of stay, we need to accurately measure the extra length of stay caused by a case of HAI (q). Any method used should account for the fact that HAIs arise at different times during a hospital stay in different patients and that other factors influence length of stay, such as primary diagnosis and co-morbidity."
One method cited by Graves, et al. (2010) is to compare physician assessment with matched cohort studies, where infected patients are matched with uninfected control subjects on variables thought likely to cause an excess stay. As Graves, et al. (2010) explain, "Physician assessments provide the ideal measure but are time-consuming; matched cohort studies are easier to conduct but suffer from two sources of bias. The first bias arises because some patients are predisposed to a long hospital stay regardless of HAI status, and matching on confounding variables is not able to control all the bias. The second bias arises from increasing the number of matching variables in an attempt to control the first bias, as this often causes infected individuals to be selected out of the study because the pool of uninfected control subjects is exhausted. In matched cohort studies, one can only find the best tradeoff between these two biases; they cannot be simultaneously eliminated."
Graves, et al. (2010) also point to statistical models that control for differences between patients at the analysis stage rather than at the design stage: "A statistical model can be built to describe the relationship between a cost outcome, such as length of stay in the hospital, and predictors of that outcome. An advantage is that multiple predictors can be included without selecting out cases of HAI. Statistical models, such as event history analysis or survival analysis, can be used to account for the time-varying nature of infection. They model the hazards or rates between hospital admission, potential onset of HAI, discharge alive from the hospital, and death in the hospital. Additional time-dependent information, such as daily intubation status, may also be included."
Scott (2009) explains that, "The most common analytical approach for measuring the cost of HAIs by infection site usually employs some type of observational epidemiologic study in which a group of patients not infected with a specific microorganism is compared to a group of infected (or exposed) patients. However, study populations and methods vary and include differing economic evaluation methods (cost analysis, cost- effectiveness analysis, or cost-benefit analysis), observational study designs (prospective versus retrospective, concurrent versus comparative design, matched versus unmatched analysis, selection and number of confounders used), patient populations and settings (e.g., ICU, specific disease), and cost information used (charges, adjusted charges, or micro-cost data)."
Graves, et al. (2007) cites concern about inherent bias in cost calculations: "Research-based models that describe the economics of additional infection control programs therefore rely on valid estimates of the independent effect of HAI on length of hospital stay and cost, but it is difficult to make bias-free estimates."
One method, direct attribution, requires an expert reviewer to assess the extra cost from HAI. As Graves, et al. (2007) explain, "This method has been criticized as being subjective and not reproducible, and comparative attribution studies have been preferred by the research community. Researchers undertaking comparative attribution studies use data collected from a cohort of hospitalized patients and either select a subset of infected patients who are then matched with uninfected controls for variables thought likely to affect cost outcomes (e.g., age, sex, and co-morbidities) or build multivariable statistical regression models that describe the relationship between HAI and cost outcomes, while controlling for other factors thought likely to affect cost outcomes. The disadvantage of matching is that infected patients can only be matched to uninfected controls for a limited number of variables... Matching too few variables might cause bias from omitted variables because important factors that explain the variation in cost outcomes are excluded. The consequence of this bias is that the cost attributed to HAI is either overstated or understated. If case patients are subsequently excluded from the study to match more variables (i.e., to mitigate bias from omitted variables), then a selection bias arises because not all case patients have the same opportunity to be included in the comparison of cost outcomes. The use of statistical regression analysis for a cohort of patients can avoid selection bias completely and presents an opportunity to reduce bias from omitted variables. A correctly specified statistical regression model will summarize the association between the outcome variable (i.e., length of stay or cost) and the independent variables (i.e., HAI and other observable factors that might explain variation in outcomes)."
Another source of bias arises from the relationship between the variables HAI and LOS. According to Graves, et al. (2007), "Although we know that HAI increases the length of stay, there is good evidence that length of stay also increases the risk of HAI. This reverse causality induces a correlation between the error terms and the independent variables, leading to biased estimates and tests of hypotheses. This problem is called 'endogenous variables bias' and has been discussed in the context of HAI ...Controlling bias from endogenous variables and interpreting the results of an unbiased model is a methodological challenge for future research."
In the debate between using fixed versus variable costs, Graves, et al. (2007) sides with using length of stay and variable costs as the most appropriate way to describe the economic cost of HAI: "Between 80 percent and 90 percent of the costs of running a hospital are fixed in the short term, and the short term is generally the time frame in which decisions about investments in infection control are made. The financial expenditures made for fixed costs, as recorded by the hospitals cost accountants, are important for those who manage the cash flow and financial viability of the hospital. However, they are largely irrelevant for economic analysis and decision making in the short term, because these expenditures for fixed costs will not change with rates of HAI. A more useful measure on which to base decisions about additional investment in infection control is the number of bed-days used for HAIs, and the monetary value of these bed-days depends on their value for alternate uses (i.e., the revenue from providing treatment to newly admitted patients)."
Graves, et al. (2007) add, "In contrast, expenditures for variable costs, such as dressings, drugs, fluids, gloves, gowns, and other consumables used by HAI, will change with rates of HAI; however, these items are much lower in value compared with the high fixed overheads typical of healthcare organizations. Because the main reason we wish to understand the cost of HAI is to demonstrate how costs will change with increased investments in infection control, we should concentrate on measuring the cost outcomes that change rather than those that do not; therefore, data from the cost-accounting department of a hospital that describe expenditures for fixed costs are not useful. Length-of-stay data and expenditure for variable costs are more useful, as well as easier to procure and interpret."
It's clear that additional cost analyses are warranted. As Yokoe and Classen (2008) observe, "Unfortunately, estimates of the economic impact of interventions to reduce HAIs required for optimal decision-making by infection control experts and hospital administrators are limited in their availability. High-quality cost-effectiveness analyses are clearly needed. Numerous regulatory requirements for infection control infrastructure at the healthcare delivery and organizational level are currently in place and are likely to expand, given the current public focus, complicating research efforts to effectively evaluate the true cost-effectiveness of infection control programs."
The Nuts and Bolts of Making the Business Case
So, after wading through an immense amount of data, how does one put it all together? There is no magic formula for making the business case, but there is a very helpful strategy outlined in a guideline from the Society for Healthcare Epidemiology of America (SHEA). In this guideline, Perencevich, et al. (2007) provide the tools necessary to complete a thorough business-case analysis for infection prevention, whether it is to justify the viability of a program or to add an intervention.
According to Perencevich, et al. (2007) the process of completing a business-case analysis can be broken down into several steps.
Step 1: Frame the problem and develop a hypothesis about potential solutions
If you want to implement an intervention to reduce infections, it might be necessary to hire additional staff for your infection control department. Therefore, you must convincing hospital administration that the cost of an additional full-time employee (FTE) will be offset by the cost savings created by a reduced infection rate.
Step 2: Meet with key administrators
Meeting with these individuals to accomplish three goals: (1) obtain agreement that the issue that you are addressing is of institutional concern and has the support of hospital leadership; (2) ascertain that administrators can help identify critical individuals and departments who may be affected by your proposal and whose needs should be included in the business-case analysis; (3) ascertain that administrators can help identify the critical costs and factors that should be included in the analysis.
Step 3: Determine the annual cost
In the aforementioned example, the cost is the salary of an FTE plus the price of benefits for that individual -- a full-time IP might earn $60,000 and benefits may cost the institution 28 percent of that total, which means the hospitals cost for the FTE is $76,800.
Step 4: Determine what costs can be avoided through reduced infection rates
Optimally, the up-front cost of hiring a new IP can be recouped in the current fiscal year. You could look for data from your own institution to determine whether a particular HAI decreased after hiring this individual. Alternatively, the literature can be reviewed to see whether others have published data regarding a similar issue.
Step 5: Determine the costs associated with the infection of interest at your institution
If hospital administrative data are readily available, the attributable cost of an infection could be calculated; alternatively, if they are not available, a literature review might be performed. A method suggested by Ward and colleagues focuses on optimizing the investment in fixed costs instead of focusing on cost savings when justifying a new program. In infection prevention, the greatest opportunity to improve hospital profits comes from reducing excess length of stay. Thus, instead of focusing on how much an additional hospital-day costs, as above, one could estimate the additional revenue gained by filling the additional bed-days available, because patients who do not develop infections are discharged sooner.
Step 6: Calculate the financial impact
To complete the business-case analysis, take the estimated cost savings or additional profits and subtract the costs of the up-front outlay -- in the aforementioned example, it would be the salary and benefits for an IP.
Step 7: Include the additional financial or health benefits
Many infection control interventions have multiple benefits. For instance, a contact isolation program developed in response to an outbreak of Acinetobacter baumannii infections would also be expected to reduce the rate of methicillin-resistant Staphylococcus aureus (MRSA) infections and vancomycin-resistant Enterococcus (VRE) infections. In the example, the efforts of the new IP could also be expected to reduce the incidence of catheter-related bloodstream infection, prevent other SSIs, and improve compliance with hand hygiene. All of these factors need to be included in a proper business-case analysis. To further make the business case for an additional IP, one must include the reduced costs expected to be associated with these other types of preventable infections. After these are included, it would be expected that hiring an additional IP would save the hospital money.
Step 8: Make the case for your analysis
The completed analytical portion of a business case must be complemented by effective communication of its findings and your recommendations to critical stakeholders at your institution.
Step 9: Prospectively collect cost and outcome data once the program/intervention/resource is in place
If an infection control intervention program has been in existence for several years and has kept infection rates low, administrators might be tempted to eliminate or reduce the program even though associated costs would have been higher in its absence. Therefore, it is imperative that intervention-specific outcome data and costs be collected after the intervention is implemented. It is important to show stable outcome rates or continued improvement associated with the intervention, to maintain consensus support and organizational momentum.
In conclusion, making the business case requires diligence but can yield rich returns on investment. "The simplest business case for infection prevention is knowing what your infection rates are and using costs of the infection rates from the literature, saying if we can decrease this many infections, we'll save this much money," says Stone. "Whether it's making a business case for the whole department if it is at risk, or making a business case for another staff member or a new intervention, you must do your homework, make certain that the data is correct, make those connections with your hospital finance experts, and be ready with your facts so you are not shot down. Infection preventionists are so smart, resourceful and passionate -- that's very helpful when facing your hospital administrators."
As infection preventionists do their part, Graves, et al. (2010) issues a call to action for researchers: "The 'HAI costs a lot' approach to influencing decision making has served the infection control community well. Important articles have stated that very large costs arise from HAIs; all have been cited frequently and used to attract resources toward infection control programs. The time has arrived, however, for the methodological advances that have been achieved in this area to be implemented by researchers. Complete economic evaluations that include changes to all costs and health benefits should be performed. The information used to update these studies should be of high quality and bias free ...The time when reliable economic arguments will be paramount for obtaining extra resourcesand even retaining existing onesis close. Those working toward reducing the number of HAIs should craft valid economic arguments on the basis of sound methods and use them to build strong and cost-effective infection control programs."
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