A primary concern of hospital CEOs is the financial position of their organizations (Gamble, 2015). Both for-profit and not-for-profit hospitals and healthcare systems in the United States are under increasing economic pressure attributable to a number of factors. These include changes in public and private payer reimbursement tied to improvements in quality of care, patient safety mandates, and increases in costs of the inputs to healthcare delivery such as labor, medical devices, pharmaceuticals, and general supply expenses.
Changes to healthcare reimbursement models are emerging to address the persistent patient safety and quality-of-care issues. Nearly 20 years ago, the Institute of Medicine (IOM) report titled To Err Is Human: Building a Safer Health System indicated both patient safety and quality needed to become a prominent focus in healthcare delivery (Kohn, Corrigan, & Donaldson, 2000). The report suggested that nearly 100,000 deaths from preventable medical errors result in an additional cost of between $17 and $29 billion annually. Little seems to be changing. A recent study conducted at Johns Hopkins University has found medical errors to be the third leading cause of death in the United States, with reported preventable medical errors surpassing 250,000 annually (Makary & Daniel, 2016).
It is likely that the scale and implied cost of errors affect hospital profitability. Product and service quality has long been known to be a determinant of a firm’s performance and sustained financial success. Studies have shown a positive quality– profitability relationship across a diverse set of industries (Angelini & Bianchi, 2015; Haines, 2016; Mellat-Parast, Golmohammadi, McFadden, & Miller, 2015). However, not all quality improvement initiatives are economically sustainable or provide a return to the organization (Aaker & Jacobson, 1994). Harkey and Vraciu (1992) and Alexander, Weiner, and Griffith (2006) found that hospitals that pursue broad and intense baseline quality improvement programs demonstrated improved financial performance. Several authors have suggested that this is an area requiring further study to better understand the factors associated with overall hospital profitability (Bai & Anderson, 2016; Holt, Clark, DelliFraine, & Brannon, 2011; Turner, Broom, & Counte, 2015).
The amount of recent literature pertaining to hospital profitability is limited. Gapenski, Vogel, and Langland-Orban (1993) studied a number of hospital profitability determinants including teaching status, hospital size, ownership status, system affiliation status, age of plant, case mix, average length of stay, and others. They categorized the determinant factors as organization, management, patient mix, and market variables (Gapenski et al., 1993). Holt et al. (2011) added to the literature by examining current studies and categorizing ownership, governance, management strategy, integration, and quality as the five most-studied determinants. Turner, Broom, Elliott, and Lee (2015) looked at the determinants of hospital profitability using the DuPont analysis tool and recommended further study of the relationship between quality outcomes and profitability. Bai and Anderson (2016) used similar metrics in their study of the important factors that financially successful hospitals share. They found that for-profit ownership, higher markup, regional power, and price regulation had the largest positive association with hospital profitability and identified quality performance as an area for further study.
The IOM report elevated healthcare quality concerns to a national level (Devers, Pham, & Liu, 2004). Subsequent efforts by numerous organizations including the Centers for Medicare & Medicaid Services (CMS), the Centers for Disease Control and Prevention, and The Joint Commission yielded a list of evolving programs designed to improve patient safety and quality through enhanced organizational performance. The Affordable Care Act of 2010 established the Hospital Value-Based Purchasing (VBP) Program that rewards more than 3,000 acute care hospitals with incentive payments for the quality of care they provide to Medicare beneficiaries. Numerous factors combine to influence an acute care hospital’s VBP score. Among them is performance on numerous Surgical Care Improvement Project (SCIP) factors.
The SCIP originated in 2006 from a partnership between the CMS and the Centers for Disease Control and Prevention to reduce perioperative mortality and morbidity. Data elements address infection, cardiac, venous thromboembolism, vascular, respiratory, and global measures. In 2013, these elements were connected to Medicare reimbursement under the VBP program with an expectation that commercial insurers would follow (Weston, Caldera, & Doron, 2012).
The VBP program continues to change on an annual basis. In addition to the SCIP measures, numerous other data elements have been added since its inception. The SCIP data were included in Medicare’s Clinical Process of Care domain between 2013 and 2016. The VBP program now includes evaluation of the patient experience of care using the Hospital Consumer Assessment of Healthcare Providers and Systems survey, numerous clinical outcomes, and patient safety indicators (such as central line-associated bloodstream infections, catheter-associated urinary tract infections, methicillin-resistant Staphylococcus aureus, pneumonia, heart failure, and acute myocardial infarction). The patient experience of care evaluation also includes efficiency as determined by the Medicare Spending per Beneficiary. These measures join other CMS value-based programs, including the Hospital Readmissions Reduction Program, the Hospital-Acquired Condition Reduction Program, the Physician Value-Based Modifier Program, and the Medicare Access and CHIP Reauthorization Act of 2015 Quality Payment Program.
Despite the prevalence of improvement programs, the evidence is mixed regarding whether these programs have yielded positive effects (Figueroa, Tsugawa, Zheng, Orav, & Jha, 2016; Glickman et al., 2007; Landrigan et al., 2010; Rosenthal, Frank, Li, & Epstein, 2005). Some have asked if the efforts invested in quality improvement have any meaningful return on investment (Swensen, Dilling, McCarty, Bolton, & Harper, 2013). Others have questioned whether the incentives in the current VBP models are sufficient to drive meaningful improvement and if the aggregate results are worth the efforts involved to improve performance (Jha, 2013; Jha, 2017; Turner et al., 2015).
Patient Safety and Financial Performance
According to Rust, Zahorik, and Keiningham (1995), in the services sector, superior service quality supports profitability via two separate pathways. First, the authors conceptualize how improvement efforts indirectly boost revenues by means of improved perceptions of quality, customer satisfaction, customer retention, positive word-of-mouth, and attraction of new customers. Second, the authors also consider the direct impact of cost reductions generated from service quality improvement. Given the service role of the healthcare sector, we can look to the Rust, Zahorik, and Keiningham model as a guide to help conceptualize how improved hospital quality performance can simultaneously increase revenues, reduce costs, enhance market share, and positively influence hospital profitability.
Although few studies investigate individual safety and quality measures as they relate to healthcare finance, the drivers of profitability and overall safety outcomes have been studied separately. Previous studies focused on outcome measures of adverse events and the associated subsequent economic costs (Rezende, Or, Com-Ruelle, & Michel, 2012).
Beauvais, Richter, and Kim (2019) examined the impact of hospital safety scores from the Leapfrog Group on hospital financial performance and found a positive relationship between the scores and all financial measures tested. However, these authors did not examine each of the component Leapfrog factors to examine which contributed the most to financial outcomes. Nevertheless, their findings provide guidance to inform the current study. Therefore, we assert the following:
Hypothesis 1 (H1): Hospitals with better SCIP performance will have higher operating margins.
Hypothesis 2 (H2): Hospitals with better SCIP performance will have higher net patient revenues.
Data and Sample
The American Hospital Association (AHA) Annual Survey Database and the AHA financial module reflect an annual comprehensive census conducted on more than 6,400 hospitals. The survey, which has been completed since 1946 (AHA, n.d.), provided the dependent and control variables for our study, and the financial module provided data from the Healthcare Provider Cost Reporting Information System managed by the CMS.
The Hospital Compare tool offers information about the quality of care at more than 4,000 Medicare-certified hospitals in the United States. The tool gives consumers the ability to both find local hospitals and compare the history of the quality of care delivered. We used the Hospital Compare website archive for the independent variables of interest for our study. All independent variables of interest were extracted from the VBP SCIP datasets (CMS, n.d.).
The potential for reverse causality prompted us to use older data from the Hospital Compare tool to ensure that our two datasets did not fully overlap. This would allow for the impact of improved patient safety performance to be realized in the hospital financial reporting systems. The practice of replacing an explanatory variable with its lagged value to counteract endogeneity is prevalent across a wide variety of disciplines in economics and finance (Beauvais et al., 2019; Buch, Koch, & Koetter, 2013; Stiebale, 2011). The AHA financial data were extracted from the 2015 database, which includes data as of November of each calendar year. The Hospital Compare data used for the current study reflected the 2014 calendar year. The datasets were linked by matching the Medicare provider number.
Whether for-profit or not-for-profit, hospitals must remain financially viable if they are to continue to offer services. We selected two common measures of profitability as dependent variables for our study: operating margin and net patient revenue. Operating margin is generally accepted as the total operating revenue less total operating expenses divided by total operating revenue (Healthcare Financial Management Association, n.d.). Gapenski et al. (1993) used operating margin to capture the amount of revenue earned by hospitals and to illustrate their ability to control costs. Singh and Wheeler (2012) used operating margin because of its ability to focus on the hospital’s core mission, patient care, and subsequent revenues. More recently, Richter and Muhlestein (2017) used operating margin as a dependent variable in their study concerning the relationship between patient experience and hospital profitability.
Net patient revenue, our second dependent variable, generally refers to all revenue from patient care services less expenses and other associated costs (e.g., bad debt). McDermott (2009) used net patient revenue as a measure for his study relating profitability to hospital system affiliation. Richter and Muhlestein (2017) used net patient revenue as a profitability measure in their study of patient experience and hospital profitability. Finally, Beauvais et al. (2019) used net patient revenue as a dependent variable in their examination of the association between the Leapfrog hospital safety score and profitability.
The independent variables of interest include SCIP-Inf-1 (prophylactic antibiotic prophylaxis received within 1 hr prior to surgical incision), SCIP-Inf-2 (prophylactic antibiotic selection for surgical patients), SCIP-Inf-3 (prophylactic antibiotics discontinued within 24 hr after surgery end time), SCIP-Inf-4 (cardiac surgery patients with controlled 6 A.M. postoperative serum glucose management), SCIP-Inf-9 (urinary catheter removal postsurgery), SCIP-Inf-Card-2 (beta-blocker during the perioperative period), and SCIP-Inf-VTE-2 (venous thromboembolism prophylaxis).
We selected these measures for three reasons. First, we wanted to provide specific guidance to the practitioner community regarding where specific resource allocation can have actionable results. Although aggregate scores are appropriate for deriving general effect, they lack the specificity that can guide operational decision-making. Second, we wanted to begin our analysis in the heart of the acute care hospital: the operating room. The selected SCIP variables comprise the full set of SCIP measures available in the archived Hospital Compare data. Third, improved organizational performance across the U.S. healthcare system on several of these measures led CMS to transition several of the variables from mandatory reporting to voluntarily reporting in 2017 and beyond (CMS, 2015a, b). Thus, the data used for this study are evaluating measures considered to be sufficiently improved at the highest level of performance and represent one of the last full years of data available for research purposes.
Our control variables included bed count, average length of stay, case mix index, government payer mix, wage index, outpatient service mix, rural percentage, government operation, sole community provider status, for-profit designation, network membership, teaching facility designation, system membership, and occupancy rate. These variables are consistent with the other studies on hospital profitability (Beauvais et al., 2019; Gapenski et al., 1993).
We used a retrospective, cross-sectional study design. The unit of analysis for this study was hospitals drawn from the AHA data. Our sample size varied by SCIP variable of interest and ranged between 1,122 hospitals for SCIP-Inf-4 and 2,933 for SCIP-VTE-2. Preliminary multivariate regression analyses yielded problematic residuals, so we analyzed the two outcome measures of hospital profitability in 14 separate multinomial logistic regressions. We considered an ordinal logistic regression, but tests on the proportional odds assumption showed it did not hold. Because our outcome measure was categorical, we analyzed how a change in patient safety performance affected the relative risk of whether a hospital belonged in one profitability quartile or in another. This approach has been accepted in the literature for profitability studies (Beauvais et al., 2019). We used Stata release 14 (StataCorp, 2015) for data analyses.
Table 1 reflects the complete list of variables and descriptive statistics including the mean and standard deviation. Our full sample differed from the full AHA dataset as the AHA data contained hospitals drawn from more urban, more not-for-profit, higher bed size, higher case mix, higher government payer mix, higher outpatient service mix, and higher wage index facilities; the AHA sample also included more sole community providers, more network members, and more system members.
Table 2 indicates the mean of each quartile for each of the two outcome variables. Quartile 1 had a mean net patient revenue of $7.89 million and operating margin of –42.44%, quartile 2 had a mean net patient revenue of $27.46 million and operating margin of –4.16%, quartile 3 had a mean net patient revenue of $90.61 million and operating margin of 3.68%, and quartile 4 had a mean net patient revenue of $463 million and operating margin of 17.5%.
Tables 2 and 3 also show the relative risk ratios from our multinomial logistic regressions. Relative risk ratios measure the impact a one-unit increase in patient safety performance has on the relative risk of a hospital being in one profitability or revenue quartile versus another. Our results generally indicate that higher patient safety performance is associated with a higher relative risk of a hospital being in a higher quartile of profitability, but there is some variation among our independent variables of interest.
Net Patient Revenue
SCIP patient safety performance was a significant predictor in five of the seven multinomial logistic regressions performed: SCIP-Inf-1, SCIP-Inf-3, SCIP-Inf-9, SCIP-Card-2, and SCIP-VTE-2. However, SCIP-Inf-2 and SCIP-Inf-4 were not significant when regressed on the net patient revenue-dependent variable. Of our profitability analyses, the pseudo-R2 values were largest in these models with values ranging between 57.41% (SCIP-Inf-3) and 60.23% (SCIP-VTE-2). Our analysis generally indicates hospitals that increased patient safety performance had a higher relative risk to be in a higher revenue quartile. For SCIP-Inf-1, the relative risk of a hospital being in the highest quartile of net patient revenue (quartile 4) compared to the bottom level (quartile 1) was 16.88 (β = 16.88, p < .05). This was repeated for SCIP-Inf-3 (β = 13.78, p < .01), SCIP-Inf-9 (β = 15.80, p < .001), SCIP-Card-2 (β = 12.12, p < .05), and SCIP-VTE-2 (β = 12.36, p < .05).
SCIP patient safety performance was a significant predictor in all seven multinomial logistic regressions performed, although the pseudo-R2 values for the operating margin models analyzed only ranged between 6.95% (SCIP-VTE-2) and 8.16% (SCIP-Inf-3). Consistent with our other analysis, hospitals that increased their patient safety performance had a higher relative risk to be in a higher operating margin quartile. For SCIP-Inf-1, the relative risk of a hospital being in the highest quartile of operating margin quartile 4 compared to the bottom level quartile 1 is 20.13 (β = 20.13, p < .001). This was repeated for SCIP-Inf-2 (β = 23.75, p < .001), SCIP-Inf-3 (β = 23.76, p < .001), SCIP-Inf-4 (β = 17.50, p < .001), SCIP-Inf-9 (β = 8.34, p < .001), SCIP-Card-2 (β = 9.44, p < .001), and SCIP-VTE-2 (β = 6.07, p < .001).
Key Research Findings and Implications
Our results indicate that improved patient safety performance is associated with both increased operating margin and net patient revenue across most measures of SCIP patient safety performance. To provide one set of interpretations, for every one-point increase in a hospital’s patient safety performance in antibiotic prophylaxis received within 1 hr prior to surgical incision (SCIP-Inf-1), the hospital’s relative risk of being in the top quartile for net patient revenue, compared to the bottom quartile, is 16.88 times greater (p < .05). Likewise, for every one-point increase in a hospital’s patient safety performance in urinary catheter removal post-surgery (SCIP-Inf-9), the hospital’s relative risk of being in the top quartile for operating margin, compared to the bottom quartile, is 8.34 times greater (p < .001). Comparable interpretations can be made for the other statistically significant SCIP measures in our analyses.
Our findings appear to support the conceptualized drivers of profitability in service-based industries proposed by Rust et al. (1995). These results are also generally consistent with the empirical work by Beauvais et al. (2019) in their examination of the association between Leapfrog score performance and hospital profitability. In our estimation, our results follow both the revenue enhancements that result from market recognition of improved quality performance by payers, providers, and the public as well as the direct savings generated from reduced infections and associated sequelae. In fact, hospitals that have the highest surgical quality are also those with the greatest satisfaction scores (Tsai, Orav, & Jha, 2015). However, a more nuanced understanding of the impact of SCIP performance is required to appreciate why some SCIP measures are not associated with net patient revenue when other measures appear to be connected.
Beauvais et al. (2019) suggested that improved hospital safety scores were associated with a relative risk of being in the top versus bottom quartile of financial performance for operating margin and net patient revenue. The study by Beauvais et al. (2019) was original in its investigation and observation of the interplay of profitability and safety in hospital systems, but our current study takes a deeper dive into patient safety data to provide more specific evidence of an association between patient safety performance in very specific clinical areas and hospital financial outcomes.
This study’s strongest findings involve the net patient revenue-dependent variable where pseudo-R2 values exceeded 57% across all variables of interest, indicating that our model has high explanatory power. The association between clinical introduction of antibiotic prophylaxis received within 1 hr prior to surgical incision (SCIP-Inf-1), discontinuance of prophylactic antibiotics within 24 hr after surgery end time (SCIP-Inf-3), urinary catheter removal post-surgery (SCIP-Inf-9), beta-blocker use during the perioperative period (SCIP-Card-2), and venous thromboembolism prophylaxis (SCIP-VTE-2) were all found to be significantly associated with the relative risk of being of the hospital being in the highest net patient revenue quartile. This is a key indicator to hospital executives that targeted attention to these areas can, and likely will, result in subsequent positive net patient revenue.
Although more work needs to be done, we contend that our findings establish the first focused association between specific areas of patient safety performance and organizational financial performance. This effort begins to provide an evidence base that supports focused investments in patient safety that can be profitable irrespective of the influence of current or future VBP initiatives. In essence, we are suggesting that improved patient safety performance can be its own reward. With our results, administrators may be able to quantify a return on investment among the patient safety dimensions we have analyzed.
Study Limitations and Guidance for Future Research
This is among the first known studies that focused on the association between hospital patient safety and financial performance. However, this study is limited in several ways. First, although our analysis explained a large percentage of the variation in net patient revenue (pseudo-R2 = 57% or greater), we recognize that the healthcare sector continues to evolve and thus the potential for additional exogenous effects on hospital profitability are present including staffing, market characteristics, and other factors that were beyond the scope of our dataset. Our findings are limited also by the relatively low explanatory power of the operating margin variable. Additional research into the drivers of this dependent variable is warranted. Also, the SCIP means are near maximum compliance, suggesting that the average to top-performing hospitals have limited room to improve performance on these measures. An opportunity also exists for future researchers to examine our association across time and across additional patient safety measures.
While pay-for-performance initiatives for hospitals continue to evolve, there are those who believe that VBP programs are not sufficient to meaningfully drive improvement in patient outcomes (Jha, 2017). However, we suggest that advancements in patient safety should be undertaken regardless of what occurs at the public policy level. As the depth, breadth, and quality of data reporting improve, patient safety performance improvement across multiple areas is associated with substantially higher hospital financial results. Specific areas where improvement can yield optimal financial return is worthy of additional scrutiny as financial pressures on hospitals continue to increase.
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