ANSWER
Is the financial status of a hospital related to the quality and safety of care delivered? While this simple question has attracted considerable attention, it has been challenging to answer. Efforts to control the high healthcare costs in the United States presuppose hospitals can do more with less. Hospitals face considerable pressure to lower costs while maintaining quality outcomes [1, 2]. Initiatives to financially incentivize quality, such as public reporting and value-based payment (VBP), will succeed in improving population health for all only if they are designed to account for the complicated relationship between quality and facility financial stability. Otherwise, these programs run the risk of perpetuating the “rich get richer” history of the American healthcare system and will continue to penalize safety net hospitals and their underserved populations[3]

Prior literature suggests that some aspects of patient care may be compromised as a hospital’s financial condition declines [4–11]. However, studies directly examining the correlation between economic status and the quality and safety of patient care have been equivocal, and the findings are uncertain. The lack of clear associations may be due to the poor predictive validity of the measures of finances and quality. When considering financial performance, many financial distress models rely on specific stress indicators, including bankruptcy and closure data, which are easier to obtain but do not represent the range of financial health [12]. Other studies used only narrow measures of hospital financial performance (e.g., operating margin), which do not capture the full range of revenue potentially available for investment in quality improvements [13]. Concerning quality performance, studies have generally focused on specific outcomes, such as mortality or hospital readmission from pneumonia, heart failure, or myocardial infarction [14–17]. The expansion of public reporting by the Centers for Medicare and Medicaid Services (CMS), as part of VBP, has widened the pool of measures available for quality analysis. [14, 18].

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While most previous studies have used limited approaches in describing the abstract measures representing hospital financial health and quality of care, this paper considers an entire profile of economic characteristics and patient quality and safety measures. We hypothesize that robust measurement of these financial, quality and safety measures improves the likelihood of observing the relationship between poor financial health and inferior hospital quality of care and patient safety. We attempted to determine whether a composite economic indicator derived from a machine learning methodology (principle component analysis) would outperform already established financial indicators used in the literature in examining the correlation with quality and safety of patient care. In the next section, we present a conceptual framework and the contribution of our analysis, followed by a summarization of several studies assessing issues relevant to the ones we are examining. This is followed by a discussion of study methods and measures and concludes with the presentation of results and policy implications.

Conceptual framework
The hypothesis that financial status and quality/safety are linked has strong construct validity. Good hospitals with solid cash flows can pay off debt quicker, which allows them to further invest in the capital at lower costs than cash-strapped hospitals. With more money, these facilities can make sizeable investments in clinical and administrative information technology and monitoring systems, hire better-qualified staff, sustain ongoing training programs, and initiate evidence-based clinical protocols and quality improvement projects, with the goal and outcome of attracting more market share and increasing profits. [19–21] Financial distress may stem from exogenous factors, such as policy changes or the local economy. At the same time, it also may be attributable to internal or efficiency issues, such as inferior services or poor management. [19, 20] Given that activities to improve hospital quality and patient safety can entail substantial costs, it is presumed that hospitals facing more significant financial pressure from inadequate revenues will limit quality improvements as financial performance declines. [22] A record of hospital financial losses will likely reduce access to capital and raise borrowing costs, further hindering the facility. [23, 24] Previous studies support this expectation, demonstrating declines in hospital staffing, infrastructure investment and critical process of care measures when financial pressure mounts. [25, 26] Existing literature suggests that the lack of resources prevents net safety facilities from investing in care-improvement initiatives, leading to higher mortality and morbidity rates. [27, 28] These facilities have also been shown to provide costly and overpriced care due to inefficient systems and staffing, all of which have negatively impacted patient care and increased the length of hospital stay. [29–32]

Value-based payment initiatives are designed to provide a direct return on investment (ROI) for improved outcomes but often presume all facilities have comparable baseline financial resources to invest in quality improvement (QI) (QI). Installing QI can require significant upfront resources and usually requires already robust financial health to engage in such initiatives. [33, 34] Additionally, many VBP initiatives target specific patient groups and specific outcomes, raising concern about disparity in investment in QI among different inpatient populations. Like VBP, public reporting of quality data is intended to incentivize improvement by connecting consumer choice to quality. Public reporting can influence reputation and, in turn, affect patient perceptions, demand for hospital services, and market share. [35, 36] Despite general support for public reporting and pay-for-performance initiatives, critics worry that such efforts may harm safety-net providers struggling with lower reimbursement rates and higher costs associated with caring for populations with more significant medical complications and socioeconomic impediments [37–39]. There is concern that these initiatives negatively target safety-net hospitals with limited resources for quality improvement programs and infrastructure. [40, 41]

In this study, we aim to answer whether the quality and patient safety metrics are related to hospital financial performance by examining various measures of financial performance and multiple indicators of patient quality of care and patient safety. This study is unique in that we used principal component analysis to combine various measures into meaningful predictive models while creating composite and robust measures that are more discriminating in detecting differences in performance across hospitals for both our independent variable of financial health and our dependent variables measuring hospital quality and patient safety.

Literature review
When considering the financial health of hospital facilities, varying economic indicators measuring profitability, liquidity, and solvency represent significant markers of financial health; however, discerning financial health is complicated among hospitals, and it is difficult to rank the numerous indicators by importance or predictive power. Additionally, individual indicators do not capture all aspects of hospital financial health. Their order of priority needs to be clarified since varying studies cite different measures as the most effective indication of impending fiscal problems. [19, 42–44] Limitations of past studies include using financial data that focused only on specific populations (e.g., Medicare patients), the outsized influence of facilities at the extremes of financial performance, and the employment of gross metrics such as operating margin and total margin. [45–50] Using these limited approaches, several studies have found that poor hospital financial health may lead to increased adverse outcomes for some publicly reported effects and not others. The equivocal findings are challenging to interpret because hospital margins may be misleading indicators of financial health, and a negative margin or a net loss is not the sole predictor of financial distress. For instance, despite positive margins, some hospitals may have insufficient liquid assets to meet all current or future obligations surrounding quality improvement. Revenues might be underestimated because of the absence of nonoperating transfers, income from grants, loan forgiveness, or other exclusions from regular accounting reports. [51] Therefore, it is essential to consider a range of economic dimensions, including measures surrounding liquidity, financial leverage, and physical facilities. Broader-ranging financial data that better depict a hospital’s financial health are publicly reported and available at state and federal agencies. This work shows the value of looking beyond the limited measures of hospital financial health previously utilized.

Similarly, quality and safety measures included in previous studies have been limited based on the following:
The use of distal outcomes such as mortality.
Including only specific patient populations such as Medicare patients.
Including only special conditions, most commonly heart attack (AMI), congestive heart failure (CHF), and pneumonia (PN) (PN).

[14, 17, 18, 45, 49, 52] These three conditions (AMI, CHF, PN) are among the most common causes of hospitalizations for the US population, particularly the elderly. There is scientific evidence supporting associations between mortality and readmission for these conditions with specific clinical care processes, leading to the use of these indicators to measure the consistent quality of care for these conditions. [53] Many prior research articles have used these mortality and readmission indicators to proxy hospital quality. [18, 41, 52–56]

Several quality and safety indicators are now publicly reported, including CMS Hospital Compare VBP Total Performance Score (VBP-TPS) and a Five-Star hospital rating system (https://www.medicare.gov/hospitalcompare/Data/Hospital-overall-ratings-calculation.html). These measures, however, were designed for the specific purpose of payment reform and may be better for research purposes. The VBP-TPS includes cost efficiency and year-by-year quality improvement, which may introduce confounding by past financial performance. Cefalu et al. recently reported a principal component analysis across 25 hospital quality measures, concluding that four factors representing the patient experience of care, the selection process of care measures, and inpatient mortality demonstrated the multidimensionality of hospital quality. [57]

Methods \sPopulation
The study population included general medical/surgical hospitals in New York State (NYS) participating in the Centers for Medicare and Medicaid Services (CMS) inpatient prospective payment system. All facilities included in this study provided a broad enough range of services to ensure sufficient quality of care indicators and had financial data available in 2014. These requirements lead to the exclusion of specialty hospitals, federal hospitals, and some small hospitals providing limited services (e. g. critical care hospitals) (e. g. essential hospitals of care). When multiple hospitals were in the same network, ancillary facilities without independent financial information from a principal facility were excluded from the analysis. All general medical/surgical hospitals in NYS are nonprofit or government owned.

This study was approved by the NYS Department of Health (NYSDOH) Institutional Review Board (IRB) (IRB). Informed consent was not required for health services research of administrative health records. Patients should have been contacted.

Measurement of hospital quality of care and patient safety
A total of 46 indicators of quality of care and patient safety were incorporated into a composite measure, covering four domains: (1) inpatient quality, (2) patient safety, (3) process of care, and (4) patient experience of care. We call this measure the composite quality/safety performance score.

The inpatient quality domain included two Inpatient Quality Indicators (IQIs) developed by the Agency for Healthcare Research and Quality (AHRQ) and endorsed by the National Quality Forum (NQF): risk-adjusted heart failure mortality rate (IQI 16) and risk-adjusted pneumonia mortality rate (IQI 20). (IQI 20). The IQI mortality rates were obtained from the NYSDOH Open Data website and based on the NYS Statewide Planning and Research Cooperative System (SPARCS) inpatient discharge data for 2014 [accessioned March 27, 2018, IQI version 5.0, March 2015]. [58, 59]

The patient safety domain was assessed using 11 AHRQ Patient Safety Indicators (PSIs), also based on SPARCS data from 2014 obtained from the NYSDOH Open Data website [accessioned March 27, 2018, PSI version 6.0, September 2015].

[59] The domain encompassed six measures of perioperative and postoperative adverse events. These events included postoperative hip fracture (PSI 08), perioperative hemorrhage or hematoma (PSI 09), postoperative physiologic and metabolic derangements (PSI 10), postoperative respiratory failure (PSI 11), perioperative pulmonary embolism or deep vein thrombosis (PSI 12), postoperative wound dehiscence (PSI 14), pressure ulcers (PSI 03), iatrogenic pneumothorax (PSI 06), central venous catheter-related bloodstream infection (PSI 07), accidental puncture or laceration (PSI 15), and deaths among patients with low-mortality diagnoses (PSI 02). (PSI 02). These PSIs, except for PSI 10, are either NQF-endorsed or included in the NQF-endorsed composite PSI.

The process of care (also known as timely and effective strategies) domain was compiled from the CMS Hospital Inpatient Quality Reporting (IQR) Program indicators derived from chart reviews [accessioned March 27, 2018, HQA 2007, year of admission = 2014]. For each hospital, 21 processes of care indicators contributed to the calculation of the composite quality/safety performance score. The five processes of care categories include:
Emergency department throughput (six hands).
Preventive care (six indicators).
Surgical care improvement (six indicators).
Pneumonia care (two arrows).
Stroke care (one indicator) (one hand).

The Patient Experience of Care domain was assessed via the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Patient’s Perspectives of Care Survey, a nationally standardized publicly reported survey utilized for measuring patients’ perceptions of their hospital experience. [60] Eleven HCAHPS measures are publicly reported on the Hospital Compare website, including six composite topics, two individual items, and three global items. Blended topics include communication with doctors, communication with nurses, the responsiveness of hospital staff, pain management, communication about medications, and discharge information. Individual items include cleanliness and quietness of the hospital environment. In contrast, global items have overall hospital rating, willingness to recommend the hospital, and care transition—patient understanding of their care at discharge. The 11 measures included in this submission have been endorsed since 2006, and results have been tied to hospital pay for reporting since 2007 and used in pay for performance and VBP since 2012. [61] Survey response rates for hospitals in our analysis range from 10% to 52%. The varying and often low response rates between hospitals led us to perform a sensitivity analysis of our findings with and without the patient experience measures (accessioned March 27, 2018, hospital compare. Data was used from the measure start date of 04/01/2014 till 03/31/2015). [61]

In addition to the four-domain-based composite, several individual quality indicators were included to perform an analysis comparable with published literature. These included the 2014 CMS Value-Based Purchasing Total Performance Score (VBP-TPS), all-cause risk-adjusted 30-day readmission and 30-day mortality among adults, as well as risk-adjusted 30-day readmission and 30-day mortality for acute myocardial infarction (AMI), congestive heart failure (HF) and pneumonia (PN) (PN). The readmission measures, endorsed by the NQF, were obtained from the Hospital Inpatient Quality Reporting Program during the calendar year 2014 and are available on the Hospital Compare website (http://www.hospitalcompare.hhs.gov). NQF endorsements have included the consideration of condition-specific readmission and mortality measures since April 2012 (http://www.qualityforum.org/ProjectDescription.aspx?projectID=73619).

Measurement of hospital composite financial performance
We examined financial performance using data from CMS cost reports for the 2014 fiscal year. We generated a continuous composite financial performance score for each hospital based on a combination of economic measures.
[62] Thus, our analyses considered multiple measures of financial health, including operating profit or loss, net profit margin, return on total assets, cash flow margin, working capital, current ratio, days cash on hand, net asset position, equity financing, fixed asset financing, debt coverage, total debt ratio, long term debt ratio, salary ratio, total asset turnover, average operating margin and average total margin. These indicators were used to create a composite financial performance score. The CMS cost report data were obtained from the NYSDOH, although comparable data is publicly available from CMS. NYSDOH data were used due to ease of access.

Hospital characteristics and covariates
To describe the hospitals studied and to adjust for potential confounding that may influence the financial performance of hospitals, the following hospital characteristics were utilized: teaching status, bed count, the proportion of discharges with Medicare as a payer (Percent-Medicare), proportion of shots with Medicaid as a payer (Percent-Medicaid), and rural versus urban geography.

Consistent with other research, all hospitals were placed into one of three categories based on their response to the American Hospital Association (AHA) survey: major teaching hospitals (those that are members of the Council of Teaching Hospitals [COTH]), minor teaching hospitals (non-COTH members that had a medical school affiliation reported to the American Medical Association), and nonteaching hospitals (all other institutions) [63, 64]. Bed count assesses the number of short-term acute beds in the hospital, whether staffed or not, obtained from the AHA Annual Survey of Hospitals (Retrieved March 4, 2016; https://www.ahadataviewer.com/quickreport/). Annual Medicare caseload was defined as the proportion of Medicare discharges divided by the total number of shots, based on 2014 SPARCS data. Similarly, the yearly Medicaid caseload was defined as the proportion of Medicaid discharges divided by the total number of releases based on 2014 SPARCS data. A hospital was considered urban if it was located in a metropolitan statistical area and thought nonurban otherwise. This information was obtained from the AHA Annual Survey of Hospitals (Retrieved March 4, 2016; https://www.ahadataviewer.com/quickreport/).

Statistical analyses
Hospital-level composite quality/safety and financial performance scores were developed. For each composite score, principal components analysis was used to synthesize the indicators simultaneously, loading weights were calculated based on indicator variance, and scores were standardized using the SAS Factor Procedure with varimax rotation. The number of factors to retain was determined based on the Scree Plots. The controlled elements were used to calculate individual hospital composite scores by summing the personal hospital factor score weighted by the factor eigenvalue (variance explained) [65].

Several linear regression models were developed using the following permutations of dependent and independent variables, as well as with and without adjustment for percent Medicare and percent Medicaid, as follows:

In order to compare the composite quality/safety performance scores calculated as described above, the following published quality metrics were also modeled:
CMS Value-Based Purchasing Total Performance Score
Risk-adjusted 30-day readmission for all patients
Risk-adjusted 30-day readmission for AMI
Risk-adjusted 30-day readmission for CHF
Risk-adjusted 30-day readmission for PN
Risk-adjusted 30-day mortality for AMI
Risk-adjusted 30-day mortality for CHF
Risk-adjusted 30-day mortality for PN
Similarly, operating and total margins were modeled as independent financial variables to compare to the composite financial performance score.

All measures were standardized before regression analysis. Standardizing these coefficients allowed comparison of the relative importance of each coefficient in our regression models. [66] The strength of the coefficients based on standardized independent and dependent variables are internally comparable, and the strongest association is theoretically the one with the most significant total effect. [67] Model fit was assessed for influence and outliers in each model.

Regression tree models were developed as an alternative to the standardized beta weights using the Classification And Regression Trees (CART) methods.

[68] Regression trees consist of recursive partitions of data into subsets according to ranges of ordered values of ordinal covariates or subsets of values of categorical covariates, which are as homogenous as possible concerning the composite financial performance score. For all partitions, all observed covariates remain available even if they have been used earlier in the tree so that a covariate can reappear at several points in a tree. Unlike traditional linear regression, covariates with similar information are kept in the process and assessed for every partition. CART ranks all covariates based on their contribution to the improvement in homogeneity (even if it does not appear in the tree) (even if it does not appear in the tree). This measures how “important” each covariate is based on explanatory power and, in the case of correlated covariates, based on their ability to perform as a main splitting criterion. Building a regression tree requires a decision to stop partitioning the data. In this study, we stopped the trees when additional partitions did not improve homogeneity. Regression tree models for this study were developed using CART available in the Salford System’s Predictive Modeler v8.0. (https://www.salford-systems.com/products/cart)

Results
Of the 214 non-federal acute care hospitals in New York in 2014, 109 (51%) were included in the principal component analysis. Reasons for exclusion were specialty facilities without medical or surgical beds (29), critical access facilities (18), recent closure (2), and ancillary facilities without independent financial information from a principal facility (56). (56). The included hospitals accounted for 71% of inpatient discharges from non-veteran Affairs (VA) NYS Hospitals in 2014.

Composite financial performance score components
Principal component analysis of financial variables revealed seven factors with eigenvalues greater than one, accounting for 87 percent of the variance of the financial health subscale. The factors were interpreted as measuring profitability (38%), asset efficiency (13%), the absolute size of assets (11%), debt coverage (9%), capital structure (7%), uncompensated care or unutilized income (5%), and growth (4%) (Table 1). (Table 1). The standardized composite financial performance scores for the 109 hospitals ranged from -3.70 to 3.05, Interquartile range (IQR) from -0.45 to 0.38.

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Table 1. The proportion of variance explained by principal components analysis of financial indicators, NYS hospitals, 2014.
https://doi.org/10.1371/journal.pone.0219124.t001

Composite quality/patient safety performance components
Principal components analysis of quality variables revealed fourteen factors with eigenvalues greater than one, explaining 77 percent of the quality/safety subscales variance. Based on the analysis of scree plots, we narrowed the number of components to seven (Table 2), which explained 57 percent of the conflict and had a robust correlation (r = 0.91) with the 14-component summary score.

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Table 2. The proportion of variance explained by principal components analysis of hospital quality indicators, New York State, 2014.
https://doi.org/10.1371/journal.pone.0219124.t002

The first component, interpreted as a patient experience of care (23%), included all ten subscales derived from the HCAHPS survey. The second component, interpreted as timeliness in surgical care improvement (10%), had the process of care subscales predominantly related to reducing poor surgical outcomes, including cardiac, venous thromboembolism, and infections. The third component featured the timeliness of stroke care and other prophylactic therapies (7%). The following two pieces were related to emergency department (ED) process measures: factor four includes measures of ED delays following evaluation (5%), and factor five has standards of ED quality, including timeliness of pain control and assessment. The sixth component included patient safety indicators (4%), and the seventh included inpatient mortality (6%). Additional analysis conducted without the patient experience measures found similar components and proportion of variance explained (43% for five factors, data not shown). The standardized composite quality/safety performance scores for the 109 hospitals ranged from -4.45 to 1.86, with an interquartile range (IQR) from -0.59 to 0.76. One facility was identified as a low outlier for the standardized financial score (-3.70) and quality score (-4.45). (-4.45). This outlier was removed from the following regression analyses to provide conservative estimates of association.

Associations between hospital financial status and hospital quality of care
As measured using the composite financial performance score, a more robust hospital financial standing was positively associated with better quality of care and service delivery as measured by the composite quality/safety performance score. (Table 3) Additionally, strong hospital financial standing also was associated with the CMS Value-Based Purchasing Total Performance Score (VBP-TPS) (VBP-TPS). The composite financial performance score was negatively related to hospital-wide 30-day readmission and 30-day readmission for heart failure and pneumonia, along with 30-day mortality from acute myocardial infarction (Table 3). (Table 3).

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Table 3. Relationship between New York State hospital financial indicators and quality outcomes, without adjustment for percent medicare and percent Medicaid (model1) (model1).
https://doi.org/10.1371/journal.pone.0219124.t003

Overall, adjustment for Percent-Medicare coverage and Percent-Medicaid coverage attenuated the associations (Table 4). (Table 4). The composite quality/safety performance score, VBP-TPS and 30-day readmission for CHF remained statistically significant after adjustment for percent Medicaid and Medicare. The association between the composite financial performance score and a composite quality/safety performance score without patient experience measures was weaker (unadjusted for Medicare and Medicaid coverage: 0.169, p = 0.09; adjusted for Medicare and Medicaid coverage: 0.171, p = 0.12).

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Table 4. Relationship between New York State hospital financial indicators and quality outcomes, with adjustment for percent medicare and percent Medicaid (model2) (model2).
https://doi.org/10.1371/journal.pone.0219124.t004

The composite quality/safety performance score regressed against the composite financial performance score with adjustment for percent Medicare and percent Medicaid had the best fit of all models, with an R-square of 0.29, p<0.0001. The model correlating VBP-TPS with the composite score, including percent Medicare and percent Medicaid, had an R-square of 0.27, p<0.0001. The mixed quality score model regressed against the composite financial without adjustment for percent Medicare and percent Medicaid, and the R-square was 0.21, p<0.0001. Both operating margin (p = 0.02) and total margin (p = 0.02) demonstrated a statistically significant association with the composite quality score when the model was not adjusted for percent Medicare and percent Medicaid. With adjustment for percent Medicare and percent Medicaid, operating margin and total margin were not significantly associated with quality metrics, except for one instance with operating margin related to total VBP-TPS (Table 4). (Table 4). None of the financial measures significantly correlated with adjusted 30-day mortality for pneumonia and congestive heart failure (data not shown) (data not shown). Decision-tree analyses The results from regression tree models supported the findings from the traditional weighted linear regression models. The composite financial performance scores outperformed the total and operating margin in predicting the quality of care in NYS acute care hospitals. The combined financial performance score contributed to a regression tree model's most considerable reduction in mean squares. The operating revenue margins contributed with a decrease of less than half that of the composite financial performance score, and the total margins were approximately 10% of that of the composite financial score. This ranking remained similar when other predictors were added to the regression tree model. As more covariates were introduced in the regression tree model, the total margin and the operating revenue margin ended up as the weakest predictors of quality of care. However, the composite financial performance score was similar to the entire margin and outperformed the operating margin when predicting the composite quality/safety performance score. The combined financial performance score contributed an 83% reduction in mean squares compared to the total margin. Both measures outperformed the operating margin, contributing only a 32% reduction in mean squares. All three measurements showed weak associations with the readmission measure. Discussion Our analyses found strong evidence, as hypothesized, that financially stable hospitals have better patient experience, lower readmission rates, and show proof of decreased risk of adverse patient quality and safety outcomes for both medical and surgical patients. Hospitals that are better off financially can maintain highly reliable systems and provide ongoing resources for quality improvement, as measured predominantly by patient experiences and better performance on the process of care initiatives. At the same time, financially distressed facilities struggle in these categories. These superior outcomes in financially stable hospitals persisted after adjusting for public payer caseload and hospital characteristics, suggesting that the qualities of financially well-off facilities lead to excellent medical and surgical care. A few studies have suggested a limited association between improved hospital financial performance, improved quality of care, and patient safety in specific scenarios. [14, 17, 18, 48, 52] We improve on previous cross-sectional snapshots by developing financial and quality/safety composite measures that have enhanced predictive validity. The results suggest that money does matter. In studying this relationship, we also recognize that measurement matters. The strength of the relationship between finances and quality in this report varies across the indicators used in these regression analyses. Financial health and quality/patient safety are complicated concepts that can be measured along many dimensions. Challenges arise when attempting to find elusive indicators for abstract, broad, and complex measures, such as the financial health of organizations or the quality of care of health facilities. Financial health can be measured considering capital structure, cost, profitability, liquidity and efficiency; patient safety/quality care can range from hospital regulations adherence to patient perspectives on care. [62] The findings from previous studies on this topic are equivocal and have varying limitations [14, 25, 45–47, 50, 69–75]. This report attempts to overcome prior limitations related to measurement by integrating a broader spectrum of existing data routinely collected. All measures were standardized before regression analysis. With standardization, the interpretation of the regression coefficients is the standard deviation change in the dependent variables per average deviation change in the independent variable. This technique preserves internal validity, but the standardized coefficients are only generalizable to other populations with similar variable distributions. Standardized coefficients also facilitate comparisons between equations using the same independent variable set. [66, 67] When comparing the various financial indicators and utilizing the model without adjustment for percent Medicare and Medicaid, the composite quality/safety performance score had the most superior strength of the effect, followed by VBP-TPS and various subsets of 30-day readmissions. This model also significantly associated thirty-day mortality for acute myocardial infarction with financial health. However, none of the other mortality indicators were significant in either model. The same pattern held when the percent Medicare and percent Medicaid were included in the model, though strength was attenuated and fewer associations were significant. When hospitals are compared to one another based on patient outcomes, concerns inevitably arise about risk-adjustment and statistical heterogeneity due to small numerators. To improve measurement, intermediate process and performance metrics have been added to the measure sets, raising the concern of whether these measures appropriately inform meaningful health outcomes. [76] While there is modest evidence connecting many surrogate endpoints, such as risk-factor control or care processes, these metrics may be chosen because they are easy to access and measure rather than meaningful, patient-centered outcomes [76, 77]. With payment at stake, clinicians and health organizations may feel compelled to engage in gaming, over-testing and overtreatment, or devoting disproportionate effort to patients that improve these surrogate endpoints rather than focusing on those at highest risk [76, 77]. Furthermore, the availability and influence of these markers interfere with opportunities to establish more thoughtful interventions and individualized approaches to clinical complications such as social determinants and multimorbidity. [76, 78–82] We attempted to address these concerns by creating and using global risk measures representing financial health and quality of care. Decisions for entire hospitals and health systems often rely on hospital-level indicators. International standards are more robust and preferable to individual risk factors, as they are more likely to indicate highly reliable organizations by reducing the influence of gaming and interventions focused on improving individual metrics [76, 83, 84]. Variables chosen for adjustment in our models are well chronicled in the literature. It is well documented that greater dependence on government payers, such as Medicare and Medicaid, is associated with a higher probability of financial distress because these payers typically do not pay the average cost of care. [85–91] The analyses were adjusted for teaching hospital status as prior studies associated teaching hospitals with lower financial performance, considering they often support more labor-intensive staff and offer a wide array of costly medical services. The sheer size of a hospital, measured by the number of beds, allows a hospital to withstand costly outliers, which could more likely have adverse effects on smaller facilities. [92] The mixture of operational and market factors influencing the financial condition of hospitals differently in urban versus non-urban areas is well documented. [19] Non-urban hospitals tend to be smaller and offer fewer services than urban hospitals. Finally, the outcome-based metrics used for the quality composite score were based on published risk-adjustment methodology. [93, 94] Policy implications Federal and State policy also matters as deficits in the quality of care can be systemic, requiring systems-level modifications to produce the desired changes and results. As policymakers consider action to achieve the triple aim, the interrelatedness of cutting healthcare costs and achieving quality needs to be addressed, mainly as it affects the ability of fiscally distressed facilities serving vulnerable patients to engage in quality improvement. This study has policy implications for the millions of patients who gained Medicaid coverage beginning in 2014 from the Affordable Care Act and for the future of the Medicaid program. The attenuation of the association observed here when controlling for public payer are consistent with previous studies that found that hospitals with high Medicaid case-mix had worse quality of care than other hospitals. [56, 95–100] Research on nursing homes also suggests a link between lower Medicaid reimbursement levels and lower rates. [101–104] Despite this association, robust evidence has been published suggesting that Medicaid has a positive impact on access to care, financial security, and self-reported health. [105, 106] At the same time, under the veil of deficit reduction, expected cutbacks could lead to reduced access to high-quality care. Our findings suggest that any cost-cutting efforts by Medicare, Medicaid, and private payers must be carefully designed and managed to ensure patient safety and patient-centered care are not compromised. Since our analysis was performed with hospital-level data, we cannot examine variations in individual patient care within each hospital. The patient-level research would be relevant if the disparity in quality that arises from financial distress contributed to differences in outcomes that have been observed for vulnerable patient populations, including older and poorer patients, those covered by Medicaid or Medicare, those with complex comorbidities, and medically disadvantaged groups such as racial and gender minorities. [107, 108] [109–114] Evaluating performance on the hospital level of analysis, however, is relevant since systems and policy decisions are most commonly determined either at the hospital, state, or federal level [57]. Further, there is no current source of individual-level data on patient experience of care, which the analysis presented here confirms accounts for the most variability across the hospital healthcare system. The results presented here represent the State of affairs in New York before launching the Delivery System Reform Incentive Payment (DSRIP) Program funded by the Section 1115 Demonstration Waiver [115]. Repeating these analyses at the end of New York's five-year demonstration will identify if systemic changes have reduced the chasms in quality of care or made them more profound. [116] Our findings support the notion that hospitals under more significant financial distress have less favorable patient experience of care, higher readmission rates, and increased risk of adverse patient quality and safety outcomes for both medical and surgical patients. These substandard outcomes persisted after adjusting for public payer caseload and hospital characteristics in financially distressed hospitals. This suggests that poorer facilities' underlying qualities can lead to inferior medical and surgical care and a bad patient experience. This study provides composite measures that optimized the estimated correlation between financial status and quality/patient safety outcomes. These findings suggest that it is imperative to address economic disparities when incentivizing health care quality through value-based purchasing to ensure financial stability and quality of care in safety net fa QUESTION
(Need someone who can complete this in 24 hours)

Answer the following questions using the Week 6 Correlations Exercises SPSS Output provided in this week’s Learning Resources.

What is the strongest correlation in the matrix? (Provide the correlation value and the names of variables)
What is the weakest correlation in the matrix? (Provide the correlation value and the names of variables)
How many original correlations are present on the matrix?
What does the entry of 1.00 indicate on the diagonal of the matrix?
Indicate the strength and direction of the relationship between body mass index (BMI) and physical health component subscale.
Which variable is most strongly correlated with BMI? What is the correlational coefficient? What is the sample size for this relationship?
What is the mean and standard deviation for BMI and doctor visits?
What is the mean and standard deviation for weight and BMI?
Describe the strength and direction of the relationship between weight and BMI.
Describe the scatterplot. What information does it provide to a researcher?