How Does Patient Safety Save Hospitals Money

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Open Access

Peer-reviewed

Enquiry Article

Correlation between hospital finances and quality and condom of patient care

• Dean D. Akinleye,
• Louise-Anne McNutt,
• Victoria Lazariu,
• Colleen C. McLaughlin

x

• Published: August sixteen, 2019
• https://doi.org/10.1371/journal.pone.0219124

Figures

Abstruse

Background

Hospitals nether financial force per unit area may struggle to maintain quality and patient condom and have worse patient outcomes relative to well-resourced hospitals. Poor predictive validity may explain why previous studies on the clan between finances and quality/safe have been equivocal. This manuscript employs principal component analysis to produce robust measures of both financial condition and quality/safety of care, to assess our a priori hypothesis: hospital financial performance is associated with the provision of quality care, equally measured by quality and rubber processes, patient outcomes, and patient centered intendance.

Methods

This 2014 cross-sectional report investigated infirmary financial condition and hospital quality and safety at acute care hospitals. The hospital financial information from the Centers for Medicare and Medicaid Services (CMS) cost report were used to develop a composite financial performance score using primary component analysis. Hospital quality and patient safe were measured with a composite quality/safety operation score derived from principal component analysis, utilizing a range of established quality and safety indicators including: chance-standardized inpatient mortality, 30-day mortality, 30-24-hour interval readmissions for select conditions, patient safety indicators from inpatient admissions, process of care nautical chart reviews, CMS value-based purchasing total operation score and patient experience of care surveys. The correlation between the blended financial performance score and the blended quality/safety performance score was calculated using linear regression adjusting for hospital characteristics.

Results

Among the 108 New York State acute care facilities for which information were bachelor, there is a clear human relationship between hospital financial performance and hospital quality/safety performance score (standardized correlation coefficient 0.34, p<0.001). The blended fiscal operation score is too positively associated with the CMS Value Based Purchasing Full Performance Score (standardized correlation coefficient 0.277, p = 0.002); while information technology is negatively associated with thirty twenty-four hours readmission for all outcomes (standardized correlation coefficient -0.236, p = 0.013), 30-day readmission for congestive heart failure (standardized correlation coefficient -0.23, p = 0.018), xxx day readmission for pneumonia (standardized correlation coefficient -0.209, p = 0.033), and a decrease in 30-day mortality for acute myocardial infarction (standardized correlation coefficient -0.211, p = 0.027). Used alone, operating margin and total margin are poor predictors of quality and rubber outcomes.

Conclusions

Strong financial performance is associated with improved patient reported experience of care, the strongest component distinguishing quality and safe. These findings suggest that financially stable hospitals are better able to maintain highly reliable systems and provide ongoing resources for quality improvement.

Citation: Akinleye DD, McNutt L-A, Lazariu Five, McLaughlin CC (2019) Correlation between hospital finances and quality and prophylactic of patient care. PLoS ONE 14(8): e0219124. https://doi.org/10.1371/journal.pone.0219124

Editor: Takeru Abe, Yokohama City University, Japan

Received: September xix, 2018; Accepted: June 17, 2019; Published: August 16, 2019

Copyright: © 2019 Akinleye et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the written report are available from New York Country health data NY data website, represented past the post-obit documents: "All Payer Inpatient Quality Indicators (IQI) past Hospital (SPARCS): Beginning 2009" every bit well as "All Payer Patient Safety Indicators (PSI) by Hospital: Start 2009", https://healthdata.ny.gov/. The CMS price report data was obtained from the NYSDOH, although comparable data is publicly bachelor from CMS. NYSDOH information were used due to ease of access: https://data.medicare.gov/data/hospital-compare. The authors did not take special admission privileges.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have alleged that no competing interests exist.

Background/Introduction

Is the financial condition of a hospital related to the quality and safety of care delivered? While this uncomplicated and straightforward question has attracted considerable attention, it has been remarkably hard to answer. Efforts to command the loftier costs of wellness care in the U.s.a. presuppose hospitals can do more with less. Hospitals face considerable pressure to lower costs while maintaining quality outcomes [i, 2]. Initiatives to financially incentivize quality, such equally public reporting and value-based payment (VBP), will succeed in improving population health for all but if they are designed to account for the complicated relationship between quality and facility fiscal stability. Otherwise, these programs run the take a chance of perpetuating the "rich get richer" history of the American health intendance organization and will continue to penalize safety net hospitals and their underserved populations[iii]

Prior literature suggests that some aspects of patient care may be compromised equally a hospital'southward financial condition declines [4–11]. Studies straight examining the correlation between financial status and quality and safety of patient care, however, accept been equivocal and the findings uncertain. Lack of clear associations may exist due to the poor predictive validity of the measures of finances and of quality. When considering financial operation, many financial distress models relied on specific indicators of stress, including bankruptcy and closure information, which are easier to obtain just do not stand for the range of fiscal health [12]. Other studies used merely narrow measures of hospital financial performance (eastward.g., operating margin), which do non capture the full range of revenue potentially bachelor for investment in quality improvements [13]. Concerning quality performance, studies have generally focused on specific outcomes, such as bloodshed, or infirmary readmission from weather such as pneumonia, center failure, or myocardial infarction [14–17]. The expansion of public reporting past the Centers for Medicare and Medicaid Services (CMS), every bit part of VBP, has widened the pool of measures bachelor for quality analysis.[14, 18].

While most previous studies have used limited approaches in describing the abstruse measures representing hospital financial health and quality of care, this paper considers an entire profile of fiscal characteristics and patient quality and safety measures. Nosotros hypothesize that robust measurement of these financial measures and quality and safety measures meliorate the likelihood of observing the relationship betwixt poor financial health and inferior hospital quality of care and patient safety. We attempted to determine whether a blended financial 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 summarization of several studies that have assessed problems relevant to the ones we are examining. This is followed by a give-and-take of study methods and measures and concludes with the presentation of results and policy implications.

Conceptual framework

The hypothesis which posits that financial status and quality/prophylactic are linked has strong construct validity. Profitable hospitals with stiff cash flows can pay off debt quicker, which allows them to further invest in capital at lower costs than cash-strapped hospitals. With more than capital, these facilities can make sizeable investments in clinical and administrative data technology and monitoring systems, rent better qualified staff, sustain ongoing training programs, initiate evidence-based clinical protocols and quality improvement projects, with the goal and result of alluring more market share and increasing profits.[19–21] Financial distress may stalk from exogenous factors, such as policy changes or local economy, while it likewise may exist attributable to internal or efficiency issues, such as inferior services or poor management.[19, 20] Given that activities to better hospital quality and patient safety can entail substantial costs, it is presumed that hospitals facing greater financial pressure level from inadequate revenues volition limit quality improvements as financial performance declines.[22] A tape of infirmary financial losses likely will as well reduce access to capital and raise the costs of borrowing, 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 rubber net facilities from investing in care-improvement initiatives, which can lead to higher rates of mortality and morbidity.[27, 28] These facilities have besides been shown to provide costly and overpriced care due to inefficient systems and staffing, all of which accept been shown to negatively touch patient care and increase length of hospital stay.[29–32]

Value-based payment initiatives are designed to provide directly return on investment (ROI) for improved outcomes, merely often presumes all facilities have comparable baseline financial resources to invest in quality improvement (QI). Actions to install QI can require pregnant upfront resources, and often requires already having robust financial wellness to appoint in such initiatives.[33, 34] Additionally, many VBP initiatives target specific patient groups and specific outcomes, raising business organization about disparity in investment in QI amidst different inpatient populations. Similar VBP, public reporting of quality data is intended to incentivize improvement through connecting consumer option to quality. Public reporting has the potential to influence reputation and, in turn, affect patient perceptions, demand for hospital services, and market share.[35, 36] Despite full general support for public reporting and pay for performance initiatives, critics worry that such efforts may have a deleterious effect on condom-net providers struggling with lower reimbursement rates and higher costs associated with caring for populations with greater 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 written report, nosotros aim to answer the question of whether quality and patient safety metrics are related to infirmary financial functioning by examining various measures of financial performance and multiple indicators of patient quality of care and patient rubber. This study is unique in that we used principal component analysis to combine multiple measures into meaningful predictive models, while creating composite and robust measures that are more than discriminating in detecting differences in performance across hospitals for both our contained variable of financial health and our dependent variables measuring hospital quality and patient safety.

Literature review

When considering the fiscal wellness of infirmary facilities, varying fiscal indicators measuring profitability, liquidity, and solvency represent significant markers of financial health; still, discerning financial wellness is complicated among hospitals and information technology is difficult to rank the numerous indicators by importance or predictive power. Additionally, individual indicators practise not necessarily capture all aspects of hospital financial health, and their social club of importance is unclear since varying studies cite unlike measures as being the well-nigh effective indication of impending fiscal problems. [xix, 42–44] Limitations of by studies include utilization of financial data that focused merely on specific populations (e.yard. Medicare patients), the outsized influence of facilities at the extremes of financial performance, and the employment of gross metrics such every bit operating margin and full margin. [45–l] Using these limited approaches, several studies accept found that poor hospital financial health may lead to increased negative outcomes for some publicly reported outcomes and not others. The equivocal findings are difficult to interpret because hospital margins may be misleading indicators of financial health, and negative margin or net loss are not the sole predictors of financial distress. For case, despite positive margins, some hospitals may have insufficient liquid assets to meet all electric 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 typical accounting reports.[51] Therefore, information technology is of import to consider a range of financial dimensions including measures surrounding liquidity, fiscal leverage, and physical facilities. Wider ranging financial data that ameliorate depicts a hospitals financial health are publicly reported and available at land and federal agencies. This work shows the value of looking beyond the express measures of hospital financial health previously utilized.

Similarly, quality and safety measures included in previous studies take been express based on 1) use of distal outcomes such every bit mortality; 2) including merely specific patient populations such as Medicare patients; and iii), including only select conditions, well-nigh normally heart assail (AMI), congestive heart failure (CHF), and pneumonia (PN).[14, 17, 18, 45, 49, 52] These three weather (AMI, CHF, PN) are among the most common causes of hospitalizations for the United states of america population overall, particularly the elderly. At that place is scientific evidence supporting associations between mortality and readmission for these atmospheric condition with specific clinical intendance processes, leading to use of these indicators to measure provision of consistent quality of care for these conditions.[53] These mortality and readmission indicators accept been used equally proxies for hospital quality in many prior inquiry articles.[18, 41, 52–56]

Several quality and rubber indicators are now publicly reported, including CMS Hospital Compare VBP Total Functioning Score (VBP-TPS) and a V-Star infirmary rating organisation (https://www.medicare.gov/hospitalcompare/Data/Hospital-overall-ratings-calculation.html). These measures, notwithstanding, were designed for the specific purpose of payment reform and may non be platonic for research purposes. The VBP-TPS includes cost efficiency and year-past-twelvemonth quality improvement, which may introduce confounding by past financial functioning. Cefalu et al. recently reported use of primary component analysis across 25 hospital quality measures, concluding that iv factors representing patient feel of intendance, select process of care measures, and inpatient mortality demonstrated the multidimensionality of hospital quality. [57]

Methods

Population

The study population included general medical/surgical hospitals in New York State (NYS) that participated in the Centers for Medicare and Medicaid Services (CMS) inpatient prospective payment system. All facilities included in this study provided a broad plenty range of services to ensure availability of sufficient quality of care indicators and had financial data bachelor in 2014. These requirements lead to the exclusion of specialty hospitals, federal hospitals, and some small hospitals providing limited services (e. k. critical intendance hospitals). In the state of affairs where there were multiple hospitals in the aforementioned network, coincident facilities without independent financial data from a principal facility were excluded from the assay. All full general medical/surgical hospitals in NYS are nonprofit or government endemic.

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

Measurement of infirmary quality of care and patient rubber

A total of 46 indicators of quality of care and patient prophylactic were incorporated into a composite measure, covering iv domains: (i) inpatient quality, (2) patient safety, (3) process of care, and (4) patient feel of care. We call this measure the composite quality/safety performance score.

The inpatient quality domain included two Inpatient Quality Indicators (IQIs) developed past the Agency for Healthcare Research and Quality (AHRQ) and endorsed by the National Quality Forum (NQF): risk adjusted middle failure mortality rate (IQI 16) and risk adapted pneumonia bloodshed rate (IQI 20). The IQI bloodshed 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 v.0, March 2015].[58, 59]

The patient rubber 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 level ulcers (PSI 03), iatrogenic pneumothorax (PSI 06), key venous catheter-related bloodstream infection (PSI 07), accidental puncture or laceration (PSI fifteen), and deaths amongst patients with low-bloodshed diagnoses (PSI 02). These PSIs, except for PSI 10, are either NQF endorsed or included in the NQF endorsed composite PSI.

The process of intendance (also known equally timely and effective strategies) domain was compiled from the CMS Infirmary Inpatient Quality Reporting (IQR) Programme indicators derived from chart reviews [accessioned March 27, 2018, HQA 2007, year of admission = 2014]. For each hospital, 21 procedure of intendance indicators contributed to the calculation of the blended quality/safety operation score. The 5 process of care categories include: emergency department throughput (six indicators), preventive intendance (6 indicators), surgical care improvement (vi indicators), pneumonia care (ii indicators), and stroke care (one indicator).

The Patient Experience of Care domain was assessed via the Infirmary 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 measures on the Hospital Compare website, including half dozen composite topics, two private items, and three global items. Composite topics include advice with doctors, communication with nurses, responsiveness of hospital staff, hurting direction, communication about medications, and discharge information. Individual items include cleanliness and quietness of the infirmary environment, while global items include overall rating of the hospital, willingness to recommend the hospital, and care transition—patient understanding of their care at belch. The xi 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 ten% to 52%. The varying and often depression response rates between hospitals led us to perform a sensitivity analysis of our findings with and without the patient feel measures (accessioned March 27, 2018, hospital compare. Data was used from the mensurate showtime engagement of 04/01/2014 till 03/31/2015).[61]

In addition to the iv-domain based composite, several individual quality indicators were included in the analysis with a view to performing analysis comparable with published literature. These included the 2014 CMS Value Based Purchasing Total Performance Score (VBP-TPS), all-cause take chances-adapted 30-mean solar day readmission and xxx-day bloodshed amidst adults, likewise as risk-adjusted 30-mean solar day readmission and thirty-day bloodshed for astute myocardial infarction (AMI), congestive centre failure (HF) and pneumonia (PN). The readmission measures, endorsed by the NQF, were obtained from the Hospital Inpatient Quality Reporting Program during calendar yr 2014, and are available on the Hospital Compare website (http://world wide web.hospitalcompare.hhs.gov). NQF endorsements have included the consideration of status-specific readmission and mortality measures since April 2012 (http://world wide web.qualityforum.org/ProjectDescription.aspx?projectID=73619).

Measurement of hospital composite financial performance

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

Hospital characteristics and covariates

To describe the hospitals studied and to adjust for potential confounding that may influence the financial functioning of hospitals, the following hospital characteristics were utilized: teaching condition, bed count, proportion of discharges with Medicare as a payer (Percent-Medicare), proportion of discharges 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 Infirmary Association (AHA) survey: major didactics hospitals (those that are members of the Quango of Teaching Hospitals [COTH]), pocket-sized educational activity 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 curt-term acute beds in the hospital, whether staffed or not, obtained from the AHA Annual Survey of Hospitals (Retrieved March four^thursday, 2016; https://www.ahadataviewer.com/quickreport/). Annual Medicare caseload was divers as the proportion of Medicare discharges divided by the full number of discharges, based on 2014 SPARCS data. Similarly, annual Medicaid caseload was defined as the proportion of Medicaid discharges divided by the total number of discharges based on 2014 SPARCS data. A hospital was considered urban if it was located in a metropolitan statistical surface area considered nonurban otherwise. This information was obtained from the AHA Annual Survey of Hospitals (Retrieved March 4^thursday, 2016; https://world wide web.ahadataviewer.com/quickreport/).

Statistical analyses

Hospital level composite quality/safe performance scores and composite financial performance scores were developed. For each blended score, primary components analysis was used to synthesize the indicators simultaneously, loading weights were calculated based on indicator variance, and scores were standardized using the SAS Cistron Procedure with varimax rotation. The number of factors to retain were determined based on the Scree Plots. The retained factors were used to calculate private hospital composite scores past summing the individual hospital cistron score weighted by the factor eigenvalue (variance explained) [65].

Several linear regression models were adult using the following permutations of dependent and contained variables, too as with and without adjustment for percent Medicare and per centum Medicaid, as follows:

In social club to compare the composite quality/safety performance scores calculated as described above, the following published quality metrics were too modeled: (i) CMS Value Based Purchasing Total Performance Score, (2) risk adjusted 30 day readmission for all patients, (iii) take a chance adapted 30 day readmission for AMI, (4) chance adjusted 30 solar day readmission for CHF, (five) risk adapted 30 solar day readmission for PN, (6) risk adjusted 30 24-hour interval mortality for AMI, and (7) risk adjusted xxx day mortality for CHF, (8) run a risk adjusted thirty solar day mortality for PN. Similarly, operating margin and total margin were modeled as contained financial variables to compare to the composite fiscal operation score.

All measures were standardized prior to regression analysis. Standardizing these coefficients immune 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 i with the greatest total effect.[67] Model fit was assessed for influence and outliers in each model.

Every bit an alternative to the standardized beta weights, regression tree models were adult using the Nomenclature And Regression Trees (CART) methods.[68] Regression trees consist of recursive partitions of data into subsets co-ordinate to ranges of ordered values of ordinal covariates or to subsets of values of categorical covariates which are as homogenous every bit possible with respect to the blended financial performance score. For all partitions, all observed covariates remain available even if they have been used earlier in the tree, so it is possible for a covariate to reappear at several points in a tree. Unlike traditional linear regression, covariates with similar data are kept in the process and assessed for every sectionalisation. CART ranks all covariates based on their contribution to the comeback in homogeneity (even if it does non appear in the tree). This is a measure out of how "important" each covariate is based on explanatory ability and, in the case of correlated covariates, based on their ability to perform as main splitting criterion. The process of building a regression tree requires a decision to stop partitioning the data. In this study, we stopped the copse when boosted partitions did non improve homogeneity. Regression tree models for this study were developed using CART bachelor in the Salford System'southward Predictive Modeler v8.0. (https://world wide web.salford-systems.com/products/cart)

Results

Of the 214 non-federal astute 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), disquisitional access facilities (xviii), recent closure (ii), and ancillary facilities without independent fiscal information from a principal facility (56). The included hospitals account for 71% of inpatient discharges from non-Veteran's Affairs (VA) NYS Hospitals in 2014.

Blended financial performance score components

Primary component analysis of fiscal variables revealed seven factors with eigenvalues greater than one, accounting for 87 percent of the variance of the fiscal health subscale. The factors were interpreted every bit measuring profitability (38%), asset efficiency (13%), absolute size of avails (11%), debt coverage (ix%), uppercase structure (7%), uncompensated care or unutilized income (5%), and growth (iv%) (Tabular array 1). The standardized composite fiscal performance scores for the 109 hospitals ranged from -iii.70 to 3.05, Interquartile range (IQR) -0.45 to 0.38.

Composite quality/patient safety performance components

Principal components analysis of quality variables revealed fourteen factors with eigenvalues greater than one, explaining a total of 77 percent of the variance of the quality/safe subscales. Based on analysis of scree plots, nosotros narrowed the number of components to seven (Table 2), which explained 57 percent of the variance and had a very stiff correlation (r = 0.91) with the fourteen-component summary score.

The get-go component, interpreted equally patient experience of care (23%), included all ten subscales derived from the HCAHPS survey. The second component, interpreted every bit timeliness in surgical care improvement (ten%), included process of intendance subscales predominantly related to reducing poor surgical outcomes including cardiac, venous thromboembolism, and infections. The third component featured timeliness of stroke care and other prophylactic therapies (seven%). The side by side two components were both related to emergency section (ED) process measures: factor iv includes measures of ED delays following evaluation (5%) and cistron five includes measures of ED quality, including timeliness of pain control and of evaluation. The sixth component included patient safety indicators (4%) and the 7th included inpatient mortality (6%). Additional analysis conducted without the patient experience measures found similar components and proportion of variance explained (43% for 5 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 equally a depression outlier for both the standardized financial score (-iii.seventy) and quality score (-4.45). To provide conservative estimates of association, this outlier was removed from the following regression analyses.

Associations between hospital fiscal status and hospital quality of care

Stronger hospital financial standing, as measured using the composite financial performance score, was positively associated with better quality of intendance and service delivery as measured by the composite quality/prophylactic performance score. (Tabular array 3) Additionally, potent hospital financial standing as well was associated with the CMS Value Based Purchasing Total Performance Score (VBP-TPS). The composite financial performance score was negatively associated with hospital wide 30-twenty-four hour period readmission and 30-day readmission for heart failure and pneumonia, along with 30-day mortality from acute myocardial infarction (Table iii).

Overall, adjustment for Percent-Medicare coverage and Percent-Medicaid coverage attenuated the associations (Table four). The composite quality/safety performance score, VBP-TPS and 30-day readmission for CHF remained statistically pregnant later on adjustment for percent Medicaid and Medicare. The association between the blended fiscal operation score and a blended quality/safety performance score without patient feel 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).

The composite quality/safe 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-foursquare of 0.29, p<0.0001. The model correlating VBP-TPS with the composite score including per centum Medicare and pct Medicaid had an R-square of 0.27, p<0.0001. In the model of the composite quality score regressed against the blended financial without aligning for pct Medicare and per centum Medicaid the R-foursquare was 0.21, p<0.0001.

Both operating margin (p = 0.02) and full margin (p = 0.02) demonstrated a statistically pregnant association with the composite quality score when the model was not adapted for pct Medicare and pct Medicaid. With adjustment for pct Medicare and per centum Medicaid, operating margin and total margin were not significantly associated with whatsoever of the quality metrics, except for one instance with operating margin associated with total VBP-TPS (Tabular array iv). None of the financial measures used demonstrated a meaning correlation with adjusted 30-twenty-four hour period mortality for pneumonia and for congestive heart failure (data not shown).

Decision-tree analyses

The results from regression tree models supported the findings from the traditional weighted linear regression models. The composite fiscal performance scores outperformed the total margin and the operating margin in predicting quality of care in NYS acute care hospitals. The blended financial functioning score contributed with the largest reduction in mean squares in a regression tree model. The operating revenue margins contributed with a reduction less than half that of the composite financial operation score and the total margins was approximately x% that of the composite fiscal 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 concluded upwardly as the weakest predictors of quality of care. Still, the composite fiscal performance score had a similar performance as the total margin and clearly outperformed the operating margin when predicting the composite quality/safety operation score. The composite financial performance score contributed 83% reduction in hateful squares compared to full margin and both measures clearly outperformed the operating margin which only contributed 32% reduction in hateful squares. All 3 measures showed weak associations with the readmission measure out.

Discussion

Our analyses constitute strong show, every bit hypothesized, that financially stable hospitals take better patient experience, lower readmission rates, and show evidence of decreased take chances of adverse patient quality and safety outcomes for both medical and surgical patients. Hospitals that are better off financially tin maintain highly reliable systems and provide ongoing resources for quality improvement, every bit measured predominantly by patient experiences and meliorate functioning on process of care initiatives, while financially distressed facilities struggle in these categories. These superior outcomes in financially stable hospitals persisted afterward adjusting for public payer caseload and hospital characteristics, suggesting that underlying qualities of financially well-off facilities lead to medical and surgical care that is superior. A pocket-size number of studies accept suggested a express association between improved hospital financial performance and improved quality of care and patient safe in specific scenarios.[fourteen, 17, eighteen, 48, 52] We improve on previous cross-exclusive snapshots past developing financial and quality/safety blended measures that have improved predictive validity. The results advise that money does matter.

In studying this human relationship, nosotros also recognize that measurement matters. The strength of the human relationship between finances and quality in this report varies beyond the indicators used in these regression analyses. Fiscal wellness and quality/patient safety are complicated concepts that tin be measured along many dimensions. Challenges arise when attempting to find elusive indicators for abstract, wide, and complicated measures, such every bit the fiscal health of organizations and/or quality of intendance of health facilities. Financial health can be measured considering capital construction, price, profitability, liquidity and efficiency; while patient safe/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, l, 69–75]. This report attempts to overcome prior limitations related to measurement by integrating a broader spectrum of existing information routinely nerveless.

All measures were standardized prior to regression assay. With standardization, the interpretation of the regression coefficients is the standard deviation alter in the dependent variables per standard deviation change in independent variable. This technique preserves internal validity, but the standardized coefficients are simply generalizable to other populations with similar variable distributions. Standardized coefficients likewise facilitate comparison between equations that utilize the same independent variable set.[66, 67] When comparing the diverse financial indicators and utilizing the model without adjustment for percent Medicare and Medicaid, the composite quality/safety performance score had the largest strength of effect followed by VBP-TPS and so various subsets of xxx mean solar day readmissions. Thirty-day bloodshed for acute myocardial infarction was also found to be significantly associated with financial health using this model, however none of the other indicators of mortality were significant in either model. The same design held when percent Medicare and percent Medicaid were included in the model, though strength was attenuated and fewer associations were significant.

When hospitals are compared to i another based on patient outcomes, concerns inevitably arise about gamble-adjustment and statistical heterogeneity due to small numerators. To better measurement, intermediate process and operation metrics have been added to the measure sets, raising business organization of whether these measures appropriately inform meaningful health outcomes. [76] While there is minor testify connecting many surrogate endpoints, such as chance-factor control or care processes, these metrics may be called because they are like shooting fish in a barrel to admission and measure out, rather than being meaningful, patient-centered outcomes [76, 77]. With payment at stake, clinicians and health organizations may feel compelled to engage in gaming, in over-testing and overtreatment, or in devoting disproportionate effort to patients that meliorate these surrogate endpoints rather than focusing on those at highest risk [76, 77]. Furthermore, the availability and influence of these markers interferes with opportunities to establish more thoughtful interventions and individualized approaches to clinical complications such equally social determinants and multimorbidity. [76, 78–82] We attempted to address these concerns by creating and using global risk measures representing both fiscal health and quality of care, equally decisions for entire hospitals and wellness systems often rely on infirmary level indicators. Global measures are more robust and are preferable to private take a chance factors, equally they are more probable to indicate highly reliable organizations by reducing the influence of gaming and interventions focused on comeback of individual metrics [76, 83, 84].

Variables chosen for aligning in our models are well chronicled in the literature. It is well documented that greater dependence on government payers, such as Medicare and Medicaid are associated with a college probability of fiscal distress because these payers typically practice not pay the average total toll of intendance.[85–91] The analyses were adjusted for teaching infirmary condition as prior studies associated educational activity hospitals with lower financial operation, considering they frequently 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 than likely have adverse effects on smaller facilities.[92] The mixture of operational and market factors influencing the fiscal status of hospitals differently in urban versus non-urban areas is well documented.[19] Not-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 all based on published risk-adjustment methodology.[93, 94]

Policy implications

Federal and State policy also matters equally deficits in the quality of care tin exist systemic, requiring systems level modifications to produce the desired changes and results. As policy makers consider activity to attain the triple aim, the interrelatedness of cut healthcare costs and achieving quality needs to be addressed, particularly as information technology affects the power 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, in general. The attenuation of the association observed hither when controlling for public payer are consequent with previous studies that institute that hospitals with high Medicaid case-mix had worse quality of intendance than other hospitals.[56, 95–100] Research on nursing homes too propose a link between lower Medicaid reimbursement levels and lower quality.[101–104] Despite this association, powerful evidence has been published suggesting that Medicaid has a positive touch on on access to care, financial security, and cocky-reported health.[105, 106] At the same time, under the veil of deficit reduction, future expected cutbacks could pb to reduced access to high quality care. Our findings suggest that any price-cutting efforts by Medicare, Medicaid, and private payers needs to be carefully designed and managed so that patient rubber and patient centered care are not compromised.

Since our assay was performed with hospital level information, we cannot examine variation in individual patient care within each hospital. Patient level assay would be relevant if the disparity in quality that arises from fiscal distress contributed to disparities in outcomes that take 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 every bit racial and gender minorities.[107, 108] [109–114] Evaluating performance on the hospital level of analysis, nonetheless, is relevant since systems and policy decisions are almost commonly determined either at the hospital, land, or federal level [57]. Further, there is no current source of individual level information on patient experience of care, which the analysis presented hither confirms accounts for the most variability beyond the infirmary healthcare system. The results presented here represents the land of affairs in New York prior to the launch of the Delivery System Reform Incentive Payment (DSRIP) Programme funded by the Section 1115 Sit-in Waiver [115]. Repeating these analyses at the end of New York's five-year sit-in will place if systemic changes have reduced the chasms in quality of care, or fabricated them deeper.[116]

Our findings back up the notion that hospitals nether greater financial distress have less favorable patient feel of care, higher readmission rates, and increased hazard of adverse patient quality and condom outcomes for both medical and surgical patients. These substandard outcomes in financially distressed hospitals persisted after adjusting for public payer caseload and hospital characteristics. This suggests that underlying qualities of poorer facilities can lead to medical and surgical care that is inferior equally well as an junior experience for patients. This study provides composite measures that optimized the estimated correlation between financial status and quality/patient prophylactic outcomes. These findings suggest that it is imperative to address financial disparities when incentivizing health care quality through value-based purchasing in order to ensure financial stability and quality of care in safety net facilities.

References

1. 1. Loubeau PR, Jantzen R. U.Southward. hospital bond ratings in the managed care era. J Health Care Finance. 2005;31(three):41–51. Epub 2005/08/06. pmid:16080414.
   • View Article
   • PubMed/NCBI
   • Google Scholar
2. ii. Kim Thursday, McCue MJ. Association of market, operational, and financial factors with nonprofit hospitals' capital investment. Inquiry. 2008;45(two):215–31. Epub 2008/09/05. pmid:18767385.
   • View Article
   • PubMed/NCBI
   • Google Scholar
3. 3. Commission J. <Health intendance at the crossroads: guiding principles for the development of the hospital of the future.pdf>. 2009.
   • View Article
   • Google Scholar
4. 4. Bazzoli GJ, Clement JP, Lindrooth RC, Chen H-F, Aydede SK, Braun BI, et al. Hospital financial condition and operational decisions related to the quality of hospital care. Medical Care Research and Review. 2007;64(2):148–68. pmid:17406018
   • View Article
   • PubMed/NCBI
   • Google Scholar
5. v. Encinosa WE, Bernard DM. Hospital finances and patient safety outcomes. Enquiry. 2005;42(1):threescore–72. Epub 2005/07/fifteen. pmid:16013586.
   • View Article
   • PubMed/NCBI
   • Google Scholar
6. half dozen. Mueller SK, Lipsitz S, Hicks LS. Impact of hospital teaching intensity on quality of intendance and patient outcomes. Med Care. 2013;51(vii):567–74. Epub 2013/04/23. pmid:23604017.
   • View Article
   • PubMed/NCBI
   • Google Scholar
7. 7. Dong GN. Performing well in financial management and quality of care: evidence from hospital process measures for treatment of cardiovascular illness. BMC Wellness Serv Res. 2015;15:45. Epub 2015/02/02. pmid:25638252; PubMed Central PMCID: PMCPMC4345031.
   • View Article
   • PubMed/NCBI
   • Google Scholar
8. 8. Hoehn RS, Wima Thousand, Vestal MA, Weilage DJ, Hanseman DJ, Abbott DE, et al. Effect of Infirmary Rubber-Net Brunt on Cost and Outcomes After Surgery. JAMA Surg. 2016;151(2):120–viii. Epub 2015/10/xvi. pmid:26466334.
   • View Article
   • PubMed/NCBI
   • Google Scholar
9. ix. Jha AK, Orav EJ, Epstein AM. The result of fiscal incentives on hospitals that serve poor patients. Ann Intern Med. 2010;153(v):299–306. Epub 2010/09/08. pmid:20820039.
   • View Article
   • PubMed/NCBI
   • Google Scholar
10. x. Smith RB, Dynan L, Fairbrother G, Chabi G, Simpson L. Medicaid, hospital financial stress, and the incidence of adverse medical events for children. Health Serv Res. 2012;47(4):1621–41. Epub 2012/02/23. pmid:22353008; PubMed Central PMCID: PMCPMC3401402.
    • View Article
    • PubMed/NCBI
    • Google Scholar
11. 11. Weissman JS, Vogeli C, Levy DE. The quality of infirmary treat Medicaid and private pay patients. Med Care. 2013;51(5):389–95. Epub 2013/01/xxx. pmid:23358385.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
12. 12. Platt Hd, Platt MB. Predicting corporate financial distress: reflections on choice-based sample bias. Journal of Economics and Finance. 2002;26(ii):184–99.
    • View Article
    • Google Scholar
13. thirteen. Ly DP, Jha AK, Epstein AM. The clan betwixt hospital margins, quality of care, and closure or other change in operating status. J Gen Intern Med. 2011;26(eleven):1291–six. Epub 2011/08/xiii. pmid:21837374; PubMed Primal PMCID: PMCPMC3208470.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
14. 14. Bazzoli GJ, Chen HF, Zhao Yard, Lindrooth RC. Infirmary financial status and the quality of patient intendance. Health Econ. 2008;17(8):977–95. Epub 2007/12/25. pmid:18157911.
    • View Article
    • PubMed/NCBI
    • Google Scholar
15. fifteen. Volpp KG, Konetzka RT, Zhu J, Parsons L, Peterson E. Event of cuts in Medicare reimbursement on process and consequence of care for acute myocardial infarction patients. Circulation. 2005;112(15):2268–75. Epub 2005/10/06. pmid:16203913.
    • View Article
    • PubMed/NCBI
    • Google Scholar
16. xvi. Krumholz HM, Lin Z, Keenan PS, Chen J, Ross JS, Drye EE, et al. Human relationship between infirmary readmission and mortality rates for patients hospitalized with astute myocardial infarction, heart failure, or pneumonia. Jama. 2013;309(half dozen):587–93. Epub 2013/02/14. pmid:23403683; PubMed Central PMCID: PMCPMC3621028.
    • View Article
    • PubMed/NCBI
    • Google Scholar
17. 17. Encinosa WE, Bernard DM. Hospital finances and patient safety outcomes. INQUIRY: The Periodical of Wellness Care Arrangement, Provision, and Financing. 2005;42(1):60–72. pmid:16013586
    • View Article
    • PubMed/NCBI
    • Google Scholar
18. 18. Ly DP, Jha AK, Epstein AM. The association between hospital margins, quality of care, and closure or other change in operating status. Journal of general internal medicine. 2011;26(11):1291. pmid:21837374
    • View Article
    • PubMed/NCBI
    • Google Scholar
19. xix. Brecher C, Nesbitt S. Factors associated with variation in financial condition among voluntary hospitals. Health Serv Res. 1985;twenty(iii):267–300. Epub 1985/08/01. pmid:4019212; PubMed Cardinal PMCID: PMCPMC1068881.
    • View Article
    • PubMed/NCBI
    • Google Scholar
20. 20. Kim Th. Factors associated with financial distress of nonprofit hospitals. Wellness Care Manag (Frederick). 2010;29(1):52–62. Epub 2010/02/xi. pmid:20145468.
    • View Article
    • PubMed/NCBI
    • Google Scholar
21. 21. Leatherman S, Berwick D, Iles D, Lewin LS, Davidoff F, Nolan T, et al. The business concern case for quality: case studies and an assay. Health affairs (Project Hope). 2003;22(2):17–xxx. Epub 2003/04/04. pmid:12674405.
    • View Article
    • PubMed/NCBI
    • Google Scholar
22. 22. Marshall MN, Shekelle PG, Davies HT, Smith PC. Public reporting on quality in the United states of america and the Britain. Health affairs (Project Hope). 2003;22(three):134–48. Epub 2003/05/22. pmid:12757278.
    • View Article
    • PubMed/NCBI
    • Google Scholar
23. 23. Traska MR. Will operating margins limit admission to capital? Hospitals. 1988;62(2):38–44. Epub 1988/01/20. pmid:3335370.
    • View Article
    • PubMed/NCBI
    • Google Scholar
24. 24. Sherlock DB. Indigent intendance in rational markets. Inquiry. 1986;23(3):261–7. Epub 1986/01/01. pmid:2944835.
    • View Article
    • PubMed/NCBI
    • Google Scholar
25. 25. Lindrooth RC, Bazzoli GJ, Clement J. The event of reimbursement on the intensity of hospital services. Southern Economic Journal. 2007:575–87.
    • View Article
    • Google Scholar
26. 26. Duffy SQ, Friedman B. Hospitals with chronic financial losses: what came side by side? Health affairs (Project Promise). 1993;12(two):151–63. Epub 1993/01/01. pmid:8375810.
    • View Article
    • PubMed/NCBI
    • Google Scholar
27. 27. Burstin Hr, Lipsitz SR, Udvarhelyi IS, Brennan TA. The effect of hospital financial characteristics on quality of care. Jama. 1993;270(7):845–9. Epub 1993/08/18. pmid:8340984.
    • View Article
    • PubMed/NCBI
    • Google Scholar
28. 28. Mouch CA, Regenbogen SE, Revels SL, Wong SL, Lemak CH, Morris AM. The quality of surgical intendance in safety cyberspace hospitals: a systematic review. Surgery. 2014;155(5):826–38. Epub 2014/05/03. pmid:24787109.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
29. 29. Martsolf GR, Auerbach D, Benevent R, Stocks C, Jiang HJ, Pearson ML, et al. Examining the value of inpatient nurse staffing: an assessment of quality and patient care costs. Med Intendance. 2014;52(11):982–eight. Epub 2014/x/12. pmid:25304017.
    • View Article
    • PubMed/NCBI
    • Google Scholar
30. 30. Dranove D, White WD. Medicaid-dependent hospitals and their patients: how have they fared? Health Serv Res. 1998;33(ii Pt 1):163–85. Epub 1998/06/10. pmid:9618666; PubMed Central PMCID: PMCPMC1070259.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
31. 31. Langa KM, Sussman EJ. The event of toll-containment policies on rates of coronary revascularization in California. The New England journal of medicine. 1993;329(24):1784–9. Epub 1993/12/09. pmid:8232488.
    • View Article
    • PubMed/NCBI
    • Google Scholar
32. 32. Bazzoli GJ, Lindrooth RC, Hasnain-Wynia R, Needleman J. The Balanced Upkeep Human action of 1997 and U.S. hospital operations. Inquiry. 2004;41(iv):401–17. Epub 2005/04/20. pmid:15835599.
    • View Article
    • PubMed/NCBI
    • Google Scholar
33. 33. Silow-Carroll S, Alteras T, Meyer JA. Hospital quality improvement: strategies and lessons from US hospitals. New York: The Democracy Fund. 2007.
34. 34. Banks DA, Paterson Thousand, Wendel J. Uncompensated hospital intendance: charitable mission or profitable business organisation conclusion? Health Econ. 1997;6(2):133–43. Epub 1997/03/01. pmid:9158966.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
35. 35. Hibbard JH, Stockard J, Tusler M. Hospital performance reports: impact on quality, market share, and reputation. Health affairs (Project Hope). 2005;24(4):1150–threescore. Epub 2005/07/xiv. pmid:16012155.
    • View Article
    • PubMed/NCBI
    • Google Scholar
36. 36. Swensen SJ, Dilling JA, Mc Carty PM, Bolton JW, Harper CM Jr. The business case for health-care quality improvement. J Patient Saf. 2013;9(ane):44–52. Epub 2013/02/23. pmid:23429226.
    • View Article
    • PubMed/NCBI
    • Google Scholar
37. 37. Casalino LP, Elster A, Eisenberg A, Lewis E, Montgomery J, Ramos D. Will pay-for-operation and quality reporting bear on health care disparities? Health affairs (Projection Hope). 2007;26(three):w405–14. Epub 2007/04/12. pmid:17426053.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
38. 38. Chien AT, Chin MH, Davis AM, Casalino LP. Pay for performance, public reporting, and racial disparities in health intendance: how are programs existence designed? Medical care inquiry and review: MCRR. 2007;64(v Suppl):283s–304s. Epub 2007/10/19. pmid:17881629.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
39. 39. Epstein AM, Lee Th, Hamel MB. Paying physicians for high-quality care. The New England journal of medicine. 2004;350(4):406–10. Epub 2004/01/23. pmid:14736934.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
40. 40. Laschober M, Maxfield M, Felt-Lisk Southward, Miranda DJ. Hospital response to public reporting of quality indicators. Health Care Financ Rev. 2007;28(3):61–76. Epub 2007/07/25. pmid:17645156; PubMed Fundamental PMCID: PMCPMC4194994.
    • View Article
    • PubMed/NCBI
    • Google Scholar
41. 41. Werner RM, Goldman LE, Dudley RA. Comparing of modify in quality of care between safety-net and non-condom-internet hospitals. Jama. 2008;299(18):2180–7. Epub 2008/05/15. pmid:18477785.
    • View Article
    • PubMed/NCBI
    • Google Scholar
42. 42. Kim TH. Factors associated with fiscal distress of nonprofit hospitals. The health care managing director. 2010;29(one):52–62. pmid:20145468
    • View Article
    • PubMed/NCBI
    • Google Scholar
43. 43. Kim TH, McCue MJ. Clan of market, operational, and financial factors with nonprofit hospitals' upper-case letter investment. INQUIRY: The Journal of Wellness Care Arrangement, Provision, and Financing. 2008;45(2):215–31. pmid:18767385
    • View Article
    • PubMed/NCBI
    • Google Scholar
44. 44. Swensen SJ, Dilling JA, Mc Carty PM, Bolton JW, Harper CM Jr. The business case for health-care quality improvement. Journal of patient safety. 2013;ix(1):44–52. pmid:23429226
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
45. 45. Volpp KG, Konetzka RT, Zhu J, Parsons L, Peterson E. Effect of cuts in Medicare reimbursement on process and upshot of care for acute myocardial infarction patients. Circulation. 2005;112(xv):2268–75. pmid:16203913
    • View Article
    • PubMed/NCBI
    • Google Scholar
46. 46. Hadley J, Zuckerman S, Feder J. Profits and financial pressure in the prospective payment system: their impacts on hospitals. Inquiry. 1989;26(iii):354–65. Epub 1989/01/01. pmid:2529213.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
47. 47. Shen YC. The result of financial pressure on the quality of care in hospitals. J Health Econ. 2003;22(ii):243–69. Epub 2003/02/28. pmid:12606145.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
48. 48. Nguyen OK, Halm EA, Makam AN. Human relationship between hospital financial performance and publicly reported outcomes. Journal of hospital medicine. 2016;11(seven):481–8. pmid:26929094
    • View Article
    • PubMed/NCBI
    • Google Scholar
49. 49. Hoehn RS, Wima Yard, Vestal MA, Weilage DJ, Hanseman DJ, Abbott DE, et al. Effect of infirmary safety-cyberspace burden on cost and outcomes after surgery. JAMA surgery. 2016;151(2):120–8. pmid:26466334
    • View Article
    • PubMed/NCBI
    • Google Scholar
50. 50. Clement JP, Lindrooth RC, Chukmaitov As, Chen H-F. Does the patient'south payer matter in hospital patient condom?: a report of urban hospitals. Medical intendance. 2007;45(two):131–8. pmid:17224775
    • View Article
    • PubMed/NCBI
    • Google Scholar
51. 51. Duffy SQ, Friedman B. Hospitals with chronic financial losses: what came next? Health Diplomacy. 1993;12(2):151–63. pmid:8375810
    • View Article
    • PubMed/NCBI
    • Google Scholar
52. 52. Nguyen OK, Halm EA, Makam AN. Relationship between hospital fiscal performance and publicly reported outcomes. J Hosp Med. 2016;xi(seven):481–viii. Epub 2016/03/02. pmid:26929094; PubMed Fundamental PMCID: PMCPMC5362822.
    • View Article
    • PubMed/NCBI
    • Google Scholar
53. 53. Jha AK, Li Z, Orav EJ, Epstein AM. Care in U.Due south. hospitals—the Hospital Quality Brotherhood program. The New England journal of medicine. 2005;353(3):265–74. Epub 2005/07/22. pmid:16034012.
    • View Article
    • PubMed/NCBI
    • Google Scholar
54. 54. Jha AK, Orav EJ, Epstein AM. The effect of fiscal incentives on hospitals that serve poor patients. Annals of internal medicine. 2010;153(5):299–306. pmid:20820039
    • View Article
    • PubMed/NCBI
    • Google Scholar
55. 55. Mueller SK, Lipsitz S, Hicks LS. Touch of hospital teaching intensity on quality of intendance and patient outcomes. Medical care. 2013;51(7):567–74. pmid:23604017
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
56. 56. Goldman LE, Vittinghoff Eastward, Dudley RA. Quality of care in hospitals with a high percentage of Medicaid patients. Med Intendance. 2007;45(6):579–83. Epub 2007/05/23. pmid:17515786.
    • View Article
    • PubMed/NCBI
    • Google Scholar
57. 57. Cefalu MS, Elliott MN, Setodji CM, Cleary PD, Hays RD. Hospital quality indicators are not unidimensional: A reanalysis of Lieberthal and Comer. Health services research. 2019;54(2):502–viii. pmid:30259508
    • View Article
    • PubMed/NCBI
    • Google Scholar
58. 58. Indicators AQ. Guide to inpatient quality indicators 2002 [cited 2018 09/12/2018]. Available from: https://www.ahrq.gov/downloads/pub/inpatqi/iqi_guide.pdf.
    • View Article
    • Google Scholar
59. 59. Health NYSDo. Hospital Inpatient Discharges (SPARCS De-Identified) Downloadable File: 2014. February five, 2016 ed. http://health.data.ny.gov/Wellness/Infirmary-Inpatient-Discharges-SPARCS-De-Identified/rmwa-zns42014.
60. threescore. Services CfMM. HCAHPS Fact Sheet 2017 [updated November 2017]. Available from: http://world wide web.hcahpsonline.org/globalassets/hcahps/facts/hcahps_fact_sheet_november_2017a.pdf.
61. 61. Forum NQ. National Quality Forum Endorsed Measures for Person and Family Centered Intendance.pdf [Draft Report]. 2014 [updated September v, 2014]. Available from: http://www.qualityforum.org/projects/person_family_centered_care/.
    • View Commodity
    • Google Scholar
62. 62. Gapenski LC, Reiter KL. Healthcare finance: an introduction to bookkeeping and fiscal direction: Health Assistants Press Chicago, IL; 2008.
63. 63. Rosenthal GE, Harper DL, Quinn LM, Cooper GS. Severity-adjusted mortality and length of stay in teaching and nonteaching hospitals: results of a regional study. Jama. 1997;278(6):485–90. pmid:9256223
    • View Article
    • PubMed/NCBI
    • Google Scholar
64. 64. Allison JJ, Kiefe CI, Weissman NW, Person SD, Rousculp Chiliad, Canto JG, et al. Relationship of hospital instruction status with quality of intendance and bloodshed for Medicare patients with acute MI. Jama. 2000;284(10):1256–62. pmid:10979112
    • View Article
    • PubMed/NCBI
    • Google Scholar
65. 65. Lieberthal RD, Comer DM. What are the characteristics that explain hospital quality? A longitudinal pridit approach. Risk Management and Insurance Review. 2014;17(1):17–35.
    • View Article
    • Google Scholar
66. 66. Freedman DA. Statistical models: theory and practice: cambridge university press; 2009.
67. 67. Pedhazur E. Multiple regression in behavioral research: Explanation and prediction. Third ed. Thomson Learning Inc.: Christopher P. Klein; 1997.
68. 68. Breiman L. Classification and regression trees: Routledge; 2017.
69. 69. Feder J, Hadley J, Zuckerman South. How did Medicare's prospective payment arrangement bear upon hospitals? New England Journal of Medicine. 1987;317(fourteen):867–73. pmid:3306387
    • View Article
    • PubMed/NCBI
    • Google Scholar
70. lxx. Hadley J, Zuckerman South, Feder J. Profits and financial pressure level in the prospective payment system: Their impacts on hospitals. Inquiry. 1989:354–65. pmid:2529213
    • View Article
    • PubMed/NCBI
    • Google Scholar
71. 71. Shen Y-C. The effect of financial force per unit area on the quality of care in hospitals. Journal of health economics. 2003;22(two):243–69. pmid:12606145
    • View Article
    • PubMed/NCBI
    • Google Scholar
72. 72. Volpp K, Buckley E. The effect of increases in HMO penetration and changes in payer mix on in-infirmary mortality and treatment patterns for acute myocardial infarction. The American periodical of managed care. 2004;x(seven Pt 2):505–12.
    • View Article
    • Google Scholar
73. 73. Feder J, Hadley J, Zuckerman S. How did Medicare'southward prospective payment organisation affect hospitals? The New England journal of medicine. 1987;317(14):867–73. Epub 1987/10/01. pmid:3306387.
    • View Article
    • PubMed/NCBI
    • Google Scholar
74. 74. Clement JP, Lindrooth RC, Chukmaitov As, Chen HF. Does the patient'southward payer thing in infirmary patient safety?: a report of urban hospitals. Med Care. 2007;45(2):131–8. Epub 2007/01/17. pmid:17224775.
    • View Article
    • PubMed/NCBI
    • Google Scholar
75. 75. Volpp KG, Buckley East. The event of increases in HMO penetration and changes in payer mix on in-infirmary mortality and treatment patterns for acute myocardial infarction. Am J Manag Intendance. 2004;x(vii Pt ii):505–12. Epub 2004/08/x. pmid:15298238.
    • View Article
    • PubMed/NCBI
    • Google Scholar
76. 76. Saver BG, Martin SA, Adler RN, Candib LM, Deligiannidis KE, Golding J, et al. Care that Matters: Quality Measurement and Health Care. PLOS Medicine. 2015;12(11):e1001902. pmid:26574742
    • View Article
    • PubMed/NCBI
    • Google Scholar
77. 77. Glasziou PP, Buchan H, Del Mar C, Doust J, Harris K, Knight R, et al. When financial incentives practise more than good than harm: a checklist. BMJ (Clinical research ed). 2012;345:e5047. Epub 2012/08/sixteen. pmid:22893568.
    • View Article
    • PubMed/NCBI
    • Google Scholar
78. 78. Eddy DM, Adler J, Patterson B, Lucas D, Smith KA, Morris Grand. Individualized guidelines: the potential for increasing quality and reducing costs. Ann Intern Med. 2011;154(ix):627–34. Epub 2011/05/04. pmid:21536939.
    • View Article
    • PubMed/NCBI
    • Google Scholar
79. 79. Guthrie B, Payne K, Alderson P, McMurdo ME, Mercer SW. Adapting clinical guidelines to accept account of multimorbidity. BMJ (Clinical enquiry ed). 2012;345:e6341. Epub 2012/10/06. pmid:23036829.
    • View Article
    • PubMed/NCBI
    • Google Scholar
80. 80. McGlynn EA, Schneider EC, Kerr EA. Reimagining quality measurement. The New England journal of medicine. 2014;371(23):2150–iii. Epub 2014/12/04. pmid:25470693.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
81. 81. McShane Yard, Mitchell E. Person centred coordinated care: where does the QOF point us? BMJ (Clinical enquiry ed). 2015;350:h2540. Epub 2015/06/13. pmid:26067130.
    • View Article
    • PubMed/NCBI
    • Google Scholar
82. 82. Loxterkamp D. Humanism in the time of metrics—an essay past David Loxterkamp. BMJ (Clinical research ed). 2013;347:f5539. Epub 2013/09/21. pmid:24052583.
    • View Article
    • PubMed/NCBI
    • Google Scholar
83. 83. Eddy DM, Adler J, Morris M. The 'Global Outcomes Score': A Quality Measure, Based On Wellness Outcomes, That Compares Current Intendance To A Target Level Of Care. Wellness Affairs. 2012;31(11):2441–50. pmid:23129674
    • View Article
    • PubMed/NCBI
    • Google Scholar
84. 84. Eddy DM, Adler J, Morris Thou. The 'Global Outcomes Score': a quality measure, based on wellness outcomes, that compares electric current care to a target level of care. Wellness affairs (Project Hope). 2012;31(11):2441–fifty. Epub 2012/11/07. pmid:23129674.
    • View Article
    • PubMed/NCBI
    • Google Scholar
85. 85. Vogel WB, Langland-Orban B, Gapenski LC. Factors influencing high and low profitability amid hospitals. Health Care Management Review. 1993;18(two):fifteen–26. pmid:8320103
    • View Article
    • PubMed/NCBI
    • Google Scholar
86. 86. Dranove D, White WD. Medicaid-dependent hospitals and their patients: How have they fared? Health services research. 1998;33(2 Pt 1):163.
    • View Article
    • Google Scholar
87. 87. McCue MJ, Renn SC, Pillari GD. Factors affecting credit rating downgrades of hospital revenue bonds. Inquiry. 1990:242–54. pmid:2145225
    • View Article
    • PubMed/NCBI
    • Google Scholar
88. 88. Rosko Doc. The supply of uncompensated care in Pennsylvania hospitals: motives and financial consequences. Health Care Management Review. 2004;29(3):229–39. pmid:15357233
    • View Article
    • PubMed/NCBI
    • Google Scholar
89. 89. Vogel WB, Langland-Orban B, Gapenski LC. Factors influencing high and low profitability amidst hospitals. Health Care Manage Rev. 1993;18(2):15–26. Epub 1993/01/01. pmid:8320103.
    • View Article
    • PubMed/NCBI
    • Google Scholar
90. ninety. McCue MJ, Renn SC, Pillari GD. Factors affecting credit rating downgrades of hospital revenue bonds. Inquiry. 1990;27(3):242–54. Epub 1990/01/01. pmid:2145225.
    • View Article
    • PubMed/NCBI
    • Google Scholar
91. 91. Rosko Medico. The supply of uncompensated care in Pennsylvania hospitals: motives and financial consequences. Health Care Manage Rev. 2004;29(iii):229–39. Epub 2004/09/11. pmid:15357233.
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
92. 92. McCue MJ. The utilise of cash catamenia to analyze financial distress in California hospitals. Periodical of Healthcare Direction. 1991;36(ii):223.
    • View Commodity
    • Google Scholar
93. 93. Davies SM, Geppert J, McClellan M, McDonald KM, Romano PS, Shojania KG. Refinement of the HCUP quality indicators. 2001.
    • View Article
    • Google Scholar
94. 94. Davies SM, Geppert J, McClellan M, McDonald KM, Romano PS, Shojania KG. AHRQ Technical Reviews. Refinement of the HCUP Quality Indicators. Rockville (Physician): Agency for Healthcare Research and Quality (US); 2001.
95. 95. Goldman LE, Vittinghoff E, Dudley RA. Quality of care in hospitals with a high percent of Medicaid patients. Medical care. 2007;45(six):579–83. pmid:17515786
    • View Article
    • PubMed/NCBI
    • Google Scholar
96. 96. Werner RM, Goldman LE, Dudley RA. Comparing of change in quality of care betwixt safety-internet and non–condom-internet hospitals. Jama. 2008;299(18):2180–7. pmid:18477785
    • View Article
    • PubMed/NCBI
    • Google Scholar
97. 97. Popescu I, Werner RM, Vaughan-Sarrazin MS, Cram P. Characteristics and outcomes of America's lowest-performing hospitals: an assay of acute myocardial infarction hospital care in the United States. Circulation: Cardiovascular Quality and Outcomes. 2009;2(3):221–7. pmid:20031841
    • View Article
    • PubMed/NCBI
    • Google Scholar
98. 98. Landon BE, Schneider EC, Normand S-LT, Scholle SH, Pawlson LG, Epstein AM. Quality of care in Medicaid managed care and commercial health plans. JAMA. 2007;298(fourteen):1674–81. pmid:17925519
    • View Article
    • PubMed/NCBI
    • Google Scholar
99. 99. Popescu I, Werner RM, Vaughan-Sarrazin MS, Cram P. Characteristics and outcomes of America's lowest-performing hospitals: an analysis of astute myocardial infarction hospital intendance in the Usa. Circulation Cardiovascular quality and outcomes. 2009;2(3):221–7. Epub 2009/12/25. pmid:20031841; PubMed Cardinal PMCID: PMCPMC5361404.
    • View Article
    • PubMed/NCBI
    • Google Scholar
100. 100. Landon BE, Schneider EC, Normand SL, Scholle SH, Pawlson LG, Epstein AM. Quality of intendance in Medicaid managed care and commercial health plans. Jama. 2007;298(14):1674–81. Epub 2007/10/11. pmid:17925519.
     • View Article
     • PubMed/NCBI
     • Google Scholar
101. 101. Mor V, Gruneir A, Feng Z, Grabowski DC, Intrator O, Zinn J. The event of state policies on nursing home resident outcomes. Journal of the American Geriatrics Society. 2011;59(1):3–9. pmid:21198463
     • View Article
     • PubMed/NCBI
     • Google Scholar
102. 102. Grabowski DC, Gruber J, Angelelli JJ. Nursing home quality equally a common expert. The Review of Economics and Statistics. 2008;xc(4):754–64. pmid:20463859
     • View Commodity
     • PubMed/NCBI
     • Google Scholar
103. 103. Mor V, Gruneir A, Feng Z, Grabowski DC, Intrator O, Zinn J. The consequence of state policies on nursing home resident outcomes. J Am Geriatr Soc. 2011;59(1):3–nine. Epub 2011/01/05. pmid:21198463; PubMed Key PMCID: PMCPMC3731756.
     • View Commodity
     • PubMed/NCBI
     • Google Scholar
104. 104. Grabowski DC, Gruber J, Angelelli JJ. Nursing Dwelling Quality equally a Common Good. Rev Econ Stat. 2008;90(iv):754–64. Epub 2008/11/01. pmid:20463859; PubMed Primal PMCID: PMCPMC2867608.
     • View Article
     • PubMed/NCBI
     • Google Scholar
105. 105. Baicker K, Finkelstein A. The furnishings of Medicaid coverage—learning from the Oregon experiment. New England Journal of Medicine. 2011;365(viii):683–5. pmid:21774703
     • View Article
     • PubMed/NCBI
     • Google Scholar
106. 106. Baicker K, Finkelstein A. The effects of Medicaid coverage—learning from the Oregon experiment. The New England journal of medicine. 2011;365(8):683–five. Epub 2011/07/22. pmid:21774703; PubMed Cardinal PMCID: PMCPMC3321578.
     • View Commodity
     • PubMed/NCBI
     • Google Scholar
107. 107. Gaskin DJ, Spencer CS, Richard P, Anderson GF, Powe NR, LaVeist TA. Practise hospitals provide lower-quality care to minorities than to whites? Wellness Diplomacy. 2008;27(2):518–27. pmid:18332510
     • View Article
     • PubMed/NCBI
     • Google Scholar
108. 108. Gaskin DJ, Spencer CS, Richard P, Anderson GF, Powe NR, Laveist TA. Do hospitals provide lower-quality care to minorities than to whites? Health diplomacy (Project Promise). 2008;27(ii):518–27. Epub 2008/03/12. pmid:18332510.
     • View Article
     • PubMed/NCBI
     • Google Scholar
109. 109. Barnato AE, Lucas FL, Staiger D, Wennberg DE, Chandra A. Hospital-level racial disparities in acute myocardial infarction treatment and outcomes. Medical care. 2005;43(iv):308. pmid:15778634
     • View Article
     • PubMed/NCBI
     • Google Scholar
110. 110. Hasnain-Wynia R, Baker DW, Nerenz D, Feinglass J, Aggravate AC, Landrum MB, et al. Disparities in wellness intendance are driven by where minority patients seek care: examination of the infirmary quality brotherhood measures. Archives of Internal Medicine. 2007;167(12):1233–9. pmid:17592095
     • View Article
     • PubMed/NCBI
     • Google Scholar
111. 111. Smedley BD, Stith AY, Nelson AR. Establish of Medicine, Commission on Agreement and Eliminating Racial and Ethnic Disparities in Health Care. Unequal treatment: against racial and ethnic disparities in healthcare. Washington, DC: National Academies Press; 2003.
112. 112. Barnato AE, Lucas FL, Staiger D, Wennberg DE, Chandra A. Hospital-level racial disparities in astute myocardial infarction treatment and outcomes. Med Intendance. 2005;43(4):308–19. Epub 2005/03/22. pmid:15778634; PubMed Key PMCID: PMCPMC2121607.
     • View Article
     • PubMed/NCBI
     • Google Scholar
113. 113. Hasnain-Wynia R, Baker DW, Nerenz D, Feinglass J, Beal Ac, Landrum MB, et al. Disparities in health care are driven by where minority patients seek care: examination of the infirmary quality brotherhood measures. Curvation Intern Med. 2007;167(12):1233–9. Epub 2007/06/27. pmid:17592095.
     • View Article
     • PubMed/NCBI
     • Google Scholar
114. 114. Plant of Medicine Committee on U, Eliminating R, Ethnic Disparities in Health C. In: Smedley BD, Stith AY, Nelson AR, editors. Diff Treatment: Against Racial and Ethnic Disparities in Wellness Care. Washington (DC): National Academies Press (US) Copyright 2002 by the National Academy of Sciences. All rights reserved.; 2003.
115. 115. Roby DH, Louis CJ, Cole MMJ, Chau N, Wiefling B, Salsberry DC, et al. Supporting transformation through delivery system reform incentive payment programs: lessons from New York state. 2018.
     • View Commodity
     • Google Scholar
116. 116. Corrigan JM. Crossing the quality chasm. Building A Better Delivery System. 2005.
     • View Article
     • Google Scholar

Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0219124

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