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Layout References Mallings Review Help Predicting Financial Distress and Closure in Rural Hospitals. Purpose: Annual rates of rural hospital closure have been increasing since 2010, and hospitals that close have poor financial performance relative to those that remain open. This study develops and validates a latent index of financial distress to forecast the probability of financial distress and closure within 2 years for rural hospitals. Methods: Hospital and community characteristics are used to predict the risk of financial distress 2 years in the future. Financial and community data were drawn for 2,466 rural hospitals from 2000 through 2013. We tested and validated a model predicting a latent index of financial distress (FDI), measured by unprofitability, equity decline, insolvency, and closure. Using the predicted FDI score, hospitals are assigned to high, medium-high, medium-low, and low risk of financial distress for use by practitioners. Findings: The FDI forecasts 8.01% of rural hospitals to be at high risk of financial distress in 2015, 16.3% as mid-high, 46.8% as mid-low, and 28.9% as low risk. The rate of closure for hospitals in the high-risk category is 4 times the rate in the mid-high category and 28 times that in the mid-low category. The ability of the FDI to discriminate hospitals experiencing financial distress is supported by a c-statistic of.74 in a validation sample. Conclusion: This methodology offers improved specificity and predictive power relative to existing measures of financial distress applied to rural hospitals. This risk assessment tool may inform programs at the federal, state, and local levels that provide funding or support to rural hospitals. bankruptcy; critical access hospitals; financial distress; hospital closure; rural health From January 2010 to December 2015, 63 rural hospitals closed,[ 1] and over 1.7 million people are now at greater risk of negative health and economic hardship due to the loss of local acute care services.[ 2] The impact of rural hospital closures is of particular concern because residents of rural communities are typically older and poorer, more dependent on public insurance programs, and in worse health than urban residents.[ 3] , [ 4] , [ 5] Hospitals that closed have poor financial performance relative to those that remain open; furthermore, hospitals in financial distress yet remaining open may choose to cut services (eg, labor and delivery, surgery), negatively impacting local access to services.[ 2] Policy makers are currently considering legislative action to preserve access to acute care services in rural areas, and these efforts may benefit from a tool to forecast which hospitals are at high risk of financial distress and closure.[ 6] The need for such predictions is reflected in the wide range of models developed for this purpose in other industries as well as the well-documented body of literature evaluating them. Over the past 50 years, finance scholars and practitioners have produced statistical and artificial intelligence models that attempt to predict financial distress and bankruptcy.[ 7] Statistical methods, specifically multivariant discriminate analysis (MDA) and binary logistic models, have been widely adopted in industry.[ 8] , [ 9] , [ 10] , [ 11] [ 12] , [ 13] , [ 14] Edward Altman's Z-score provides a relatively transparent formula that predicts with 90% accuracy the probability of bankruptcy within 1 year, although the prediction is less accurate when bankruptcy is less eminent.[ 15] Likewise, predictive models using equity balances, number of revenue sources, administrative costs, and operating measures failed to identify vulnerable organizations more than 58% of the time,[ 16] and later work defined financial distress as a significant decrease in equitybalances over 3 years.[ 1/] Error rates remain high in models that use debt ratios, revenue concentration index and total margin to predict vulnerability, while controlling for size and sector.[ 18] Despite the evolution of new methodologies, the Altman model remains the most widely used method for predicting distress[ 19] , [ 20] ; however, existing literature suggests that the Z-score should only be cautiously applied to hospitals.[ 21] In a sample of 50 large teaching hospitals between 2002 and 2004, the mean Z-score decreased over the study period, which indicates higher risk of distress, yet mean margins increased by 30%. In the same study, two-thirds of the hospitals were deemed to be at risk for facing bankruptcy (either in the cautionary zone or in dire crisis) within a year. Similarly, the Financial Strength Index (FSI), a benchmark-based algorithm (total margin, days cash on hand, debt financing, and age of plant) developed specifically for hospitals, classifies roughly half of rural hospitals as in fair or poor financial health.[ 22] Due to the high proportions considered at risk, the identification of those hospitals facing the highest risk of closure is challenging, and the ability to target interventions designed to improve financial viability is limited. Accurate predictions of financial distress for rural hospitals are sought by organizations' managers, owners, regulators, and financiers, as well as policy makers and program administrators. However, current financial models often do not work as well when applied to the health care industry, potentially because health care services are consumed and incentivized differently than other goods. Rural hospitals are typically small, not-for-profit organizations, and few studies have explored models predicting financial distress outside of a for-profit environment. A well-functioning prediction model could be useful as an early warning system to identify hospitals at increased risk of facing financial distress. A tool categorizing rural hospitals into risk levels could easily be incorporated into program requirements. The purpose of this study is thus 2-fold: (1) identify the characteristics associated with subsequent financial distress, and ( 2) apply those findings to develop an index that can be used to identify rural hospitals at risk of financial distress in the near future. Conceptual Framework Basic financial statements give an accounting picture of an organization's operations and financial position.[ 1] In general terms, the balance sheet is a snapshot of an organization's total assets, total liabilities, and equity taken at a specified point in time: ( 1) Total assets - Total liabilities = Equity The income statement summarizes the revenue generated, the expenses incurred, and the net income over a specified period of time (2) Total revenue - Total expenses = Net income And, assuming no dividends are paid out: (3) Equity (t+1) = Equity (t) + Net income (t+1) Taken together, these 3 equations show that profitability (positive net income) leads to growth in equity and unprofitability (negative net income) leads to decline in equity. These accounting relationships form our conceptual framework for hospital financial distress. In this model, financial distress is characterized by 4 financial events of increasing signal strength. A hospital may survive some years of unprofitability (negative operating income or net income) by drawing on lines of credit, dipping into reserves, or using some other short-term tactic; however, unprofitability is a stronger signal of distress when an organization uses depreciation to meet operating expenses (ie, a negative cash flow margin). A high level of unprofitability may result in substantial equity decline as losses reported on the income statement are reflected in the equity value on the balance sheet. If a hospital hasreported off the income statement are reflected in the equity value of the balance sheet. If a hospital fias a low equity value or experiences a large loss, the result may be accounting insolvency, which occurs when total liabilities exceed total assets (negative equity). Many insolvent hospitals seek bankruptcy protection.[ 2] If bankruptcy fails and no other options such as merger and acquisition are available, a likely outcome is closure, which means a hospital no longer exists as an acute inpatient hospital and either converts to another type of facility or closes its doors altogether. Given that a rural hospital has experienced one or more financial distress events, the question arises: "In a previous time period, could we have predicted that a hospital would be in financial distress?" Potential predictors including financial performance, organizational characteristics and market characteristics were identified through a literature review.[ 23] , [ 24] , [ 25] Specifically, predictors with a clear causal connection to financial distress were prioritized to ensure a parsimonious model that was transparent and easy to understand by policy makers. Not all hospitals that close experience all of the financial events above, and not all hospitals that experience unprofitability, equity decline, and insolvency ultimately close. However, this model assumes that ( 1) some or all of the financial events are applicable to most hospitals that experience financial distress, and ( 2) hospital financial distress can be characterized by this continuum of financial events. It is hypothesized that the continuum of financial distress events can be operationalized by a Financial Distress Index (FDI) and that hospital financial performance, government reimbursement, organizational characteristics, and market characteristics can predict the risk of distress 2 years hence (Figure [Nal] ).Method Primary data sources include the Centers for Medicaid and Medicare Services (CMS) Healthcare Cost Report Information System (HCRIS, "Medicare Cost Reports") and Provider of Services (POS) files from 2000 to 2014 for financial variables and Nielsen-Claritas Population Facts for the market variables.[ 3] For this study, rural hospitals were defined as short-term general acute nonfederal facilities located outside metropolitan Core Based Statistical Areas or within metropolitan areas and having Rural-Urban Commuting Area codes of 4 or greater as well as all Critical Access Hospitals (CAHs); this is the definition used by the federal Office of Rural Health Policy, among other federal programs.[ 26] After cost reports for periods less than 360 days were excluded, the data set consisted of 31,438 hospital-year observations. Because our model included lagged and forecast data, the sample used to validate the model and generate results included 24,957 hospital-year observations from 2003 through 2013. Analysis could not be conducted for 6.7% of the sample (N = 1695), primarily due to missing multiyear data to define the 2-year change in total margin (N = 1553). Dependent Variables The latent construct of financial distress manifests in a binary state of reality: a hospital is either in distress or it is not; thus, the dependent variables were defined as binary indicators where 1 represents positive for distress event 2 years in the future (time t+2) relative to the independent variables (time t) and 0 otherwise. The 4 markers of financial distress were selected based on theory, prior empirical evidence, and validation based on comparison of the number of events experienced by open and closed hospitals using Wilcoxon rank test of medians. Unprofitability was measured by negative cash flow margin. Operating and total margin are commonly used measures;[ 24] , [ 27] , [ 28] , [ 29] , [ 30] , [ 31] , [ 32] [ 33] , [ 34] however, cash flow and balance sheet measures may be more accurate descriptions of hospital financial health.[ 30] Equity decline was measured by a greater than 20% decline in equity over 2 years. Insolvency was measured by negative equity (total liabilities > total assets). Bankruptcy was excluded from the FDI due to lack of data in this population. Closure was measured by cessation of inpatient care. For closures 2000-2013, the CMS POS files were analyzed to identify hospitals that potentially ceased inpatient operations. Potential closures were verified through contacting representatives of state hospital associations. For closures after 2013, closures were identified through websites and newspaper databases. From January 2005 through April 2015, 97 hospitals were identified as closed. Observations were considered positive for closure if the cost report year was within 2 years of the year of closure. To maximize our confidence in the model, we used a randomly selected 50% sample of the observations to calibrate the model and validated the model in the full sample.[ 35] Independent Variables Financial Performance Several financial ratios were used.[ 22] Lower profitability is hypothesized to be associated with a higher probability of financial distress and is measured by total margin and 2-year change in total margin. Reinvestment reflects the ability of a hospital to endure unprofitability. Less reinvestment, measured by retained earnings as a percent of total assets, is hypothesized to be associated with a higher probability of financial distress. Benchmark performance reflects the overall quality of hospital financial management. Comparing financial indicators over the 5 years before bankruptcy, differences between closed and open health systems were found in debt to equity ratios, debt service coverage, days cash on hand, and trends in financial performance variables.[ 36] Additional relevant financial variables include age of plant,[ 23] , [ 25] [ 32] current ratio,[ 37] , [ 21] earnings before interest and taxes over total assets, [ 21] cash flows, [ 38] return on assets, [ 39] and retained earnings.[ 21]Poorer benchmark performance is hypothesized to be associated with a higher probability of financial distress. It was measured by the average percent of nongussing benchmarks met over 2 years using the 11 benchmarks developed for assessing CAH performance (operating margin >2%, total margin >3%, cash flow margin >5%, return on equity >4.5%, current ratio >2.3, days cash on hand >60, days revenue in accounts receivable 3, long-term debt to capitalization 60%, and average age of plant 100 beds (straight line distance between coordinates geocoded from CMS addresses) and market share (if 100 beds Log form 31.58 (22.53-49.31) Market share Spline at 25% 26.06 (18.89-33.57) Proportion households in poverty Log form 0.11 ( 0.08- 0.14) Market population Log form 36,506 (17,004-71,333) Table [NaNI presents the results of the preferred model as odds ratios (OR). All of the coefficients are consistent with the hypothesized direction, except for-profit status, and they are highly significant (P <.01 except for cah status which was statistically significant at a conventional level total margin is one of the most influential factors in model and increase reduced odds distress by for-profit hospitals were more than twice as likely to experience relative government-owned not-for-profit decreased with increases hospital size among market characteristics an proportion households poverty service area increased while increasing share miles nearest or="0.86)." finally medicaid reimbursement medicare logistic regression forecasting risk financial rural description variable ratiostandard error performance profitability percent two year change reinvestment retained earnings assets net patient revenue millions benchmark benchmarks met years organizational population ownership competition bed economic condition cfiiduseholde poverllog government reimbm critical access fee index cunstan i cstatistic test discrimination prediction score equivalent selection chance higher indicates meaningful prediction. e fbi had predicting occurrence any event within years. negative cash ow equity decline scores respectively. results indicate that experiencing these signals nancial disbess receive rating not distress. figure presents range predicted theta eventspecic associated probability sample. probabilities each are shown. less assigned low midlow mid high over risk. rated midhigh midlcw cahs be ranked other mral illustrates rate events later closure fdi levels. q we per hospitalyear step up low-nsk experienced compared mid-low- hospitals. decreasing equityjumps from category highrisk category. high-risk times mid-high mid-low demonstrates ability successfully discriminate imminent closures w- methodology actual rates respectively applying z fsi algorithms same discussion hypothesized allof poorer lower smaller variables only finding contrary ownership: curious given previous research showed for-profits offer relatively profitable medical services unprofitable nonprofits often fall middle. although have operating margins they may subsidies grants charitable donations improve their margins. addition poor this study due located south where historically profitability. regions designations face observed mid-low-risk three groups particular. may. benefit predictive tool: . federal state offices health. agencies responsible health could use identify would policies interventions condition. impacts programs on regional levels also evaluated using tool. governments. many states default when provide capital guarantee loans wisconsin statute example dormitory authority new york recently filed letter bankruptcy court eastern district asserting multiple occurred under credit agreement interfaith center inc. then its own challenging assertions contained earlier letter. types situations costly both lender borrower. theeastern county local communities being asked assume through support levies forms aid. city officials quincy washington grant commissioners about possible million loan pay part debt owed county. voters rejected maintenance operations levy paid down debt. wake rejection closed surgery department laid off some employees cut others full time time. informing decision-making public private funding sources provides method trends simulate potentially closure. measures impact shocks extent included model. payment structures potential effect modeled based margin. already susceptible presence macroeconomic pressures like great recession policy changes affordable care act. limitation physician proxy reimbursement. reflects variation fee-for- includes half patients. furthermore values used here prior implementation act expected average identifying inclusive current measure power second measure. developed reliable all nevertheless seems at-risk accurately. effects individual reported should interpreted caution because designed evaluate marginal predict well outliers excluded development.conclusion has specificity existing methods identified continues accelerate future focus footnotes organizations utilize different terms balance sheet position income statement activities convenience utilized terminology. reorganization united code codified title debtors restructure repayment plans make them easily met. liquidation sell certain order money can creditors. specific hcris accounts generate contact authors. pays costs. receives allowable costs outpatient inpatient laboratory therapy post acute swing beds. though analysis restricted single prevalence influences positive value unlike vary defined dataset permanent event. thus counting multi observations double disclosures: authors declare there no conflicts interestreferences north carolina program. closures: at: http: al accessed brief kaufman bg thomas sr randolph rk et al. rising hosp insurance newkirk v damico a. coverage available ctrl click follow link april>