Financial distress refers to period when a borrower is unable to meet a payment obligation to lenders (Zaki, Bah, & Rao, 2011). Consequences of financial distress in organizations include either closure (through bankruptcy or other formal proceedings), merger (getting acquired) with competitors or a turnaround strategy (Langabeer, 2008). A failing business will go for a closure if the attempts at the other two options (mergers or turnaround) are unsuccessful (Bazzoli & Andes, 1995). If hospitals experiencing financial distress decide to close, it can affect community at large by reducing access to care (Buchmueller, Jacobson, & Wold, 2006), creating unemployment among healthcare professionals and likely affecting the health of employees and residents (Jackson & Whyte, 1998).
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Financial distress may be due to organizational characteristics like poor management or environmental characteristics like economic distress and decline in firm’s industry (Wruck, 1990). In case of acute care hospitals, researchers have found factors like teaching status (Liu, Jervis, Younis, & Forgione, 2011), payer mix (Bazzoli & Cleverley, 1994), charity care (Trussel et al., 2010) and hospital size (Brecher & Nesbitt, 1985) associated with financial distress. While all these studies provide valuable insights into organizational determinants of hospital financial distress, they have failed to consider an exhaustive list of environmental determinants. A more comprehensive understanding of the organizational and environmental determinants of hospital financial distress is important if hospitals are to survive and continue to fulfill their missions as the center for health care among their community.
Identify and discuss the theory (or theories) that might be used to examine the relationships between organizational and environmental factors and financial distress in acute care hospitals. Review empirical studies in the health services sector which have tested this theory and the relationship(s) of interest. Describe in detail a conceptual model based on this theory and empirical research that could be used to test the relationships of interest. Your answer should specify an operationalization of the variables included in the model and a discussion of the statistical methods you would use.
From the 1980s to the year 2017, there has been a gradual decline in the number of hospitals in the United States. Even though this trend leveled-off in the early 2000s, there were still significantly fewer hospitals in the country in 2017 than in 1980 (7,166 vs. 5,564, or 22 percent fewer). The decline has been primarily in the form of closures. Between 2005 and 2017, more than 120 rural hospitals have been closed in the US (North, Services, & Research, 2018). Hospitals in urban areas are not left behind in the closure trend as 52 major cities in the United States have experienced a steep decline from 781 hospitals in 1970 to 426 in 2010 (Sager, 2013). One consequence of closures is that, an increasing number of counties have lost the medical care provided by a local hospital (Zhao, 2007). Also, hospital closures have been shown to create unemployment among healthcare professionals and affect the health of employees and residents (Jackson & Whyte, 1998). Another study on hospital closures showed increased infant mortality rate, increased deaths from unintentional injuries and decreased access to care in lower-income residents (Buchmueller, Jacobson, & Wold, 2006). However, hospital closure is never considered a sudden event. It is usually a result of prolonged financial distress(Duffy & Friedman, 1993). Hospitals that experience financial distress are at a higher risk of closure (Richards, Rundle, Wright, & Hershman, 2017).
Financial distress refers to a period when a payment obligation is left unmet by a borrower to a lender (Zaki, Bah, & Rao, 2011).Hospitals that close exhibit financial distress relative to those that remain open (Holmes, Kaufman, & Pink, 2017). There is no formal definition of financial distress. Financial distress as measured by variables like higher debt, low profitability, low liquidity, and higher capital/bed have been used as predictors of hospital failure (Gardiner, Oswald, & Jahera Jr, 1996). Bazzoli and Andes defined financial distress as those hospitals with at a BBB credit rating, based on a three year average (G. Bazzoli & Andes, 1995). Other studies have used Altman Z score model (Langabeer, 2006), financial strength index (Kim, 2010) and cash flow ratio (McCue, 1991) as alternative measures of financial distress in hospitals. However, regardless of the measures used to define financial distress, there are common determinants that facilitate the distress. Understanding the determinants that lead to hospitals’ financial distress is essential if hospitals are to continue their existence and accomplish their missions as healthcare providers among their community.
Determinants of financial distress may be organization specific characteristics like poor management or environment specific characteristics like economic distress and decline in the firm’s industry (Wruck, 1990). In the case of acute care hospitals, researchers have found factors like teaching status (Liu, Jervis, Younis, & Forgione, 2011), payer mix (Gloria J Bazzoli & Cleverley, 1994), charity care (Trussel et al., 2010) and hospital size (Brecher & Nesbitt, 1985) associated with financial distress. Another study of hospitals in Pennsylvania between 1997-2006 found lower total revenues, low cash flow to debt ratio, low occupancy rate, high charity care, and teaching status to be associated with financial distress (Trussel et al., 2010). These studies provide valuable insights into organizational determinants of hospital financial distress. However, they suffer from limitations that include failure to consider the comprehensive list of environmental determinants linked with financial distress. Given that financial distress is the result of long-term deficiencies in both environmental and organizational characteristics, it is important to study all those characteristics in the prediction of financial distress in acute care hospitals (Noh et al., 2006).
The aim of this study is to examine the effect of environmental and organizational determinants on financial distress of acute care hospitals using a comprehensive measure of distress. Hospitals being open systems interact with and are affected by their external environments (Jeffrey Pfeffer, 2005). Thus, it is critical to include both environmental and organizational determinants to predict financial distress in acute care hospitals.
Although research on financial distress in acute care hospitals is available, few published studies involve both environmental variables and organizational variables. Compared with the previous studies that include hospital samples at least a decade old, the current study will examine financial distress in acute care hospitals between 2005 and 2015. Early studies were also limited by small samples of hospitals. Previous studies focused mainly on organizational-level factors, limiting their ability to provide the entire picture of determinants of financial distress in acute care hospitals. The current study will provide new insights into financial distress and employ a broader range of possible factors in a multivariate model. This is also the first study to apply Olshon O-score model as a measure of financial distress in acute care hospitals. From the perspective of resource dependency theory (RDT), this study provides an analysis of the determinants of hospital financial distress by using an exhaustive set of environmental and organizational factors. Findings of the study may provide policy makers new insights about the factors that facilitate financial distress in hospitals. For hospital managers, this information can help predict the hospitals that are most susceptible to financial distress.
- Conceptual Framework
Resource dependency theory (RDT)
This study will apply RDT, which is based on the premise that “organizations survive by acquiring and maintaining resources from their environment” (J Pfeffer & Salancik, 1978). All organizations actively try to obtain critical resources from the environment to ensure continued existence (Kotter, 1979). However, resources that are critical for organizations to function are often scarce and not equally distributed (Pennings, 1975). The ability to acquire critical resources can be challenging because some critical resources are controlled by other entities/organizations (J Pfeffer & Salancik, 1978). Additionally, an uncertain environment can add to the challenge of acquiring those critical resources. Uncertain environment refers to the fluctuations in resource availability that make it challenging for organizations to acquire critical resources (Ulrich & Barney, 1984). Thus, organizations must be proactive to reduce any uncertainty in their environment that may affect the availability of the resources (Proenca, Rosko, & Zinn, 2003).
In addition, RDT posits that organizations seek to acquire critical resources to reduce interdependence on other organizations. RDT views the organization as being an active participant in its fate (Scott & Davis, 2007). RDT has an open systems approach to environmental contingency, in which it places the burden of organizational success or failure on the organization. As a result, organizations constantly engage with their environment; the various strategies they undertake to access critical resources will change the environmental landscape (Aldrich & Pfeffer, 1976). Dess et al (Dess & Beard, 1984) have conceptualized environment into three constructs: environmental munificence, environmental dynamism, and environmental complexity. The following sections will explain each of these constructs in the context of financial distress in acute care hospitals as well as their hypothesized relationships.
Munificence. Munificence is attributed as the availability of resources in the environment that will support sustained stability or growth to the organizations (Sutcliffe, 1994). The scarcity of resources in the environment can be challenging for organizations and may affect their performance. Contrarily, a high degree of munificence can provide a necessary buffer to the organizations in the form of financial and professional slack that will help organizations facilitate both stability and growth (Andrews & Johansen, 2012). Munificence has been operationalized as overall population growth, per-capita income, growth rate of the elderly population, growth in total employment, growth in total sales, and the number of physicians in the county (Alexander, D’Aunno, & Succi, 1996; Dess & Beard, 1984; Trinh & Begun, 1999; Wiersema & Bantel, 1993).Previous research by Friedman et al (Friedman & Shortell, 1988) has suggested that there is a positive association between environmental munificence (deflated family income) and financial performance (operating margin) in hospitals. Munificence as operationalized by unemployment rate have shown to be positively associated with financial distress among not-for-hospitals located in metropolitan statistical areas (MSAs) (Kim, 2010).
In the context of this study, financial distress in the hospitals can be a result of the scarcity of resources in the environment. An environment bereft of necessary resources will fail to provide sufficient revenues to the hospital, leading to deterioration in their finances and eventually financial distress. Contrarily, hospitals operating in more munificent environments can focus on improving their efficiency rather than spending resources to acquire other resources. This leads to my first hypothesis: Hypothesis 1: Hospitals operating in more munificent environments are less likely to be financially distressed.
Dynamism. Dynamic environment is characterized by rapid changes in an external environment that may introduce uncertainty in an organization and affect its performance (Yeager, Zhang, & Diana, 2015). In healthcare, those rapid changes refer to various market elements like change in poverty level, changes in the unemployment rate, changes in population rate and HMO penetration (Menachemi, Mazurenko, Kazley, Diana, & Ford, 2012; Menachemi, Shin, Ford, & Yu, 2011). Prior research have shown that there is a negative association between dynamic environment and financial performance of the firm(Robert Baum & Wally, 2003).
A high degree of dynamism will limit an actor’s ability to access the environment accurately (Li & Simerly, 2002). Inaccurate assessment of the environment may impact planning and process of resource acquisition. Thus, operating in a more dynamic environment can be more challenging for hospitals. This leads to my second hypothesis:
Hypothesis 2: Hospitals operating in more dynamic environments are more likely to be financially distressed.
Complexity: Environmental complexity refers to a degree of heterogeneity (Dess & Beard, 1984). A heterogeneous environment contains diverse type and large number of entities that the organization needs to interact with to access critical resources. Previous studies have shown that firms operating in a complex environment have poor financial performance (Goll & Rasheed, 2004; Robert Baum & Wally, 2003). Kim et al (Kim, 2010) found an association between high environmental complexity (industry concentration) and financial distress in a study of not for profit hospitals. Another study in not for profit hospitals revealed that higher competition was associated with poor financial performance (Brecher & Nesbitt, 1985).
Complex environments require firms in the same industry to compete for similar critical resources, making it challenging to access those resources. Additionally, competing for those key resources will drain organizational resources and reduce their efficiency. This leads to my third hypothesis:
Hypothesis 3: Hospitals operating in more complex environments are more likely to be financially distressed.
In addition to environmental factors, many organizational level variables may be associated with financial distress in acute care hospitals. In the case of this study, I believe that financial distress will vary further as a function of different types of organizational characteristics. Therefore, I will distinguish between two types of organizational characteristics: structural and operational.
Structural characteristics: Structural characteristics refers to relatively stable and enduring properties of an organization (Hearld, Carroll, Hearld, & Opoku-Agyeman, 2016). Organizations with certain structural characteristics may be associated with better financial performance compared to their competitors. Organizational size, for example, is positively associated with financial performance (Goll & Rasheed, 2004). Larger hospitals have shown to be positively associated with financial performance (Brecher & Nesbitt, 1985; Landry & Landry III, 2009). Larger organizations achieve economies of scale and compete better compared to smaller organizations (Hall & Weiss, 1967). System affiliation may also be related to financial performance. Hospitals join systems in order to gain access to capital, management expertise, and wider geographical access (Fottler, Schermerhorn, Wong, & Money, 1982). Empirical evidence has shown that lack of system affiliation is associated with financial distress in hospitals (Liu et al., 2011). Also, ownership status may affect financial performance in the hospitals. Not-for-profit hospitals have less financial resources as compared to for-profit hospitals (Yeager et al., 2015). Another variable potentially related to financial performance is teaching status. Teaching status can bring prestige to the hospitals and access to resources through endowment funds. Teaching hospitals have shown to have large endowments in comparison to non-teaching hospitals (Rosko, 2004). Teaching status has shown a positive association with operating margins in acute care hospitals (Tennyson & Fottler, 2000). These considerations lead to my fourth hypothesis:
Hypothesis 4: Hospitals’ structural characteristics are associated with its financial distress.
Hypothesis 4a: Hospitals’ size is negatively associated with its financial distress.
Hypothesis 4b: Hospitals’ affiliation with a system is negatively associated with its financial distress.
Hypothesis 4c: Hospitals’ FP ownership status is negatively associated with its financial distress.
Hypothesis 4d: Hospitals’ teaching status is negatively associated with its financial distress.
Operational characteristics: Operational factors refer to indicators of the volume of services and efficiency in the hospital. Operational factors may affect financial performance in hospitals. For example, occupancy rate is associated with utilization of hospital services. Occupancy rate was found to be negatively associated with financial distress in a study of rural hospitals in Pennsylvania (Trussel et al., 2010). An aging facility can also affect the financial performance of a hospital. Hospital with older plant and equipment is suggestive of the need for capital improvements. However, hospitals with newer plant and equipment attract both patients and physicians as quality is associated with newer facilities (Cleverley & Harvey, 1992). Another two operational factors associated with financial distress in hospitals include payer mix and case mix index (Gloria J. Bazzoli & Cleverly, 1994). Decreasing reimbursements for Medicaid and Medicare can affect the financial performance of hospitals that have higher payer mix. In case mix index, it is reflective of the complexity of treatment and intensive services that may increase the costs of providing services (McCue, Thompson, & Kim, 2015). These considerations lead to my final hypotheses: Hypothesis 5: Hospitals’ operational characteristics are associated with its financial distress. Hypothesis 5a: Hospitals’ occupancy rate is negatively associated with its financial distress. Hypothesis 5b: Hospitals’ payer mix is positively associated with its financial distress. Hypothesis 5c: Hospitals’ age of the plant is positively associated with its financial distress. Hypothesis 5d: Hospitals’ case mix index is positively associated with its financial distress.
This study will use longitudinal panel data from 2005 to 2015 of US acute care hospitals.
Data Sources: Five secondary data sets will be used in the study. First, the American Hospital Association (AHA) Annual Survey will provide general organizational information about hospital data from 2005-2015. Second, Medicare cost reports (CMS) will provide the financial information of all the hospitals between 2005-2015. Third, Area Health Resource File (AHRF) will be included to examine environmental characteristics of hospitals. Fourth, Local Area Unemployment Statistics (LAUS) dataset estimates monthly and annual averages of total unemployment and total employment rates at various geographical levels. Lastly, the National Conference of State Legislatures (NCSL) will be used to find each state’s regulations as they relate to the certificate of need (CON) laws. The AHA data sets will be matched with the hospital list using Medicare provider numbers. AHRF data will be matched to all hospitals using county identifiers and states will be coded for CON status using NCSL dataset.
Study population: The sample for this research will consist of all acute care community hospitals in the United States from 2005 through 2015. Exclusion criteria will include critical access hospitals, specialty hospitals and hospitals with incomplete Medicare cost report information in the study period. Approximately 40,000 hospital year observations will be studied during the time period.
The dependent variable represents a dichotomous variable that identifies if a hospital in a given year is in financial distress (0=No; 1=Yes). Financial distress will indicate financial condition of the hospital, and it can be used to forecast the probability of a hospital fulfilling its debt obligations. The Ohlson O score will be used to examine whether or not a hospital was in financial distress. The Ohlson model uses logit regression approach to examine multiple financial ratios, such as solvency, liquidity, and profitability simultaneously to predict the likelihood of a firm’s bankruptcy (Ohlson, 1980). The process of calculating the logit function will be summarized below.
Logit Analysis Equation
Ohlson = – 1.3 – 0.4X1 + 6.0X2 – 1.4X3 + 0.8X4 – 2.4X5 – 1.8X6 + 0.3X7 – 1.7X8 – 0.5X9
X1 refers to the size of the company. Smaller organizations have greater likelihood of failure as compared to larger organizations.
X2 is defined as the ratio of total liabilities to total assets. It is an indicator of an extent to which the liabilities of the company are covered by its assets. A high total liability to total assets ratio indicates that the organization have higher risks.
X3 is defined as the ratio of net working capital to total assets. This ratio refers to working capital adequacy and measures solvency in the short term. Net working capital is defined as the difference between current assets and current liabilities.
X4 is defined as the ratio of current liabilities to current assets. It reflects the extent to which current asset coverage is available to meet the current liabilities of the company. A high current liability to current assets ratio would indicate a poor solvency condition.
X5 is a dummy variable for correction of extreme leverage. A value of 1 indicates higher likelihood of bankruptcy(Ohlson, 1980).
X6 is defined as Net Income to Total assets. This ratio indicates the overall profitability of the organization.
X7 is defined as ratio of funds from operations(FFO) to total liabilities. This ratio indicates the operating profit coverage for total liabilities. It measures the operating performance of the company.
X8 is a dummy variable for correction of continuing profits. A value of 1 indicates higher likelihood of bankruptcy.
X9 is measured as a change in income over the preceding period.
Following Ohlson’s approach, I will classify a hospital as being in financial distress if its O-score is more than 0.5; the hospital is not in financial distress if its O-score is less than 0.5(Ohlson, 1980).
Table 1: Ohlson O-Score Variables
|Name||Definition||Operationalization in Hospitals|
|Size||X1=Log of Total assets/log of GNP price level index||Log (Total assets/GNP Price level index)|
|Leverage Ratios||X2= Total Liabilities/Total Assets||Total liabilities(TL)/Total assets(TA)|
|X5= 1 if total liabilities>total assets,0 otherwise||X5= 1 if TL>TA,0 otherwise|
|Liquidity Ratios||X3= Working Capital / Total Assets||Current assets – Current liabilities / Total assets|
|X4=Current Liabilities/ Current Assets||Current liabilities/Current assets|
|X7 = Fund provided by operations / Total Liabilities||(EBITDA) / Total liabilities|
|X8=1[1 if net income is negative for last two years, 0 otherwise]|
|Profitability Ratios||X6 = Net Income / Total Assets||Fund Balance / Total assets|
|X9= (NIt-NIt-1)/ (|NIt|-|NIt-1|) where NI=net income for recent period and t is number of years|
Independent Variables All variables are presented along with their sources in Table 2. Independent variables broadly represent environmental and organizational determinants of financial distress in hospitals. Consistent with RDT, variables are chosen to represent three constructs of the external environment of hospitals (i.e., munificence, dynamism, and complexity).
Environmental munificence (hypothesis 1) will be operationalized through four environmental variables: SES composite, percentage population above 65 or older, physician supply and the geographic location of hospitals. A composite SES measure will be used as the main indicator of the socioeconomic status of the county where a hospital is located. Four standardized variables representing dimensions of the socioeconomic status of the resident community ([a] percentage of county residents below federal poverty level, [b] percentage of county residents with less than a high school education, [c] percentage of county residents that were unemployed, and [d] percentage of county residents that were uninsured) are added to create a single summary score analogous to indices of neighborhood socioeconomic status developed by Winkleby et al (Winkleby & Cubbin, 2003). Percentage of county population 65 and oldersimply refers to all individuals who are 65 and older divided by the total population of the county. Physician supply will be measured by number of physicians per capita. Hospital location will be categorized as a rural location (compared with urban).
Environmental dynamism (hypothesis 2) which represents the degree of instability will be measured by the percentage change in population, the percentage change in unemployment, HMO penetration and PPO penetration.
Environmental complexity (hypothesis 3) will be measured using three variables: the Herfindahl–Hirschman index (HHI), (HHI)2 and presence of Certificate of need (CON) laws. HHI is commonly used measure for the level of health care competition in the market.
For structural variables (hypothesis 4), hospital size will be measured by the number of hospital staffed beds; for ownership, I will categorize hospital ownership as nonfederal government, nongovernment not-for-profit, and for-profit. The variable of health care system membership will be classified based on a study by Bazzoli et al (Gloria J Bazzoli, Shortell, Dubbs, Chan, & Kralovec, 1999). Three categories from this taxonomy will be used to indicate health care system membership: centralized, decentralized, and no system affiliation. Teaching status will be categorized as a binary variable and coded “0” if the hospital has a teaching status and “1” if it does not have a teaching status.
For operational variables (hypothesis 5), occupancy rate will be measured as a ratio of total inpatient days to the total number of beds*365 (Trussel et al., 2010). Payer mix will measure the proportion of Medicaid and Medicare patients. It will have three categories. The first group will be Medicaid mix which is the ratio of the number of Medicaid inpatient days to total inpatient days. The second group will be Medicare mix which is the ratio of the number of Medicare inpatient days to total inpatient days. The third group will be a reference group of patients neither covered by Medicare nor Medicaid.
Table 2: Independent variables used in analysis
|Variables||Data Source||Type||Predicted relationship to financial distress|
|Percent of population 65 years of age and older||AHRF||Continuous||_|
|Number of active physicians per 1000 population||AHRF||Continuous||_|
|Urban location (compared with rural)||AHRF||Binary||_|
|Growth in Population||AHRF||Continuous||+|
|County unemployment rate||LAUS||Continuous||+|
|Age of the plant||AHA||Continuous||+|
Case mix index (CMI) will be measured as a ratio of DRG weights for all Medicare discharges to the number of discharges. Age of plant will be measured as a ratio of accumulated depreciation to depreciation expenses.
The unit of analysis will be a hospital. Descriptive statistics will be used to describe the prevalence of financial distress in acute care hospitals over the study period. Given the large number of covariates, data will be checked for multicollinearity. A logistic regression, with financial distress as the dichotomous dependent variable and robust clusters at the provider ID, with state and year fixed effects will be used for this analysis. State fixed effect will account for state-level differences and year fixed effects will account for time trends. As the data will have observations from the same hospitals repeatedly over time, the observations may be correlated at the facility level. To control for these interdependencies, robust clustering will be used at the provider level to produce robust standard errors for the estimates. This model will attempt to examine organizational and environmental determinants, on the log-odds of financial distress. Odds ratios will be reported and statistical significance will be considered at the alpha level of p < .05, p < .01, and p < .001. Stata 13.1 will be used for all the analysis.
The logistic regression equation is presented below. The dependent variable is the log odds of the probability of the facility experiencing financial distress. All the independent variables are represented. The general model specification for the “i”th is the hospital, the “j”th is the state and the “t” is the year.
Logit (π) = log (π / (1-π) = β0 + β1( SES Composite jt) + β2( Number of people above 65 jt) + β3( Number of active physicians per 1000 jt) + β4 ( Urban it) + β5 (Growth in population jt) + β6( Unemployment rate jt) + β7 ( HMO Penetration jt) + β8 ( PPO Penetration jt) +β9 ( Certificate of need jt) + β10(HHI)+ β11(HHI)2 +β12(Size it ) + β13(System affiliation it) + β14(Ownership it) + β15(Teaching status it) +β16(Occupancy rate it) + β17(Payer mix it) + β18(Case Mix it) + β19(Age of the plant it) + β20 ( State Dummy Variables jt)+β21 ( Year Dummy Variables jt) + ų (Year t) + ų (State j) + ψi+ Ɛit
Π = the probability of financial distress / Ψ – robust cluster at the facility level / Ɛ – Error term
There are several issues that could arise with this paper. First, the dependent variable is the product of previous statistical analyses. There is the inherent risk that an error made on the original statistical analysis could impact subsequent analysis. Secondly, as there are numerous factors that can impact an organization in the real world, it is not feasible to model all those factors. There was great effort to include all relevant variables and to use a sound theoretical basis for inclusion. All data used are secondary data that has been collected by others, so there is the risk of missing and/or inaccurate data. This is simply an innate risk of secondary data.
In summary, financial distress in hospitals is an issue of importance for practitioners and policy makers as hospital closures due to distress can impact access to care. To date, empirical examination of financial distress in acute care hospitals has been largely attributed to organizational determinants. This study aims to construct financial distress as a function of both environmental conditions and organizational attributes. Findings of the study may provide policy makers new insights about the factors that facilitate financial distress in hospitals and help them make policies that will stymie distress related hospital closures. For hospital managers, this information can help predict the hospitals that are most susceptible to financial distress.
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Proposed framework to explore environmental and organizational determinants of hospital financial distress
Hypothesis 4 (H4a, H4b, H4c and H4d)
Hypothesis 5 (H5a, H5b, H5c and H5d)
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