The differentiation between for-profit and nonprofit hospitals: another look.
Many aspects of the differences between hospital types have been studied. Previous research has examined differences in costs such as administrative costs, or the cost of Medicare, between FPs and NPs, while others have focused on profitability and additional measures of financial performance (Woolhandler & Himmelstein, 1997; Silverman et al., 1999). Most studies find FPs to be more efficient, but others find the NPs to be so, while others do not find any differences between them in terms of efficiency (Sloan & Vraciu, 1983; Silverman et al., 1999; Cutler & Horwitz, 2000).
The hospital industry continues to be in the forefront of the news as business and society struggle to contain unsustainably escalating healthcare delivery costs. Whether NP hospitals can justify their tax exempt status when the Wall Street Journal is running front page headlines stating "Nonprofit Hospitals, Once For the Poor, Strike It Rich" (Carryrou & Martinez, 2008) remains to be seen.
The purpose of this study is to examine a narrow, well defined sample of hospitals so an in-depth analysis of the differences between FPs and NPs can be achieved. Most prior research examining differences in hospital ownership structure have focused on large, cross sectional samples. Horwitz and Nichols (2007) state that hospital behavior needs to be examined in the context of the markets they serve. They find that hospitals operate in markets with varied populations and competitive characteristics and that these characteristics influence hospital behavior. Not only do differences in markets effect hospital operations but differences in regulatory environments will impact hospital behavior. Regulation of the hospital industry varies widely from state to state. For example, California has restrictions on reimbursement rates and nursing staff ratios.
This study takes a different approach than previous studies by using a matched sample of 24 FP and 24 NP hospitals in urban California. This study contributes to the literature by applying a matched sample design to control for size, location, and regulatory variations to obtain a more reliable assessment of the possible differences in hospital resource utilization and financial measures between FP and NP hospitals. By focusing on urban California hospitals, I attempt to mitigate differences in markets and regulatory environments and their impact on hospital operations to more closely examine if there are differences in the included variables. This level of control of potentially intervening variables can not be achieved as effectively in large, cross-sectional studies. This study thus provides one more step in examining the effect ownership structure has on hospital operations. The results of this paper provide information useful for the advancement of practice by documenting differences between FP and NP hospitals in a controlled setting.
Economic theory states that FP hospitals will operate more efficiently due to the profit motive while NP hospitals will have higher quality of care. Many studies have examined this theory. Some research suggests that the FP form of ownership structure achieves greater productive efficiency than the NP form. Herzlinger and Krasker (1987) compare the costs and returns between six FP hospital chains and eight NP chains. Their study supports the theory that FP hospitals generate better results for society by being more efficient, invest their earnings in renewing equipment, and offer a broader range of services to patients. Cutler and Horwitz (2000) examine the conversion of NP hospitals to FP status and find that FP hospitals can cut costs and provide capital or relieve debt burden without reducing quality or cutting back on access to the poor when the conversion takes place. Their study indicates that quality is not compromised in an FP setting. Kessler and McClellan (2002) analyze data on medical expenditures and health outcomes of Medicare beneficiaries for heart attacks and find that the FP ownership form has important benefits for medical productivity.
On the other hand, other studies have found NP hospitals to be an equally or more efficient ownership form. Duggan (2000) reviews the percentages and changes of Medicaid and uninsured patients in different ownership forms over several years. He finds NP hospitals have costs and/or qualities similar to that of FPs. Duggan rejects the theory that NPs are less responsive to financial incentives than FPs. In addition, Silverman et al. (1999) find that FP hospitals have higher Medicare costs than NPs. Woolhandler and Himmelstein (1997) find that FP hospitals have higher administrative costs than NPs. In another study that looks at a hospital's conversion from NP to FP status, Picone, Chou and Sloan, (2002) find that NP hospitals provide better quality than FP hospitals.
The main distinction between FP hospitals and NP hospitals, is what they do with their net earnings (Sloan, 2000). According to the Internal Revenue Service, an organization will not be considered NP if its net earnings inure to the personal benefit of individual persons or shareholders in the corporation. Profits can not be distributed to either the individual equity holders or individuals who exercise control over the firm if the hospital wants to maintain its NP status (Duggan, 2000; Sloan, 2000). Therefore, the net earnings of NP hospitals theoretically go to investments or other important activities, such as uncompensated care, teaching, and medical research (Cutler & Horwitz, 2000). FP hospitals, on the other hand, must satisfy their owners in addition to their other activities.
NP hospitals constitute the majority of American hospitals. NP hospitals are NP because they choose to be. "There is no financial reason why they cannot be organized on a for-profit basis" (Herzlinger & Krasker, 1987). Once they choose to be NPs, they can enjoy some benefits, such as tax-exempt privileges; however, they also need to meet the nondistribution of earnings requirements of being NP, as previously stated. These requirements may make FPs and NPs operate differently.
The data used in this paper come from the American Hospital Association Guide to the Health Care Field 2000-2001 and the 2001 Profiles of US Hospitals. The purpose of this study is to examine a narrow, well defined sample of hospitals to control as many potentially intervening factors that are known to impact hospital behavior as possible, so an in-depth analysis of the differences between NPs and FPs can be achieved. When determining the sample, geographical considerations (among others) need to be made, since different states have different regulations. Many studies, Younis and Forgione (2005) among others, have found location to be an important determinant in explaining hospital profitability. This study focuses on a single state, which allows for control of variations due to differences in location and the regulatory environment. This study uses a matched sample of FP and NP hospitals in the state of California because of the large number of FP hospitals that exist in the state. There are 143 FP hospitals in California, which provides a large potential pool for the sample.
Norton and Staiger (1994) find that patient populations differ between urban and rural hospitals. Later studies, such as Younis (2003), found that these differences persist. Therefore, it is necessary to control for urban/rural classification (1). There are relatively few FP hospitals in rural areas; (for example, California has two in 2001) therefore, only urban hospitals are used in this analysis.
In addition, a hospital's specialty has been shown to affect hospital behavior. Psychiatric and rehabilitative hospitals inherently attract a specific group of patients. Additionally, many studies have found teaching hospitals to differ from nonteaching hospitals in terms of profitability (Younis et al., 2003, Younis & Forgione, 2005). Therefore, teaching, psychiatric and rehabilitative hospitals, whether FP or NP, are not included in the sample. Of the 143 FP hospitals in the state of California in 2001, 24 are general, urban hospitals with complete data in both data sets.
Almost all studies have shown that size is an important determinant in hospital behavior. In addition, Horwitz and Nichols (2007) have shown that hospitals react to their markets. Ideally, the 24 matching hospitals should operate in the same markets as their NP hospital counterparts. For example, a general, FP hospital in Los Angles should be matched with a general, NP hospital in Los Angles to mitigate differences between FP and NP hospitals caused by differences in the markets they serve. Unfortunately, matching FP and NP hospitals by specific MSA and size is not possible because of data constraints, therefore the hospitals are matched on size. The fact that all hospitals in the sample are urban, California hospitals may lessen the differences in markets, and the influence of markets on behavior.
After selecting the 24 FP hospitals, 24 NP hospitals are matched based on the number of total beds as a surrogate for size. The number of admissions, which shows the number of patients accepted for inpatient service during a 12-month period, is the secondary matching criterion. The NP hospitals are also urban, nonteaching, general hospitals. The final sample of 24 FP and 24 NP hospitals controls for location, urban/rural classification, differences in state regulations and size differences. By lessening the impact of these factors, a better analysis of the remaining differences can be made.
The sample has eight hospitals with less than 100 beds; 20 hospitals have between 101-200 beds, 14 have between 201-300 beds, and six have more than 400 beds. The average number of beds for NP and FP hospitals is 199 and 198 beds, respectively. The average admissions for each type of hospital are 7,050 and 6,838 patients. These figures indicate that the sample is closely matched.
The variables used in this analysis expand on Becker and Potter's (2002) framework of examining hospital efficiency in the context of social responsibility. Becker and Potter use two efficiency and four social responsibility measures in their analysis. Their efficiency measures are total expenses per bed and full time equivalents (FTES) per bed. This paper expands on these measures to include additional variables: operating revenue per bed, occupancy rate, average length of stay, days in receivables and average payment period.
Examining expenses per bed as in Becker and Potter (2002) tells only part of the story. A hospital may be able to keep expenses low but without the ability to generate revenue, it will not experience financial health. Including the variable operating revenue per bed helps measure if there is a difference between FP and NP hospital's ability to generate operating revenue and thus overall potential profitability. Operating revenue, rather than total revenue, is used because it reduces the differences between hospitals caused by ancillary activities.
Occupancy rate and average length of stay are common measures of efficiency which provide a rough measurement of how well hospitals are utilizing their primary resource; bed capacity. The other two measures used (average pay period and days in receivables) have not been previously used when examining efficiency. These measures emphasize the cash flow policies and experience of a hospital. Cash flows provide a basis for decision making and for evaluating risk (Hendriksen, 1982). Many accounting studies have shown the relationship between cash flows and profitability. Cash flows are also a good predictor of bankruptcy. This study will examine whether FP or NP hospitals are better at managing their cash flows. Days in accounts receivable measures how long it takes a hospital to receive its net patient revenue. The fewer the days in accounts receivable, the faster hospitals get cash from patients, insurers, governments and other sources, and the lower the probability the receivables will become bad debts. The lower the Days in Receivables, the more efficient a hospital is in generating cash. The other cash flow variable, average payment period, measures the average amount of time that passes before current liabilities are met. This measure indicates the hospital's ability to pay their current debts in a timely manner. Both days in accounts receivable and average payment period represent accounting efficiency and how well hospitals manage their receivables and payables, and thus their cash. These variables, when viewed together provide a picture of cash management policies and cash flows. They indicate whether a hospital collects cash quicker than it pays it out, or whether the opposite is true--which puts it in a detrimental cash flow position. These variables will indicate if there are differences in efficiency between the two types of hospitals.
Becker and Potter (2002) use four measures of social responsibility: Medicaid percentage, outpatient visits to total patient days, and two measures based on community assessments. This analysis uses six measures of social responsibility: Medicare percentage, Medicaid percentage, case-mix index, percentage of outpatient revenue to operating revenue, number of outpatient visits scaled by beds and payroll per bed. Medicare percentage and case-mix index are included, along with the Medicaid percentage used by Becker and Potter, to examine if the hospitals have the same patient mix. Because hospitals that treat more seriously ill, older, or poorer patients may appear less efficient than they really are. These additional measures give a clearer picture of social responsibility by examining whether FP hospitals "cherrypick" patients by admitting healthier, younger or more affluent patients.
Becker and Potter (2002) use outpatient visits to represent a measure of social responsibility. They state that hospitals prefer inpatient to outpatient treatment because revenues generated are higher for inpatient treatment. Therefore, they argue,
hospitals that provide large levels of outpatient treatment show greater social responsibility by foregoing greater inpatient revenue to bring services to a wider range of patients through outpatient procedures. But outpatient treatments may be attributable to the lower costs of laparoscopic surgery techniques. Considering this argument, in addition to examining outpatient visits per bed, this paper examines the percentage of revenue that comes from outpatients. This gives a clearer picture of how important outpatient treatment is to the finances of the hospital. Whether outpatient services are a measure of social responsibility or efficiency, they are an important component of a hospital's operations, and therefore they are included in this analysis.
One other measure of social responsibility examined in this paper regards personnel. According to the Wall Street Journal, healthcare employment is becoming a dominant factor in the economies of many towns (Dougherty, 2008). For many towns, healthcare is replacing the jobs lost to manufacturing. This study includes the variable payroll per bed as a first pass to see if there is a difference between FP hospitals and NPs regarding pay. The variable definitions are summarized as follows.
EFFICIENCY VARIABLES: FTEs per Bed = Number of Full-time Equivalent (FTE) Personnel / Number of Beds Total Expenses per Bed = Total Expenses / Number of Beds Operating Revenue per Bed = Operating Revenue / Number of Beds Net Income per Bed = Net Income / Number of Beds Occupancy Rate = Average Daily Census (2) / Number of Beds Average Length of Stay = Total Acute Care Inpatient Days / Total Acute Care Discharges Average Payment Period = (Total current Liabilities x 365) / (Total Operating Expenses- Depreciation) Days in Accounts Receivable = Net Patient Accounts Receivable x 365 / Net Patient Revenue SOCIAL RESPONSIBILITY VARIABLES: Medicaid % = Total Medicaid Acute Care Discharges / Total Acute Care Discharges Medicare % = Total Medicare Acute Care Discharges / Total Acute Care Discharges Case-Mix Index = Using DRGs as a measure of relative complexity of treatment, as defined by Medicare Outpatient Revenue Percentage = Outpatient Gross Revenue / Total Gross Patient Revenue Outpatient Visits per Bed = Number of Outpatient Visits / Number of Beds Payroll per Bed = Payroll Expense / Number of Beds
Since the primary purpose of this paper is to examine if there is a difference between NP and FP hospitals, the variables are examined using a two-tailed differences in means procedure with the assumption of unequal variances in the variables. The results are presented in Exhibit 1.
The results for operating revenue and expense per bed are mixed with regard to efficiency, with NPs having higher revenues but FPs having lower expenses. If FP hospitals are more efficient, they should have a higher level of operating revenue and lower expenses. These results indicate that NPs are able to generate more operating revenues from their resources but they incur higher costs in doing so. To further examine these results, net income per bed is analyzed. Using the term, net income, in an NP setting may be misleading but it is used here solely to measure the difference between revenues and expenses. The results find no differences between the average net income per bed of an FP and NP hospital. This indicates that the FP's ability to generate revenue, and the NP's ability to cut costs, does not improve the institution's overall profitability.
Other measures of efficiency, based on Becker and Potter (2002) are the measures of staffing. There is no significant difference in the FTEs per bed, but payroll per bed (a social responsibility variable) is significantly higher in NP hospitals. These results indicate that NP hospitals have similar staffing levels but the pay is higher in NP hospitals. Unfortunately, the data do not permit a refinement of the personnel variable. Thus interpreting the results is difficult since it is not possible to distinguish between the CEO compensation and skilled or unskilled staff. These results could indicate that NPs pay their executives extremely well, or that they pay everyone more. Executive compensation in the NP arena has drawn increasing scrutiny by the Internal Revenue Service (Carryrou & Martinez, 2008). Although some research in the area of executive compensation for NPs has been performed (Baber et al., 2002; Fisman & Hubbard, 2005), more research is needed in this politically controversial area.
The two variables examining cash flows in this study are days in receivables and average payment period. Both of these variables are significantly different between FP and NP hospitals and provide an interesting insight into the cash management policies of hospitals. The shorter time period it takes NPs to collect their receivables indicates that they have efficient billing departments. The faster the accounts receivable are collected, the more likely they are to be received. NPs are significantly quicker at collecting their receivables. On the other hand, FP hospitals are significantly quicker at paying their current liabilities by having a lower average payment period. Therefore, FP hospitals are slower than NP hospitals in collecting cash but faster at paying bills. Thus, on average, an FP hospital receives cash from payers in 80 days, but pays its bills in 51 days. The NP hospitals receive their cash in 68 days and pay their bills in 78 days. These results indicate that FP hospitals have significant room for improving their cash collection practices. Cash management is becoming a major issue in the hospital industry as more hospitals are demanding payments in advance (Martinez, 2008). Improvements in cash management may be one way for FP hospitals to improve their efficiency.
Two of the measures used by Becker and Potter (2002) to measure social responsibility examine outpatient usage and treatment of Medicaid patients. This study expanded on their Medicaid variables to include Medicare and case-mix to get a finer measure of whether there is a difference in the types of patients the two types of entities are treating. The variables case-mix index, Medicare percentage, and Medicaid percentage, are not statistically different between the FP and NP hospitals in the sample. These results help assure that the FP and NP hospitals in the sample are structurally similar and that there is no underlying reason for one type of hospital to appear more efficient than the other type. It also indicates that the FP hospitals in this sample do not "cherry-pick" patients. These results, however, may be because urban, Californian hospitals face enough competition that "cherry-picking" is not feasible. These results supports the Horwitz and Nichols (2007) study which says that the market a hospital operates in will have an impact on the types of patients and resources offered.
FP and NP hospitals have similar patient loads (occupancy rate) and the patients stay in the hospital for similar periods of time (average length of stay). These results indicate that ownership form has no impact on how well a hospital utilizes their bed capacity. Both types of hospitals treat similar patients for a similar amount of time. These results are contradictory to previous research which has found ownership control to be associated with occupancy, Medicare, and Medicaid patients (Younis et al., 2003; Younis & Forgione 2005). However, these results may not dispute the previous findings but rather indicate that a good matching is achieved and that similar FP and NP hospitals are being compared in this analysis.
Becker and Potter (2002) use outpatient resource utilization as a measure of social responsibility. The results of this paper show that NP hospitals have a significantly higher level of outpatient visits and a higher percentage of their revenues coming from outpatients. In the past 10 years, there has been a major effort to treat more patients on an outpatient basis because of this cost effectiveness. Also, changes in medical and surgical technology have allowed many more procedures to be performed on an outpatient basis. NP hospitals seem better at generating outpatient revenue while keeping their occupancy rates at a level equal to FP hospitals. These results indicate NPs are socially responsible by offering numerous outpatient procedures.
Most previous studies use a cross sectional analysis to examine differences between the behavior of FP and NP hospitals. This study, by using a carefully matched-sample design, allows a more meticulous analysis to be made of the differences between hospital forms, while controlling as much noise as possible. Of course, using a narrow matched sample introduces challenges of its own, specifically the ability to generalize the results, which is a limitation of this study. Another limitation of this study is because the sample contains only urban hospitals, competition may be limiting the differences between hospitals. Whether these results hold for hospitals in less competitive environments remains to be tested.
The central focus of this study is to investigate further if differences exist between FP and NP hospitals. The results of this study, like the results of previous studies, are mixed. The matched-pair sample results indicate that there are no major differences between FP and NP hospitals in the types of patients treated and the length of time it takes to release a patient. Medicare, Medicaid or the more complex patient cases (as measured by case-mix) are not more likely to favor FP or NP hospitals. Both types of hospitals are comparably efficient in filling their hospitals and treating their patients in a similar amount of time. This, in and of itself, is surprising and contradictory to previous studies. Previous research has found different percentages of Medicare and Medicaid patients treated by FP and NP hospitals (Younis & Forgione, 2005). The results presented indicate that urban hospitals in California compete and treat the same types of patients. Because the sample is limited to urban hospitals, these results support Horwitz and Nichols' (2007) conclusion that competition is an important factor in determining hospital behavior, and thus may not be generalizable to suburban or rural settings.
The results also indicate that NP hospitals utilize outpatient treatment much more than FPs. Treating patients on an outpatient basis is desirable for society because of the cost savings compared to inpatient treatment. Over the past several decades, the hospital industry has tried to shift more patient care to the outpatient category. These results indicate that NP hospitals have been more effective in achieving that goal. In addition, one of the purposes of DRG-based prospective payment is to give hospitals incentives to reduce costs. The results indicate that FP hospitals have been better at reducing costs per bed than NP hospitals have. However, neither the non-profit hospitals' increased level of revenues, nor the FP hospitals' lower level of expenses, has translated into higher profitability.
The most interesting result is the difference in cash flows between the two organizational types. Both measures of cash flows are significantly different. The results show that non-profit hospitals are good at managing their cash flows by generating cash quicker than they spend it. The same was not observed in the FP hospitals. The FP hospitals spend money quicker than they collect it, which puts them in a potential negative cash flow position. This is a significant finding that has implications for FP hospital management.
The results contribute to the previous research by indicating no greater efficiency of one hospital ownership form over the other. This is an important issue, especially as a higher and higher percentage of the US GDP is being spent in the healthcare industry. If one ownership form is more efficient than the other, there would be great incentives to reward and promote that ownership form. Based on this study's results, ownership form does appear to have a significant impact on the operations of a hospital, but one form does not appear to be superior to the other.
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(1) The rural or urban areas are defined by the US Bureau of the Census on the basis of its location within or outside a metropolitan statistical area (MSA).
(2) Average Daily Census: average number of inpatients receiving care per day during the 12-month reporting period.
Address for correspondence: Catherine Plante, 320 McConnell Hall, Whittemore School of Business & Economics, University of New Hampshire, 15 College Road, Durham, NH 03824-3593 USA, email@example.com.
University of New Hampshire
EXHIBIT 1 RESULTS EFFICIENCY: Type Mean Median Std. Dev. Min. Max. FTEs per Bed FP 3.7 3.4 1.7 2 10.3 NP 4.5 4.2 1.8 2 9 Total Exp. FP 339.4 313.8 142.3 135.7 809.5 per Bed NP 449 427.4 164.4 184.4 873.6 Oper. Rev. FP 371.8 344.6 127.3 138 647 per Bed NP 470 467 169.7 187 952 Net Inc. per FP 32.5 40.7 70.3 -162 160.4 Bed NP 21 17.3 49.8 -92.9 110.5 Occup. Rate FP 49.2 47 14.2 25.4 76.9 NP 52.5 51.3 12.2 13.6 74.5 Ave. LOS FP 4.2 4 0.96 2.6 6.9 NP 4.1 4 0.91 2.6 6.5 Ave. Payment FP 50.7 37.2 45.2 23 231.1 Per. NP 77.7 75.6 32.4 38 195 Days in Accts. FP 80.4 75.6 28.7 46 164.2 Rec. NP 68.1 66.4 19.6 22.9 107.1 SOCIAL RESPONSIBILITY: Medicaid % FP 15 11.5 13.2 1 48 NP 14 11 9.5 1 33 Medicare % FP 30 30.5 12.8 11 62 NP 33.1 32 13.5 12 64 CMI FP 1.4 1.4 0.2 1.03 1.74 NP 1.4 1.3 0.18 1.13 1.79 OP Rev. % FP 27 26 8.7 14 44 NP 32.6 32.5 9.7 16 54 OP Visits per FP 378.4 261.9 358.1 109.1 1801 Bed NP 824.1 658.8 698.9 75.9 3229 Payroll per Bed FP 143.2 130.1 69.1 14.5 371.3 NP 178.4 175 62.4 79.1 347.6 EFFICIENCY: t (p-value) FTEs per Bed 2.01 (0.12) Total Exp. 2.02 ** per Bed (0.02) Oper. Rev. 2.02 ** per Bed (0.03) Net Inc. per 2.02 Bed (0.52) Occup. Rate 2.01 (0.39) Ave. LOS 2.01 (0.87) Ave. Payment 2.02 ** Per. (0.02) Days in Accts. 2.02 * Rec. (0.09) SOCIAL RESPONSIBILITY: Medicaid % 2.02 (0.77) Medicare % 2.01 (0.41) CMI 2.01 (0.72) OP Rev. % 2.01 ** (0.04) OP Visits per 2.03 *** Bed (0.009) Payroll per Bed 2.01 (0.07) Note: FP = for-profit, NP = nonprofit, LOS = length of stay, CMI = case-mix index, OP = outpatient *** = significant at the p [less than or equal to] 0.01 level ** = significant at the p [less than or equal to] 0.05 level * = significant at the p [less than or equal to] 0.10 level
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|Publication:||Research in Healthcare Financial Management|
|Date:||Jan 1, 2009|
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