Articles Who Really Profits from Not-For-Profits? Barbara Arrington and Cynthia Carter Haddock In a Harvard Business Review (1987) article, Herzlinger and Krasker suggested that not-for-profit hospitals do not return more benefit to society than doforprofit hospitals, and the authors questioned the legitimacy ofsocial subsidization of not-for-profits. Our article reports findings from an empirical reconsideration of the question, "Who profits from nonprofits?' We used hospital datafrom the same time period (1982) as that used by Herzlinger and Krasker; however, our investigation analyzed a larger data set (including both system and nonsystem hospitals) and used a different statistical technique (discriminant analysis). Our findings suggest that not-for-profits return more social benefit (e.g., in the areas of services provided, access to care, and involvement in professional education) than do forprofits. Like Herzlinger and Krasker, we find that for-profit hospitals may be more efficient than not-for-profits. We caution that public policy regarding social subsidization of not-for-profit hospitals should be made only after more intensive study and thoughtful consideration.
"Who profits from nonprofits?" asked Regina Herzlinger and William Krasker in an article in the Harvard Business Review (Herzlinger and Krasker 1987). Their study questioned whether not-for-profit hospitals achieve the social goals for which they are intended and for which they are subsidized by society (i.e., through their privileged tax-exempt status). Herzlinger and Krasker's findings suggested that not-for-profit health care organizations do not return more benefit to society than do for-profit hospitals and should not, therefore, be subsidized by society. The Herzlinger and Krasker article unleashed some aggressive criticism in a lengthy series of "Letters to the Editor" in the Harvard Address correspondence and requests for reprints to Barbara Arrington, Ph.D., Assistant Professor, Center for Health Services Education and Research, St. Louis University Medical Center, 3525 Caroline Street, St. Louis, MO 63104. Cynthia Carter Haddock, Ph.D. is Associate Professor in the Department of Health Services Administration, University of Alabama at Birmingham.
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Business Review and in a number of articles elsewhere (e.g., Fitzgerald and Jacobsen 1987; Gray 1987; Reinhardt 1987). Criticism-quite naturally - came particularly from those in the not-for-profit health care sector and, while the methodology, the conclusions, and the time period of the study (1982, prior to the introduction of the Medicare prospective payment system) were called into question, the criticisms were not supported empirically. In an earlier study of our own (Haddock, Arrington, and Skelton 1989), we did empirically evaluate the methodological criticisms and recommended remedial modifications to Herzlinger and Krasker's model. Using Herzlinger and Krasker's original model and the remedially modified models, the current artide empirically reviews the original conclusions using similar pre-PPS (1982) data. We also are currently preparing a final study that will evaluate the same issues using 1986 data to address whether observed differences in for-profit and not-for-profit performance remain or not in the post-PPS era.
THE HERZLINGER-KRASKER STUDY Herzlinger and Krasker analyzed the performance of 563 of the 725 hospitals in six for-profit and eight not-for-profit multi-institutional hospital systems. American Medical International (AMI), Charter Medical Corporation, Hospital Corporation of America (HCA), Humana, Lifemark, and National Medical Enterprises (NME) were the for-profit systems. The not-for-profit systems were Fairview Community Hospitals, Health Central, Intermountain Health Care, Kaiser Foundation Hospitals, Lutheran Health System, Mercy Health Services, Samaritan Health Services, and Southwest Community Health Services. Herzlinger and Kraskeres data were compiled from several sources: the 1983 Hospital Cost Limits File compiled for the Medicare program, an American Hospital Association (AHA) survey, a survey by the federal Office of Civil Rights concerning payer mix and admission sources, and the hospitals' Medicare cost reports. Comparing the social performance of for-profit and not-for-profit hospitals, Herzlinger and Krasker sought to put the two types on a "level playing field" by taking into account the subsidization of the notfor-profits by society at large. They made the accounting data for the two groups as comparable as possible, correcting for social subsidies (i.e., tax status), inflation, and differences in depreciation. Next, Herzlinger and Krasker developed a model of hospital performance and used this model to investigate whether the not-for-
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profits' poorer financial performance, as measured in their study, could be attributed to differences in hospital characteristics. Hospital geographic location, efficiency (the cost of providing patient care), scope of services, involvement in professional education, severity of illness of patients, quality of care, responsiveness to physicians, and size (capacity) were considered. Herzlinger and Krasker contended that neither society as a whole nor individual patients benefit from the subsidies accorded the not-forprofits. The authors drew the following conclusions from the findings of their analysis: - Not-for-profit hospitals are no more accessible to the medi-
cally indigent and uninsured than are for-profits. -Both types of hospital serve a full range of patients, neither "skimming the cream" nor providing a disproportionate share of services to the underinsured. - Not-for-profit hospitals emphasize short-term operational results, investing less than do for-profits in capital improvements to provide for the long-term needs of the communities they serve. -Not-for-profits, unlike for-profit hospitals, operate to maximize physician benefit. -Not-for-profit hospitals are less efficient than for-profit hospitals. -For-profit and not-for-profit hospitals offer equivalent scopes of service. -For-profit hospitals are as involved as not-for-profits in professional education and provide an equivalent, if not higher, quality of care. On the basis of these conclusions, Herzlinger and Krasker asserted that society should rethink its policy of social subsidization and that not-for-profit health care organizations must do more to provide for the indigent and to modernize their physical plant and equipment if they are to retain their privileged status.
A RECONSIDERATION We used hospital data from the same time period to reexamine the conclusions that Herzlinger and Krasker made concerning social bene-
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fit. However, we analyzed a larger data set and employed a different statistical technique, discriminant analysis. DATA
While inferences made from hospital data for a time prior to the introduction of the Medicare prospective payment system may be irrelevant today (Fitzgerald and Jacobsen 1987), we chose to use data from that same time period (1982) to achieve a degree of comparability between our study and that of Herzlinger and Krasker. Data for our analyses came from the 1982 American Hospital Association (AHA) Annual Survey of Hospitals. A total of 5,802 general, short-term, nonfederal, community hospitals comprised the AHA data set; 3,783 of these were for-profit or not-for-profit voluntary hospitals for which there were usable data for our study. Additional data on the hospital systems with which some hospitals were affiliated were obtained from the 1982 Multihospital System Parent Corporation data set. Data on local labor rates were obtained from the Federal Register. Unlike the sample used by Herzlinger and Krasker, our total study group included system and nonsystem hospitals. STATISTICAL METHOD
A number of statistical techniques are available to analyze the relation-
ships among multiple variables. Multiple regression and discriminant analysis are recommended when the nature of the research situation is essentially exploratory (Goldstein and Dillon 1978; Klecka 1980). The results obtained using multiple regression and discriminant analysis are essentially comparable (Cleary and Angel 1984). In their study, Herzlinger and Krasker used multiple regression analysis, the most commonly used multivariate technique in the hospital performance literature. Multiple regression was appropriate as they hypothesized independent-dependent variable relationships. Independent variables included a number of measures thought to influence hospital performance. For-profit or not-for-profit status (e.g., control status) was induded as an independent variable. Several dependent variables that reflected aspects of hospital performance (e.g., patient revenues, patient days generated) were used as dependent variables. Discriminant analysis is much less commonly used in health services research than is multiple regression. Discriminant analysis allows one to distinguish between two or more mutually exdusive groups on the basis of a collection of discriminating (independent) variables that measure characteristics on which the groups are expected to differ. The
Not-For-Profits
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task is to describe the characteristic differences between or among the groups. In our studies, we have used discriminant analysis to focus not on the exploration of independent-dependent variable relationships for for-profit and not-for-profit hospitals, as did Herzlinger and Krasker, but instead on the exploration of characteristics that discriminate between for-profit and not-for-profit hospitals. The assumptions of discriminant analysis (a number of mutually exclusive groups measured at the nominal level) appear to fit the characteristics of this problem more appropriately than those of multiple regression. In this study, we used discriminant models with discriminating variables comparable to Herzlinger and Krasker's independent variables. The dichotomous dependent variable we used was control status, that is, for-profit or not-for-profit status. Three discriminant models were estimated. In the first model, independent variables similar to those used by Herzlinger and Krasker were employed. Lack of comparable financial and case-mix information in our data set required that we substitute proxy measures for four constructs; all other constructs were measured as in the Herzlinger and Krasker study to maintain comparability of findings (Table 1). We found that several of the independent variables used by Herzlinger and Krasker were highly correlated with one another. Such high intercorrelations (multicollinearity) can cause difficulties in model estimation and interpretation, as mentioned by Reinhardt (1987). The second model, therefore, included a subset of independent variablesused by Herzlinger and Krasker, modified to reduce multicollinearity (or redundancy) among the discriminating variables (Table 1). The third model included some new discriminating variables not used in the Herzlinger and Krasker study (Table 1). To promote comparability with Herzlinger and Krasker, this set of variables was similar to those found in the first two analyses with, however, two important differences. First, this set of variables was checked for multicollinearity and was found to present low intercorrelations among the measures. Second, the new discriminating variables were chosen to reflect work previously done in the area of hospital performance, particularly studies reported in For-Profit Enterprise in Health Care (Gray 1986), while not straying too far from Herzlinger and Kraskeres construct formulation. Each discriminant analysis was performed using three data sets. The first analysis included all U.S. community hospitals belonging to the 14 systems considered by Herzlinger and Krasker. In our analysis, there were usable data for 563 of the 725 hospitals in the group. Our second data set included all (N = 1,083) system-affiliated hospitals for
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Table 1: Hospital Performance Constructs and the Variables Chosen to Operationalize the Constructs Construct Efficiency
Herzlinger/Krasker
Severity of Illness
Financial measures (unavailable on AHA tape) Employees per bed Eight services: Pediatrics acute inpatient Medical-surgical inpatient Cardiac intensive care unit Neonatal intensive care unit Burn intensive care unit Premature nursery Organ transplants Open heart surgery Emergency room visits (2 weeks of data) Medicare case-mix index
Quality of Care
JCAH accreditation
System Size Management Status
Number of hospitals Presence of a management
Professional Training
contract Medical school affiliation
Scope of Services
Access
Number of full-time medical residents Investment in Future Financial Measures Resources (unavailable on AHA tape) Physician Responsive- Private MD referrals ness Pay Classes Served Medicare days Medicaid days Voluntary/Commercial days Other/Self-pay days Labor Costs Medicare local labor rate
Hospital Capacity
Statistical beds Adjusted patient days
Arrington/Haddock Average cost per patient day Employees per bed Additive index of 7 community services: Family planning Emergency department Health promotion Outpatient department Social work Patient representative Volunteer services Emergency room visits (1 year of data) Average length of stay Ratio of births to total admissions Additive index of accreditations/certifications Number of hospitals Presence of a management contract Additive index of accredited education programs Number of full-time medical residents Capital expenditures per bed Percent physicians new to staff (1981) Medicare days Medicaid days Other days
Average pay per nonphysician FTE Adjusted patient days
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297
which complete information was available in the AHA data set. Our final data set included the 3,783 community hospitals in the AHA data set for which data were complete.
DISCUSSION On the basis of their findings, Herzlinger and Krasker drew a number of conclusions, listed at the beginning of this article, about differences in the performance of for-profit and not-for-profit hospitals. Our findings suggest different conclusions. We have identified some generic differences between for-profit and not-for-profit hospitals that merit reporting. It should be noted that our findings do not provide definitive answers regarding differences between for-profit and not-for-profit hospitals. However, they do provide additional information that we hope will inform the debate as expressed in the Herzlinger and Krasker article. We have observed a number of possible differences in terms of efficiency, scope of services, access, severity of illness, quality of care, involvement in professional education, long-term capital investment, responsiveness to physicians, and patient payer source as measured in our study. Findings from our nine discriminant analyses (three models for each of three data sets) are shown in Table 2. This table indicates whether each discriminating variable made it more likely for a hospital to be classified as for-profit or not-for-profit. It should be noted that comparable results (unreported in this article) were obtained through multiple regression. EFFICIENCY
The data used in our study did not allow use of the same financial measures employed by Herzlinger and Krasker. We measured efficiency as average cost per patient day in all three models. Hospitals with higher average cost per patient day were more likely to be classified as for-profit hospitals, suggesting that for-profit hospitals may be less efficient. However, the average length of stay was also shorter in for-profit hospitals, making the meaning of these cost differences difficult to interpret. In all cases where it was included in the model, the number of employees per bed was higher in not-for-profit hospitals suggesting that not-for-profits may be less efficient than for-profits.
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SCOPE OF SERVICES
While Herzlinger and Krasker measured scope of services as the presence or absence of eight selected services, we chose to measure this construct with an additive index of seven community-oriented services. Our selection was based on a measure suggested by the Institute of Medicine study (Gray 1986). In agreement with the Institute of Medicine study, we found that hospitals offering services typically considered to be community responsive were more likely to be classified as not-for-profit hospitals. This finding held true across the three data sets. The data do not allow analysis of actual community need for these services. ACCESS
As in the Herzlinger and Krasker study and in the Institute of Medicine study, access was measured in terms of emergency room visits. Emergency rooms generate a large number of admissions for hospitals and provide access to most individuals in a hospital's service population. Data from several sources suggest that uninsured accident and trauma victims are heavy users of uncompensated emergency and inpatient services (Committee on Implications of For-Profit Enterprise in Health Care 1986a). In almost all cases, hospitals with higher numbers of emergency room visits were more likely to be classified as not-for-profit hospitals. This finding suggests that not-for-profit hospitals are more accessible, particularly for patients who are likely to have little or no insurance coverage. This was true for the all-hospital data set and the allsystem-affiliated data set. It was not true for those system-affiliated hospitals considered by Herzlinger and Krasker, perhaps explaining their opposite finding in this regard. SEVERITY OF ILLNESS/CASE-MIX DIFFERENCES
Two measures, average length of stay and the number of births per 100 admissions, were used to measure the severity of illness or case-mix differences in the three study models. In most cases, hospitals with higher average lengths of stay were more likely to be not-for-profit hospitals, suggesting that not-for-profits may care for sicker patients. This finding may be questionable, however, given the lower average cost per patient day in not-for-profit hospitals. Watt, Renn, Hahn, et al. (1986) found that hospitals with higher numbers of births per 100 admissions tended to treat more severely ill
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patients generally and that margins per case (revenues less expenses) for an entire institution are inversely related to the volume of births in the institution. Hospitals, in all samples, with a higher number of births per 100 admissions were more likely to be classified as not-forprofit hospitals. This finding suggests that not-for-profit hospitals may treat more severely ill patients. It also suggests that not-for-profits have smaller margins per case than for-profit hospitals; that is, they generate less excess revenue than do for-profits. QUALITY
Quality of care is typically evaluated in terms of structure, process, and outcome (Donabedian 1969). Structural measures include those aspects of health care organizations that are typically identified as inputs to service, facilities, equipment, and qualifications of health care professionals. In our study, quality is measured structurally in terms of accreditations and certifications across the three models, in some models simply as Joint Commission on Accreditation of Hospitals UCAH) accreditation and in others as an additive index of accreditations and certifications. Regardless of the model or the data set, accredited hospitals were more likely to be classified as for-profit, suggesting that for-profit hospitals provide higher quality of care. Findings from the Institute of Medicine study (Committee on Implications of For-Profit Enterprise in Health Care 1986b) temper ours in that it was found that the percentage of hospitals accredited is highest among for-profit, systemaffiliated hospitals, with no real differences between independent for-profit hospitals and not-for-profit, system-affiliated hospitals. INVOLVEMENT IN PROFESSIONAL EDUCATION
Herzlinger and Krasker measured educational involvement as whether or not the hospital had a medical school affiliation. In our model, we used an additive index of educational programs. Regardless of the model or sample, hospitals involved in professional education were more likely to be classified as not-for-profit, suggesting that not-forprofit hospitals provide greater levels of professional education than do
for-profits. The presence of full-time medical residents was also used in all models to identify differences in commitment to professional education. This measure proved to be unstable across the models, however, and findings in this regard were inconclusive.
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LONG-TERM CAPITAL INVESTMENT
We were unable to use the same financial measures as Herzlinger and Krasker. We evaluated long-term capital investment in terms of actual capital expenditures per bed and found that hospitals with higher capital expenditures per bed were more likely to be classified as not-forprofit hospitals. PHYSICIAN RESPONSIVENESS
Herzlinger and Krasker stated that not-for-profit hospitals cater to the needs of physicians and operate to maximize benefit to physicians. If this is the case, one would assume that physicians would be more likely to join the staffs of not-for-profit than for-profit hospitals. We evaluated physician responsiveness in terms of the percent of total medical staff who had joined the active/associate staff of a hospital during 1981. Hospitals that had attracted higher percentages of new physicians during the study year were more likely to be classified as for-profit hospitals, suggesting that for-profit hospitals may be more physician responsive. This finding held true across data sets and models. PAYER CLASSES SERVED
Our data required the measurement of payer classification in terms of Medicare, Medicaid, and all other payer patient days. For most hospitals, the other payers category is dominated by private insurance beneficiaries. Medicare and Medicaid patient days proved to be unstable across the models. Most frequently, hospitals with higher numbers of Medicare patient days were classified as for-profit hospitals and hospitals with higher numbers of Medicaid patient days were classified as not-for-profit hospitals. Hospitals with higher numbers of others-payer patient days were consistently dassified as for-profit hospitals. This finding, along with higher Medicare patient days in for-profit hospitals during this prePPS period, suggested that not-for-profit hospitals provide care to less well insured patients than do for-profit hospitals and that for-profit hospitals provide more care to patients with "good" insurance coverage.
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CONCLUSION We continue to evaluate our work in terms of the usefulness of the identified hospital performance constructs in distinguishing between for-profit and not-for-profit hospitals. Initially, our data suggest that the following conclusions, congruent with the Institute of Medicine report and contrary to Herzlinger and Krasker, can be made:
-Not-for-profit hospitals appear more likely to be accessible to the uninsured and medically indigent than are for-profits. -While both types of hospital serve patients with a variety of payer sources, not-for-profit hospitals appear more likely to carry a heavier indigent load and for-profit hospitals appear to serve relatively more patients with "good" insurance coverage. -Not-for-profit hospitals do not necessarily emphasize shortterm operational results. In fact, not-for-profit hospitals invest more in capital improvements to provide for the longterm needs of the communities they serve than do forprofits. -For-profit hospitals appear to attract new physicians to their active and associate staffs at a higher rate than do not-forprofit hospitals, suggesting that for-profits may be more physician responsive than are not-for-profits. -For-profit hospitals appear to be more efficient than not-forprofit hospitals. -For-profit hospitals do not offer a scope of servicesparticularly community-oriented services-equivalent to that provided by not-for-profits. -For-profit hospitals are not as involved as not-for-profits in professional education. Herzlinger and Krasker argued that social subsidization of notfor-profit hospitals may no longer be legitimate based on findings from their study that, they asserted, provide evidence that not-for-profits have broken their "social contract." Initial results from our study caution that such a conclusion is premature and ill-advised. Much more work needs to be done before any definitive conclusion can be reached. Policymakers could make a grave error if future direction for health care in the United States were set without more intensive study and thoughtful consideration of these issues.
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We encourage further investigation into the performance of forprofit and not-for-profit hospitals. Such work should include refinement of the measures of hospital performance, investigation of performance in the post-PPS era, and consideration of whether the legal definition of "for-profit" and "not-for-profit" status captures accurately the issue in question. Rather than concluding debate concerning the relative merits of for-profit and not-for-profit hospitals, as Herzlinger and Krasker suggest, we assert that debate should continue, informed by further research, before conclusions are reached and new policy directions pursued.
REFERENCES Cleary, P., and R. Angel. "The Analysis of Relationships Involving Dichotomous Dependent Variables."Journal of Health and Social Behavior 25, no. 3
(1984):334.
Committee on Implications of For-Profit Enterprise in Health Care. "Access to Care." In For-Profit Enterprise in Health Care. Edited by D. H. Gray. Washington, DC: National Academy Press, 1986a, pp. 97-126. . "Quality of Care." In For-Profit Enterprise in Health Care. Edited by D. H. Gray. Washington, DC: National Academy Press, 1986b, pp. 127-41. Donabedian, A. A Guide to Medical Care Administration. Vol. II, Medical Care Appraisal-Quality and Utilization. Washington, DC: American Public Health Association, 1969. Fitzgerald, J., and B. Jacobsen. "Study Fails to Prove For-Profits' Superiority." Health Progress 68, no. 3 (1987):32-37. Goldstein, M., and W. Dillon. Discrete Discriminant Analysis. New York: John Wiley & Sons, Inc., 1978. Gray, B. H., ed. For-Profit Enterprise in Health Care. Washington, DC: National Academy Press, 1986. Gray, B. H. "Shaky Basis for Report's Sweeping Recommendation." Health Progress 68, no. 3 (1987):38-41. Haddock, C. C., B. Arrington, and A. Skelton. "Who Profits from Not-forProfits: A Reconsideration." Health Services Management Research 2, no. 2 (July 1989):82-104. Herzlinger, R. E., and W. S. Krasker. "Who Profits from Nonprofits?" Harvard Business Review 65, no. 1 (1987):93-106. Klecka, W. Discriminant Analysis. Beverly Hills, CA: Sage Publications, 1980. Reinhardt, W. E. "Flawed Methods Cripple Study on Not-for-Profits." Hospitals 61, no. 8 (20 April 1987): 136. Watt, J. H., S. C. Renn, J. E. Hahn, R. A. Derson, and C. J. Schramm. "The Effects of Ownership and Multihospital System Membership on Hospital Functional Strategies and Economic Performance." In ForProfit Enterprise in Health Care. Edited by D. H. Gray. Washington, DC:: National Academy Press, 1986, pp. 260-289.
"Who profits from nonprofits?" asked Regina Herzlinger and William Krasker in an article in the Harvard Business Review (Herzlinger and Krasker 1987). Their study questioned whether not-for-profit hospitals achieve the social goals for which they are intended and for which they are subsidized by society (i.e., through their privileged tax-exempt status). Herzlinger and Krasker's findings suggested that not-for-profit health care organizations do not return more benefit to society than do for-profit hospitals and should not, therefore, be subsidized by society. The Herzlinger and Krasker article unleashed some aggressive criticism in a lengthy series of "Letters to the Editor" in the Harvard Address correspondence and requests for reprints to Barbara Arrington, Ph.D., Assistant Professor, Center for Health Services Education and Research, St. Louis University Medical Center, 3525 Caroline Street, St. Louis, MO 63104. Cynthia Carter Haddock, Ph.D. is Associate Professor in the Department of Health Services Administration, University of Alabama at Birmingham.
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Business Review and in a number of articles elsewhere (e.g., Fitzgerald and Jacobsen 1987; Gray 1987; Reinhardt 1987). Criticism-quite naturally - came particularly from those in the not-for-profit health care sector and, while the methodology, the conclusions, and the time period of the study (1982, prior to the introduction of the Medicare prospective payment system) were called into question, the criticisms were not supported empirically. In an earlier study of our own (Haddock, Arrington, and Skelton 1989), we did empirically evaluate the methodological criticisms and recommended remedial modifications to Herzlinger and Krasker's model. Using Herzlinger and Krasker's original model and the remedially modified models, the current artide empirically reviews the original conclusions using similar pre-PPS (1982) data. We also are currently preparing a final study that will evaluate the same issues using 1986 data to address whether observed differences in for-profit and not-for-profit performance remain or not in the post-PPS era.
THE HERZLINGER-KRASKER STUDY Herzlinger and Krasker analyzed the performance of 563 of the 725 hospitals in six for-profit and eight not-for-profit multi-institutional hospital systems. American Medical International (AMI), Charter Medical Corporation, Hospital Corporation of America (HCA), Humana, Lifemark, and National Medical Enterprises (NME) were the for-profit systems. The not-for-profit systems were Fairview Community Hospitals, Health Central, Intermountain Health Care, Kaiser Foundation Hospitals, Lutheran Health System, Mercy Health Services, Samaritan Health Services, and Southwest Community Health Services. Herzlinger and Kraskeres data were compiled from several sources: the 1983 Hospital Cost Limits File compiled for the Medicare program, an American Hospital Association (AHA) survey, a survey by the federal Office of Civil Rights concerning payer mix and admission sources, and the hospitals' Medicare cost reports. Comparing the social performance of for-profit and not-for-profit hospitals, Herzlinger and Krasker sought to put the two types on a "level playing field" by taking into account the subsidization of the notfor-profits by society at large. They made the accounting data for the two groups as comparable as possible, correcting for social subsidies (i.e., tax status), inflation, and differences in depreciation. Next, Herzlinger and Krasker developed a model of hospital performance and used this model to investigate whether the not-for-
Not-For-Profits
293
profits' poorer financial performance, as measured in their study, could be attributed to differences in hospital characteristics. Hospital geographic location, efficiency (the cost of providing patient care), scope of services, involvement in professional education, severity of illness of patients, quality of care, responsiveness to physicians, and size (capacity) were considered. Herzlinger and Krasker contended that neither society as a whole nor individual patients benefit from the subsidies accorded the not-forprofits. The authors drew the following conclusions from the findings of their analysis: - Not-for-profit hospitals are no more accessible to the medi-
cally indigent and uninsured than are for-profits. -Both types of hospital serve a full range of patients, neither "skimming the cream" nor providing a disproportionate share of services to the underinsured. - Not-for-profit hospitals emphasize short-term operational results, investing less than do for-profits in capital improvements to provide for the long-term needs of the communities they serve. -Not-for-profits, unlike for-profit hospitals, operate to maximize physician benefit. -Not-for-profit hospitals are less efficient than for-profit hospitals. -For-profit and not-for-profit hospitals offer equivalent scopes of service. -For-profit hospitals are as involved as not-for-profits in professional education and provide an equivalent, if not higher, quality of care. On the basis of these conclusions, Herzlinger and Krasker asserted that society should rethink its policy of social subsidization and that not-for-profit health care organizations must do more to provide for the indigent and to modernize their physical plant and equipment if they are to retain their privileged status.
A RECONSIDERATION We used hospital data from the same time period to reexamine the conclusions that Herzlinger and Krasker made concerning social bene-
294
HSR: Health Services Research 25:2 (June 1990)
fit. However, we analyzed a larger data set and employed a different statistical technique, discriminant analysis. DATA
While inferences made from hospital data for a time prior to the introduction of the Medicare prospective payment system may be irrelevant today (Fitzgerald and Jacobsen 1987), we chose to use data from that same time period (1982) to achieve a degree of comparability between our study and that of Herzlinger and Krasker. Data for our analyses came from the 1982 American Hospital Association (AHA) Annual Survey of Hospitals. A total of 5,802 general, short-term, nonfederal, community hospitals comprised the AHA data set; 3,783 of these were for-profit or not-for-profit voluntary hospitals for which there were usable data for our study. Additional data on the hospital systems with which some hospitals were affiliated were obtained from the 1982 Multihospital System Parent Corporation data set. Data on local labor rates were obtained from the Federal Register. Unlike the sample used by Herzlinger and Krasker, our total study group included system and nonsystem hospitals. STATISTICAL METHOD
A number of statistical techniques are available to analyze the relation-
ships among multiple variables. Multiple regression and discriminant analysis are recommended when the nature of the research situation is essentially exploratory (Goldstein and Dillon 1978; Klecka 1980). The results obtained using multiple regression and discriminant analysis are essentially comparable (Cleary and Angel 1984). In their study, Herzlinger and Krasker used multiple regression analysis, the most commonly used multivariate technique in the hospital performance literature. Multiple regression was appropriate as they hypothesized independent-dependent variable relationships. Independent variables included a number of measures thought to influence hospital performance. For-profit or not-for-profit status (e.g., control status) was induded as an independent variable. Several dependent variables that reflected aspects of hospital performance (e.g., patient revenues, patient days generated) were used as dependent variables. Discriminant analysis is much less commonly used in health services research than is multiple regression. Discriminant analysis allows one to distinguish between two or more mutually exdusive groups on the basis of a collection of discriminating (independent) variables that measure characteristics on which the groups are expected to differ. The
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task is to describe the characteristic differences between or among the groups. In our studies, we have used discriminant analysis to focus not on the exploration of independent-dependent variable relationships for for-profit and not-for-profit hospitals, as did Herzlinger and Krasker, but instead on the exploration of characteristics that discriminate between for-profit and not-for-profit hospitals. The assumptions of discriminant analysis (a number of mutually exclusive groups measured at the nominal level) appear to fit the characteristics of this problem more appropriately than those of multiple regression. In this study, we used discriminant models with discriminating variables comparable to Herzlinger and Krasker's independent variables. The dichotomous dependent variable we used was control status, that is, for-profit or not-for-profit status. Three discriminant models were estimated. In the first model, independent variables similar to those used by Herzlinger and Krasker were employed. Lack of comparable financial and case-mix information in our data set required that we substitute proxy measures for four constructs; all other constructs were measured as in the Herzlinger and Krasker study to maintain comparability of findings (Table 1). We found that several of the independent variables used by Herzlinger and Krasker were highly correlated with one another. Such high intercorrelations (multicollinearity) can cause difficulties in model estimation and interpretation, as mentioned by Reinhardt (1987). The second model, therefore, included a subset of independent variablesused by Herzlinger and Krasker, modified to reduce multicollinearity (or redundancy) among the discriminating variables (Table 1). The third model included some new discriminating variables not used in the Herzlinger and Krasker study (Table 1). To promote comparability with Herzlinger and Krasker, this set of variables was similar to those found in the first two analyses with, however, two important differences. First, this set of variables was checked for multicollinearity and was found to present low intercorrelations among the measures. Second, the new discriminating variables were chosen to reflect work previously done in the area of hospital performance, particularly studies reported in For-Profit Enterprise in Health Care (Gray 1986), while not straying too far from Herzlinger and Kraskeres construct formulation. Each discriminant analysis was performed using three data sets. The first analysis included all U.S. community hospitals belonging to the 14 systems considered by Herzlinger and Krasker. In our analysis, there were usable data for 563 of the 725 hospitals in the group. Our second data set included all (N = 1,083) system-affiliated hospitals for
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Table 1: Hospital Performance Constructs and the Variables Chosen to Operationalize the Constructs Construct Efficiency
Herzlinger/Krasker
Severity of Illness
Financial measures (unavailable on AHA tape) Employees per bed Eight services: Pediatrics acute inpatient Medical-surgical inpatient Cardiac intensive care unit Neonatal intensive care unit Burn intensive care unit Premature nursery Organ transplants Open heart surgery Emergency room visits (2 weeks of data) Medicare case-mix index
Quality of Care
JCAH accreditation
System Size Management Status
Number of hospitals Presence of a management
Professional Training
contract Medical school affiliation
Scope of Services
Access
Number of full-time medical residents Investment in Future Financial Measures Resources (unavailable on AHA tape) Physician Responsive- Private MD referrals ness Pay Classes Served Medicare days Medicaid days Voluntary/Commercial days Other/Self-pay days Labor Costs Medicare local labor rate
Hospital Capacity
Statistical beds Adjusted patient days
Arrington/Haddock Average cost per patient day Employees per bed Additive index of 7 community services: Family planning Emergency department Health promotion Outpatient department Social work Patient representative Volunteer services Emergency room visits (1 year of data) Average length of stay Ratio of births to total admissions Additive index of accreditations/certifications Number of hospitals Presence of a management contract Additive index of accredited education programs Number of full-time medical residents Capital expenditures per bed Percent physicians new to staff (1981) Medicare days Medicaid days Other days
Average pay per nonphysician FTE Adjusted patient days
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which complete information was available in the AHA data set. Our final data set included the 3,783 community hospitals in the AHA data set for which data were complete.
DISCUSSION On the basis of their findings, Herzlinger and Krasker drew a number of conclusions, listed at the beginning of this article, about differences in the performance of for-profit and not-for-profit hospitals. Our findings suggest different conclusions. We have identified some generic differences between for-profit and not-for-profit hospitals that merit reporting. It should be noted that our findings do not provide definitive answers regarding differences between for-profit and not-for-profit hospitals. However, they do provide additional information that we hope will inform the debate as expressed in the Herzlinger and Krasker article. We have observed a number of possible differences in terms of efficiency, scope of services, access, severity of illness, quality of care, involvement in professional education, long-term capital investment, responsiveness to physicians, and patient payer source as measured in our study. Findings from our nine discriminant analyses (three models for each of three data sets) are shown in Table 2. This table indicates whether each discriminating variable made it more likely for a hospital to be classified as for-profit or not-for-profit. It should be noted that comparable results (unreported in this article) were obtained through multiple regression. EFFICIENCY
The data used in our study did not allow use of the same financial measures employed by Herzlinger and Krasker. We measured efficiency as average cost per patient day in all three models. Hospitals with higher average cost per patient day were more likely to be classified as for-profit hospitals, suggesting that for-profit hospitals may be less efficient. However, the average length of stay was also shorter in for-profit hospitals, making the meaning of these cost differences difficult to interpret. In all cases where it was included in the model, the number of employees per bed was higher in not-for-profit hospitals suggesting that not-for-profits may be less efficient than for-profits.
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SCOPE OF SERVICES
While Herzlinger and Krasker measured scope of services as the presence or absence of eight selected services, we chose to measure this construct with an additive index of seven community-oriented services. Our selection was based on a measure suggested by the Institute of Medicine study (Gray 1986). In agreement with the Institute of Medicine study, we found that hospitals offering services typically considered to be community responsive were more likely to be classified as not-for-profit hospitals. This finding held true across the three data sets. The data do not allow analysis of actual community need for these services. ACCESS
As in the Herzlinger and Krasker study and in the Institute of Medicine study, access was measured in terms of emergency room visits. Emergency rooms generate a large number of admissions for hospitals and provide access to most individuals in a hospital's service population. Data from several sources suggest that uninsured accident and trauma victims are heavy users of uncompensated emergency and inpatient services (Committee on Implications of For-Profit Enterprise in Health Care 1986a). In almost all cases, hospitals with higher numbers of emergency room visits were more likely to be classified as not-for-profit hospitals. This finding suggests that not-for-profit hospitals are more accessible, particularly for patients who are likely to have little or no insurance coverage. This was true for the all-hospital data set and the allsystem-affiliated data set. It was not true for those system-affiliated hospitals considered by Herzlinger and Krasker, perhaps explaining their opposite finding in this regard. SEVERITY OF ILLNESS/CASE-MIX DIFFERENCES
Two measures, average length of stay and the number of births per 100 admissions, were used to measure the severity of illness or case-mix differences in the three study models. In most cases, hospitals with higher average lengths of stay were more likely to be not-for-profit hospitals, suggesting that not-for-profits may care for sicker patients. This finding may be questionable, however, given the lower average cost per patient day in not-for-profit hospitals. Watt, Renn, Hahn, et al. (1986) found that hospitals with higher numbers of births per 100 admissions tended to treat more severely ill
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patients generally and that margins per case (revenues less expenses) for an entire institution are inversely related to the volume of births in the institution. Hospitals, in all samples, with a higher number of births per 100 admissions were more likely to be classified as not-forprofit hospitals. This finding suggests that not-for-profit hospitals may treat more severely ill patients. It also suggests that not-for-profits have smaller margins per case than for-profit hospitals; that is, they generate less excess revenue than do for-profits. QUALITY
Quality of care is typically evaluated in terms of structure, process, and outcome (Donabedian 1969). Structural measures include those aspects of health care organizations that are typically identified as inputs to service, facilities, equipment, and qualifications of health care professionals. In our study, quality is measured structurally in terms of accreditations and certifications across the three models, in some models simply as Joint Commission on Accreditation of Hospitals UCAH) accreditation and in others as an additive index of accreditations and certifications. Regardless of the model or the data set, accredited hospitals were more likely to be classified as for-profit, suggesting that for-profit hospitals provide higher quality of care. Findings from the Institute of Medicine study (Committee on Implications of For-Profit Enterprise in Health Care 1986b) temper ours in that it was found that the percentage of hospitals accredited is highest among for-profit, systemaffiliated hospitals, with no real differences between independent for-profit hospitals and not-for-profit, system-affiliated hospitals. INVOLVEMENT IN PROFESSIONAL EDUCATION
Herzlinger and Krasker measured educational involvement as whether or not the hospital had a medical school affiliation. In our model, we used an additive index of educational programs. Regardless of the model or sample, hospitals involved in professional education were more likely to be classified as not-for-profit, suggesting that not-forprofit hospitals provide greater levels of professional education than do
for-profits. The presence of full-time medical residents was also used in all models to identify differences in commitment to professional education. This measure proved to be unstable across the models, however, and findings in this regard were inconclusive.
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LONG-TERM CAPITAL INVESTMENT
We were unable to use the same financial measures as Herzlinger and Krasker. We evaluated long-term capital investment in terms of actual capital expenditures per bed and found that hospitals with higher capital expenditures per bed were more likely to be classified as not-forprofit hospitals. PHYSICIAN RESPONSIVENESS
Herzlinger and Krasker stated that not-for-profit hospitals cater to the needs of physicians and operate to maximize benefit to physicians. If this is the case, one would assume that physicians would be more likely to join the staffs of not-for-profit than for-profit hospitals. We evaluated physician responsiveness in terms of the percent of total medical staff who had joined the active/associate staff of a hospital during 1981. Hospitals that had attracted higher percentages of new physicians during the study year were more likely to be classified as for-profit hospitals, suggesting that for-profit hospitals may be more physician responsive. This finding held true across data sets and models. PAYER CLASSES SERVED
Our data required the measurement of payer classification in terms of Medicare, Medicaid, and all other payer patient days. For most hospitals, the other payers category is dominated by private insurance beneficiaries. Medicare and Medicaid patient days proved to be unstable across the models. Most frequently, hospitals with higher numbers of Medicare patient days were classified as for-profit hospitals and hospitals with higher numbers of Medicaid patient days were classified as not-for-profit hospitals. Hospitals with higher numbers of others-payer patient days were consistently dassified as for-profit hospitals. This finding, along with higher Medicare patient days in for-profit hospitals during this prePPS period, suggested that not-for-profit hospitals provide care to less well insured patients than do for-profit hospitals and that for-profit hospitals provide more care to patients with "good" insurance coverage.
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CONCLUSION We continue to evaluate our work in terms of the usefulness of the identified hospital performance constructs in distinguishing between for-profit and not-for-profit hospitals. Initially, our data suggest that the following conclusions, congruent with the Institute of Medicine report and contrary to Herzlinger and Krasker, can be made:
-Not-for-profit hospitals appear more likely to be accessible to the uninsured and medically indigent than are for-profits. -While both types of hospital serve patients with a variety of payer sources, not-for-profit hospitals appear more likely to carry a heavier indigent load and for-profit hospitals appear to serve relatively more patients with "good" insurance coverage. -Not-for-profit hospitals do not necessarily emphasize shortterm operational results. In fact, not-for-profit hospitals invest more in capital improvements to provide for the longterm needs of the communities they serve than do forprofits. -For-profit hospitals appear to attract new physicians to their active and associate staffs at a higher rate than do not-forprofit hospitals, suggesting that for-profits may be more physician responsive than are not-for-profits. -For-profit hospitals appear to be more efficient than not-forprofit hospitals. -For-profit hospitals do not offer a scope of servicesparticularly community-oriented services-equivalent to that provided by not-for-profits. -For-profit hospitals are not as involved as not-for-profits in professional education. Herzlinger and Krasker argued that social subsidization of notfor-profit hospitals may no longer be legitimate based on findings from their study that, they asserted, provide evidence that not-for-profits have broken their "social contract." Initial results from our study caution that such a conclusion is premature and ill-advised. Much more work needs to be done before any definitive conclusion can be reached. Policymakers could make a grave error if future direction for health care in the United States were set without more intensive study and thoughtful consideration of these issues.
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We encourage further investigation into the performance of forprofit and not-for-profit hospitals. Such work should include refinement of the measures of hospital performance, investigation of performance in the post-PPS era, and consideration of whether the legal definition of "for-profit" and "not-for-profit" status captures accurately the issue in question. Rather than concluding debate concerning the relative merits of for-profit and not-for-profit hospitals, as Herzlinger and Krasker suggest, we assert that debate should continue, informed by further research, before conclusions are reached and new policy directions pursued.
REFERENCES Cleary, P., and R. Angel. "The Analysis of Relationships Involving Dichotomous Dependent Variables."Journal of Health and Social Behavior 25, no. 3
(1984):334.
Committee on Implications of For-Profit Enterprise in Health Care. "Access to Care." In For-Profit Enterprise in Health Care. Edited by D. H. Gray. Washington, DC: National Academy Press, 1986a, pp. 97-126. . "Quality of Care." In For-Profit Enterprise in Health Care. Edited by D. H. Gray. Washington, DC: National Academy Press, 1986b, pp. 127-41. Donabedian, A. A Guide to Medical Care Administration. Vol. II, Medical Care Appraisal-Quality and Utilization. Washington, DC: American Public Health Association, 1969. Fitzgerald, J., and B. Jacobsen. "Study Fails to Prove For-Profits' Superiority." Health Progress 68, no. 3 (1987):32-37. Goldstein, M., and W. Dillon. Discrete Discriminant Analysis. New York: John Wiley & Sons, Inc., 1978. Gray, B. H., ed. For-Profit Enterprise in Health Care. Washington, DC: National Academy Press, 1986. Gray, B. H. "Shaky Basis for Report's Sweeping Recommendation." Health Progress 68, no. 3 (1987):38-41. Haddock, C. C., B. Arrington, and A. Skelton. "Who Profits from Not-forProfits: A Reconsideration." Health Services Management Research 2, no. 2 (July 1989):82-104. Herzlinger, R. E., and W. S. Krasker. "Who Profits from Nonprofits?" Harvard Business Review 65, no. 1 (1987):93-106. Klecka, W. Discriminant Analysis. Beverly Hills, CA: Sage Publications, 1980. Reinhardt, W. E. "Flawed Methods Cripple Study on Not-for-Profits." Hospitals 61, no. 8 (20 April 1987): 136. Watt, J. H., S. C. Renn, J. E. Hahn, R. A. Derson, and C. J. Schramm. "The Effects of Ownership and Multihospital System Membership on Hospital Functional Strategies and Economic Performance." In ForProfit Enterprise in Health Care. Edited by D. H. Gray. Washington, DC:: National Academy Press, 1986, pp. 260-289.
