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For-Profit Enterprise in Health Care. 1986. National Academy Press, Washington, D.C. Compliance of Multihospital Systems win Standards of the joint Commission on Accreditation of Hospitals Danie! R. Longo, Gary A. Chase, Lynn A. AhIgren, James S. Robert, and Carol S. Weisman BACKGROUND A major challenge to the hospital industry is to provide available and accessible high- quality, cost-effective health and medical ser- vices. Historically, the individual voluntary hospital has been the focus of the industry's efforts to meet community health care needs. This type of organization has been quite suc- cessful in the past in meeting the challenges in an environment of abundant resources, moderate regulation, and cost-based reim- bursement. Today's environment, however, is a very different one. Increasingly constrained re- sources, more regulation, advances in medical technology, greater diversity of delivery sys- tems, growing public demand en cl awareness, increased burden of liability protection, and pressure of capital financing have all contrib- uted to creating the current "less supportive" (Longest, 1980) and "hostile" (Dowling, 1983) environment in which the individual hospital is striving to remain viable. Georgopoulos (1972) described the hospital as being an "adaptive system" in that each hospital is a dynamic, self-regulating system that is in constant interchange with its external environment. The adaptive strategy for many individual hospitals in today's environment is increasingly the formation of interdependent organizational linkages. Dr. Longo is Director of Research at the Joint Com- mission on Accreditation of Hospitals (JCAH), Chicago, Illinois, and Visiting Scholar, Northwestern Univer- sity, Evanston, Illinois. Ms. Ahl~en is associate di- rector, JCAH. Dr. Roberts is Vice PresidentforAccredita- tion, JCAH. Dr. Chase is a professor and Dr. Weisman an associate professor, lbe Johns Hopkins University, School of Hygiene and Public Health, Baltimore, Maryland. 375 According to statistics from the American Hospital Association (AHA), multihospital sys- tems, one of the major forms of hospital link- ages, have grown from 10 percent of all hospitals in 1970 to approximately 33 percent of all hos- pitals today. This increase in interorganiza- tional relationships represents a dramatic change in the structure of the hospital indus- try. Previous research indicates that the or- ganizational structure of the multihospital system represents a healthy adaptation to en- vironmental pressures (Longo, 1982; Longo and Chase, 1984~. Most important, however, is to understand the potential impact of these changes on patient care. There has been much discussion in the lit- erature of the relative merits of the various types of organizational arrangements devel- oping in health care in response to this new environment. The majority of these commen- taries and investigations have focused on the ability of these structures to control the costs of health care; an undeniably important con- cern. However, in conjunction with the cost issue, it is essential to address the impact of these new structures on the quality of patient care. The primarily anecdotal literature to date investigating differences in cost and quality between system and autonomous hospitals finds few, if any, differences (Ermann and Gabel, 1984~. However, potential advantages of mul- tihospital system arrangements to individual hospitals and their surrounding communities are frequently identified in the literature. The advantages of systems cited in the literature that are thought to directly affect the quality of patient care include the following: improved availability and access to care more comprehensive scope of services improved continuity of care

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376 greater ability to attract and retain clinical and management manpower improved continuing education programs greater resources available for quality as- surance activities ~ greater ability to conduct comparative studies. The assumption that quality of patient care would be a result of some of these multihos- pital system attributes has not yet been ade- quately substantiated by empirical studies. In fact, the question of whether there is any dif- ference between the performance of multi- hospital systems and autonomous hospitals is identified by Brown (1979) and Kaluzny (1982) as a research issue that still needs to be ad- dressed. Contributing to the lack of empirical inves- tigations addressing the quality of care in dif- ferent organizational structures is the difficulty in operationalizing quality. However, empir- ical investigations have been done which eval- uate the impact of specific organizational attributes on health outcomes. The ejects of board composition and struc- ture on hospital efficiency, as measured by cost per patient day and case, and quality, as mea- surecl by post-surgical complications rate and medical-surgical death rate, were investigated by Kaufman et al. (19791. Shorted et al. (1976) also used surgical death rate and post-surgical complication rate as indications of quality pa- tient care to evaluate the impact of manage- ment practices. Patient satisfaction was the quality index Fleming (1981) used to look at the effects of ownership and teaching status on good patient care. While these investigations have explored the importance of a variety of organizational attri- butes to outcomes, studies which investigate differences between multihospital systems and autonomous hospitals have been conducted only to a limited degree. Ruchlin et al. (1973) compared investor- owned system hospitals with not-for-profit in- dependent (autonomous) hospitals on a num- ber of variables. It is important to note that the differences identified between investor- owned and not-for-profit is confounded by or- ganizational structure. That is, differences identified may be due to the fact that a facility FOR-PROFIT ENTERPRISE IN HEALTH CARE is part of a system versus being an independent (autonomous) hospital rather than due to their type of ownership (investor-owned versus not- for-profit). With this limitation in mind, these two types of facilities were found to be similar with respect to meclical stab composition, av- erage length of stay by service, the majority of mortality/morbidity and utilization mea- sures, educational programs, and accreditation status. However, significant differences were found in the gross death rates, admissions from emergency, and some patient treatments. Not-for-profit independent hospitals had a significantly greater gross death rate than the investor-owned system facilities. The authors speculate that this may be because the not- for-profit independent facilities are admitting patients with more serious medical conditions. Supporting this possibility of case-mix differ- ences are their findings that not-for-profit in- dependent hospitals have a significantly greater tendency to admit patients who were first seen in their emergency rooms. Furthermore, investor-owned system facilities were re- ported to provide significantly less therapeutic and occupational therapy treatments per in- patient day than the not-for-profit, indepen- dent hospitals. The investigation by Ruchlin et al. (1973) is one of the few extensive empirical studies in the literature that addresses the differences between system and autonomous hospitals. In addition to the confounding variable limita- tion, however, there are several other limi- tations of this study for drawing conclusions. The results are based on samples that have been matched on organizational characteris- tics, such as size, which would restrict the number of differences that may be found. The exclusion of not-for-profit system hospitals and autonomous investor-owned hospitals from this study affects the ability to generalize the study results. The study used 1970 data and, given the dynamic environment of health care de- livery, similar results may not be found in the 1980s. In addition, no distinction was made between religious affiliation within the two comparison groups that were used. Indeed, there are a variety of organizational variables that may result in differences in a hospital's provision of quality patient care. As one example, Sloan and Vraciu (1983)

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COMPLIANCE WITH MICAH STANDARDS showed that "ownership (investor-owned ver- sus not-for-profit) is a poor predictor of a hos- pital's willingness to treat low-income patients, costs to the community, and profitability." However, they compared investor-owned sys- tem hospitals and all not-for-profit hospitals only. Therefore, they have the same limitation as Ruchlin et al. (1973) in that it is possible that differences found might be related to whether a facility is part of a system or not, independent of their ownership status. In ad- dition, there may be differences found withm the not-for-profit group such as Catholic sys- tems compared to voluntary nonreligious sys- tems. Because of the variety of multihospital sys- tem types, the most accurate assessment of their impact on quality would need to differ- entiate between the different organizational structures found in systems. The most com- prehensive evaluation of quality would include the evaluation of all varying types of structures in order to better determine what feature may be responsible for differences found. The pres- ent study investigates differences in quality of care between multihospital systems and au- tonomous hospitals and includes the evalua- tion of a variety of organizational characteristics that may be important in the appropriate as- signment of factors contributing to differences found. RESEARCH QUESTIONS Compliance with loins Commission on Ac- creditation of Hospitals dCAH) standards in- dicates that accredited facilities have mechanisms in place that help an organization to provide quality care. The W. K. Kellogg Foundation-fianded project, "New Frontiers in Patient Care Assessment," has as one of its objectives to "explore the implications of the growth and development of multihospital sys- tems for ICAH standards and develop appro- priate changes in these standards." The assumption implicit in this objective is that because most ICAH standards were developed before the extensive grown of hospital sys- tems, they may not adequately reflect proper system structure and function and, thus, result in an inappropriately high level of noncom- pliance. 377 As one attempt to meet this objective, the present study investigates differences be- tween system and autonomous hospitals' com- pliance with ICAH standards. The broad research questions investigated are What Adherences, if any, exist between autonomous hospitals and hospitals that are members of a multihospital system with re- spect to compliance with ICAH standards? If differences are found, what hospital characteristics are associated with these dif- ferences? METHODS The research data set constructed to assess issues of comparative performance between system and autonomous hospitals includes se- lected ICAH variables and AHA variables from the AHA Annual Survey of Hospitals. The lat- ter data were used to measure specific orga- nizational characteristics not included in the ICAH data set. The ICAH hospital survey rec- ords selected for inclusion in this data set were all hospitals surveyed between June 1, 1982, and May 31, 1983. The JCAH data set contains variables collected at the time of an accredi- tation survey. This combined data set has been chosen for analysis because it represents the most recent complete year of data during which the process of data collection was nearly uni- form. The total number of hospitals surveyed by the JCAH's Hospital Accreditation Program during this time period is 1,657. Ofthese 1,657 facilities, 1,202 were autonomous and 455 were system hospitals. An initial comparison was made of accred- itation outcome measures (accredited without contingency, accredited with contingency, and nonaccredited). Next, an analysis was con- ducted of specific standard areas (or standard Survey Report Form iSRF] items) which con- tributed to determining accreditation out come. To facilitate the analysis of compliance with these numerous survey items, a standards compliance severity scale was used (see Table 11. On this scale, degrees of compliance with standards for each variable are ranked in ascending order from 1 (full compliance) to 5

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378 FOR-PROFIT ENTERPRISE IN HEALTH CARE TABLE 1 Standards Compliance of the hospitals surveyed receiving less than Seventy Scale full compliance and/or missing values, divided Rank Order Code by the total number of items in that section, was calculated similarly to the methods used Compliant 1 in previous analyses of ICAH standards (Longo Recommendationa 2 et al., 1983~. Recommendation of significance 3 Recommendation with special warning 4 Recommendation with contingency 5 SYSTEM/AUTONOMOUS DIFFERENCES aThis category excludes recommendations with contingencies, with special warnings, and of sig nificance. (sufficient degree of noncompliance to warrant a contingency), thus permitting ready identi- fication of the seventy of each recommenda- tion. A contingency is defined as a recommen- dation of serious enough merit that failure to comply within a specific time period may cause nonaccreditation. The first step of SRF item analysis was to determine the percentages of total hospitals receiving each score (1-5) on each item. As a result of this procedure, it was possible to ob- tain a ranking of items that have the greatest number of hospitals receiving recommenda- tions (2-4) and a second ranking of items that have the greatest number of hospitals receiv- ing contingencies in other words, the stan- dard areas with which hospitals have the most difficulty in compliance. This ranking was identified for system and autonomous hospitals separately so that differences in their 25 top problem areas could be investigated. These top problem SRF items for both recommen- dations and contingencies were then grouped into their standard chapters. SRF items are grouped into sections that relate directly to chapters in the Accreditation Manualfor Hos- pitals. In order to identify those items likely to discriminate between system and autonomous hospitals, the second step of the SRF item analysis was conducted. Thus, items were identified in which 10 percent or more of the hospitals surveyed were in less than full com- pliance. (Items with greater variance in the scores would be more likely to differentiate between types of hospitals.) A ranking of sections by the number of items within each section that had 10 percent or more Cross-tabulations and chi-square tests of sig- nificant associations were calculated compar- ing system and autonomous hospitals for each item that passed the variance screen, that is, those items in which 10 percent or more of the hospitals had less than full compliance. The 1-5 severity scores were first collapsed into three categories: compliance (1), recom- mendations (2, 3, 4), and contingencies (5) in order to eliminate cells with too few entries to allow for valid statistical test results. A sec- ond set of cross-tabulations and chi-square tests were calculated for items in which the severity scores were collapsed into two categories: compliance (1) versus all other recommenda- tions and contingencies (2-5~. The two cate- gories were used to further eliminate the problem of small cells. Items were identified in which a significant association was found between the item score and the type of hospital (system or autono- mous). By grouping the items into the SRF sections, rankings were obtained of the SRF sections by the percent of total possible items that were significant. This percentage was cal- culated by dividing the number of items sig- nificant at the p <.05 level by the total number of items in each section. For those items found to be significant, the specific hospital type hav- ing better compliance with JCAH standards was identified. The methods discussed above provide a de- scription of differences between system and autonomous hospitals. However, while these differences do resect the status offacilities sur- veyed by the ICAH, they do not control for potential confounding variables. It is possible that the differences identified between system and autonomous hospitals may, in fact, be due to other characteristics associated with these organizational structures, rather than ~ direct result of the organizational structures them- selves.

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COMPLIANCE WITH MICAH STANDARDS To investigate these associations, bivanate analyses were conducted comparing organi- zational variables from the AHA Annual Sure hey of Hospitals with hospital type (see Appendix). Because of the high percentage of investor-owned hospitals that are members of systems, accreditation outcomes were com- pared among four groups: system/investor- owned, system/not-for-profit, autonomous/ investor-owned, and autonomous/not-for-profit. Similar to the two-way comparisons be- tween system and autonomous hospitals, the differences in compliance performance among these four groups may be influenced by con- founding variables and, therefore, represent only a preliminary description of these differ- ences. Controlling for possible confounding variables in multivariate analyses may affect the differences identified through these meth- ocls. As a result, regression analyses were con- ducted to determine which organizational variables were He best predictors of accredi- tation outcome. RESULTS This section of the paper describes the re- sults of the analytic process outlined above. The findings are divided into two sections: (1) compliance with ICAH standards and (2) accreditation decisions. Compliance with ICAH Standards In a typical hospital, ICAH asks approxi- mately 2,400 questions, each designed to judge compliance with specific JCAH standards. In developing data sets across a 2-year period, 2,045 questions remained. The difference re 379 fleets changes in the survey report form from 1982 to 1983. In the analysis of the 1,657 hos- pitals in this study, there were 942 questions for which more than 10 percent of these hos- pitals were not in full compliance. When one analyzes these questions for differences in compliance between system and autonomous hospitals, only 34 items show significant var- iation. These data are summarized in Table 2. These findings indicate that there are very few differences found between system and auton- omous hospitals in meeting ICAH standards. However, when these few differences are found, in virtually all cases the system hospitals were found more in compliance with JCAH stan- dards. Top 25 Contingency Items The results show that for system hospitals, He percent that received contingencies for one or more ofthese items ranged from 8.6 percent to 26.8 percent. For autonomous hospitals, the percent that received contingencies for these items ranged Dom 11.5 percent to 29.6 per- cent. The vast majority of the top 25 items re- sulting in contingencies, 23 out of 25 (92 per- cent), were the same for both system and autonomous hospitals, although not necessar- ily in the same rank order. The two SRF items resulting in the highest level of contingencies were the same for both types of hospitals: 1. Do the minutes of the monthly medical staff or department/major clinical service meetings document the recommendations, conclusions, and action instituted, resulting Dom the review of the care and treatment of patients served by the hospitals? TABLE 2 Items with Variation Between Autonomous Hospitals and Hospitals in Multihospital Systems Data Set Number Percent Total questions asked 2,045 100.0 Questions with possible variance 942 46.0 (942/2,045) Significant differences between 34 system/autonomous 3.6 (34/942) System more compliant 33 97.0 (33/34) Autonomous more compliant 1 3.0 (1/33)

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380 FOR-PROFIT ENTERPRISE IN HEALTH CARE TABLE 3 Comparisons of Accreditationwere in the top 25 contingencies for system Decisions by Hospital Type (percentage)hospitals but not autonomous hospitals were 1. Is there evidence of medical staffpartic ipation in medical record review relative to clinical pertinence? 2. Is there evidence of medical staffpartic ipation in medical record review relative to the record's completeness and adequacy as a medico-legal document? The two items that were in the top 25 con tingenc~es for autonomous hospitals but not system hospitals were 1. Is there evidence that drug utilization and effectiveness in the hospital are reviewed? 2. Is a regular review and evaluation of the quality, safety, and appropriateness of care provided by the renal unit performed and doc umented? Type of Hospital Accredited Accredited Without win Contin- Condn- Not gencies gencies Accredited gencies System 44.8 54.3 Autonomous 36.9 61;9 0.9 1.2 2. Is the ongoing review of antibiotic use documented? For the first item, 26. 7 percept of the system hospitals received contingencies; 29.6 percent of the autonomous hospitals received contin- gencies. For the documentation of antibiotic review, 24.2 percent of system hospitals re- ceived contingencies; 24.4 percent of the au- tonomous hospitals received contingencies. The next two items resulting in contingen- cies in the top 25 were also the same for both system and autonomous hospitals although not in the same order: ~_ , ~ 1. Has there been action taken on the find- ings of the antibiotic use reviews made? 2. Is a regular (quarterly) review and eval- uation of the quality, safety, and appropriate- ness of care performed and documented for each special care unit? Actions taken on antibiotic review findings ranked third for system hospitals, and 21.3 percent received contingencies. This item was ranker] fourth for autonomous hospitals, and 21.4 percent received contingencies. Review and evaluation of special care units ranked fourth for system hospitals, and 16.9 percent received contingencies. This item was ranker} third for autonomous hospitals, and 23.2 percent received contingencies. The results indicate that when hospitals experience di~- culty in complying with ICAH standards, as measured by the receipt of a contingency, sys- tem arid autonomous hospitals are very simi- lar. When the top 25 SRF items that result in contingencies were compared between system and autonomous hospitals, only 8 percent (2/ 25) of the items di$erecl. The two items that Accreditation Decisions When final accreditation decisions were compared, the majority of both system and autonomous hospitals were found to be ac- credited with contingencies (54 percent and 62 percent, respectively). However, system hospitals were found to be accredited without contingencies significantly more frequently than autonomous hospitals (p = .013), as shown in Table 3. Looking at these data in more detail, we grouped hospitals into four categories and ana- lyzed the accreditation outcomes for each, as shown in Table 4. These results indicate that autonomous, not- for-profit hospitals tend, as far as contingencies are concerned, to have greater difficulty in complying with JCAH standards, whereas au- tonomous investor-owned tend to have a higher level of nonaccreditation. However, it is im- portar~t to note that there was not a statistically significant association found between hospital type and accreditation outcome. Multivariate Analyses In the preceding analyses, all relevant fac- tors (independent variables) were identified and explored as to their impact or contribution to accreditation outcome. In addition, step

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COMPLIANCE WITH CASH STANDARDS TABLE 4 Analysis of Accreditation Outcome Accreditation Outcome Hospital Type Accredited Accredited Without with Contingencies Contingencies Not Accredited System/investor-owned 48.1 51.3 0.6 System/not-for-profit 43.2 55.8 1.0 Autonomous/investor-owned 46.4 51.8 1.8 Autonomous/not-for-profit 36.5 62.4 1.1 wise linear multiple regression was run to disentangle the confounding effects of the independent variables on the dependent vari- able. For example, when all variables are taken together, what variables best predict accred- itation outcome? In addition, it is important to determine the strength of the relationship of the predictors; this is accomplished by a review of the "beta coefficients" that represent the correlation or strength of the association found through the use of the multiple regres- sion. The independent variables that were en- tered into the regression based on the descriptive analyses are listed with their def- initions in the Appendix. Table 5 lists those variables that multivariate analysis indicate are the better predictors of accreditation. The variables are listed in de- scending order by the absolute value of the size of the 'beta coefficient"; thatis, the strength of the association measured after all variables are taken into consideration. The sign of the coefficient (positive or negative) indicates the direction of the relationship. Accreditation outcome is the dependent variable in the equation. The results indicate that the best predictors of accreditation outcome are related to pop- ulation size (as indicated by the SMSA [stan- dard metropolitan statistical area] categories), region of the country (East Central, West Cen- tral, West South Central, and Mountain), community hospital status, multihospital sys- tem state and bed size. However, the di- rection of the coefficient must be taken into account. Thus, hospitals are more likely to be accredited without contingencies that are lo- cased in areas with a population size of be- tween 100,000 to 2,500,000 (indicated by the 381 positive coefficient of SMSA categories 4, 5, 3, and 2), a member of a multihospital system, not located in the West Central, East Central, West South Central, and Mountain regions (indicated by the negative coefficients), smaller bed size, and noncommunity hospitals. No- tably, neither for-profit nor not-for-profit sta- tus is a good predictor of accreditation outcome. In fact, the f value of the profit variable was not significant in any step of the multiple regression. Because profit status is somewhat correlated with the multihospital variable (0.392), profit status may directly influence ac- creclitation outcome, but does not add any ad- ditional predictive value to the equation after the multihospital variable is included. Also of note is that there are other variables that may predict accreditation outcome that TABLE 5 Variables that Remain in Regression Equation . Variable Beta Coefficient SMSA 4 (500,000 - 1,000,000)a SMSA 5 (1 million-2.5 million) West Central region East Central region West South Central region SMSA 3 (250,000-50.0,000) Mountain region SMSA 2 (100,000 250,000) Community hospital Multihospital system Large bed size a Standard metropolitan statistical area (SMSA) cat- egory. + 0.09222 + 0.09082 - 0.08198 - 0.07952 -0.07403 + 0.06435 - 0.06415 + 0.05848 -0.05808 + 0.05617 - 0.05562

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382 were not examined in this analysis. Further- more, the SMSA and regional variables may be viewed as "dummy" variables that capture a wide variety of"environmental" character- istics such as hospital location, level of com- petition, numbers of patients, and He presence of resources that may possibly contribute to the ability of hospitals to comply with stan- dards. For example, it may be possible that the negative coefficient for the East North Central region may actually measure the pres- ence of older hospitals. However, the present analysis is unable to assess the specific char- acteristic of region or population size that in- fluences the variables' ability to serve as a predictor of accreditation outcome. Future analyses should explore the impact of more specific characteristics upon accreditation out- come. In summary, it is important to emphasize that the numerous characteristics' impact on a hospital's ability to comply with ICAH stan- dards and that any of the results taken in iso- lation would not accurately represent the complicated profile of a hospital accredited without contingencies. Multivariate analyses assist in controlling for confounding variables, but may mask the possible direct influences of correlates of the variables that are included in the equation as the best predictors. L~I1IATIONS Limitations of this study include the exclu- sive use of available national data from the ICAH and AHA that measure structure and process. Therefore, outcome measures such as morbidity/mortality and patient satisfaction are not taken into account. There are also limitations of the data set used, in particular the deletion of SRF items as the result of changes in ICAH standards during the study penod. Thus, the impact of standards deleted or implemented during the study period cannot be determined. In addition, it is not known whether systems acquire hospitals that were previously better or worse in complying with JCAH standards. Thus, additional research should use a longi- tudinal approach in investigating the impact of pre-acquisition accreditation history upon FOR-PROFIT ENTERPRISE IN HEALTH CARE subsequent facility compliance as part of a sys- tem. SUMMARY AND CONCLUSIONS Previous investigations have found that the organizational structure of a hospital may im- pact on performance. The present study ex- amined differences in compliance with ICAH standards between the two predominant or- ganizational structures system and autono- mous hospitals. A multistep approach was taken to investigate the two research questions out- lined. These steps included descriptive statis- tics, including frequencies and percentages, the use of bivariate chi-square statistics, mul- tiple comparison groups, and multiple regres- sion. The present study resulted in several find- ings concerning compliance and accreditation. Compliance with Individual Standards There was a remarkably similar level of com- pliance between system and autonomous hos- pitals. This is indicated by the following: Of the 2,045 items asked, there were only 46 percent (942) for which 10 percent or more of the hospitals showed important levels of noncompliance. Of the 942 items, only 3.6 percent (34) showed differences between sys- tem and autonomous hospitals and in 97 per- cent (33) of these, system hospitals showed significantly higher levels of compliance than autonomous hospitals. ~ When the top 25 SRF items which re- sulted in contingencies were compared be- tween system and autonomous hospitals, only 8 percent (2) of the items differed. Accreditation Decisions When final accreditation decisions were compared, the majority of both system and autonomous hospitals were found to be ac- credited with contingencies (54 percent and 62 percent, respectively). However, system hospitals were found to be accredited without contingencies significantly more frequency Han autonomous hospitals (44.8 percent and 36.9 percent, respectively; p = .013~.

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COMPLIANCE WITH]CAH STANDARDS The set of predictors of accreditation out- come are population size, region, community hospital status, multihospital system status, and bed size. Thus, hospitals accreditecl without contingencies are more likely to be located in areas where the population is between 100,000 and 2,500,000 people, located in a region other than the East Central, West Central, West South Central, and Mountain, not a commu- nity hospital, a member of a multihospital sys- tem, and of smaller bed size. Finally, these findings do not support the suspicion that ICAH standards may possibly discriminate against hospitals that are part of multihospital systems. ACKNOWLEDGMENTS Funded in part by a grant from the W. K. Kellogg Foundation, Battle Creek, Michigan. The authors wish to acknowledge the com- puter support of Teri Grey and Laura Merri- gar~ and the secretarial support of Betty Johnson. REFERENCES Brown, Montague (1979) Systems development: Trends, issues and implications. Health Care Manage- ment Review 4 Winter:l. Dowling, William J. (1983) Muldhospital systems We growth, constraints, unexploited options. Hospital Progress (Apri1~:48-53. Ermann, Dan, and Jon Gabel (1984) Multihospital systems: Issues and empirical findings. Health Affairs 3~11:51-64 Fleming, G. V. (1981) Hospital structure and con- sumer satisfaction. Health Services Research 16~1~:43- 63. 383 Georgopoulos, Basil S. (1972) The hospital as an or- ganization and problem solving system. In Georg- opoulos, Basil S. (ed.), Organizational Research on Health Institutions. Ann Arbor: Institute for Social Re- search, University of Michigan. Kaluzny, Arnold D. (1982) Present and fixture re- search on multihospital systems. Health Services Re- search 17~4~:331-336. Kaufman, Kenneth, Stephen Shortell, Selwyn Becker, and Duncan Neuhauser (1979) The ejects of board composition and structure on hospital performance. Hospital and Health Services Administration. 2441~:37- 62. Longest, Jr., B. B. (1980) A conceptual framework for understanding the multihospital arrangement strat- egy. Health Care Management Review 4(Winter3:17- 23. Longo, Daniel R. (1982) A Case-Control Study of the Structural Determinants of Hospital Closure. Bi1- timore: The Johns Hopkins University. Doctoral dis- sertation. Longo, Daniel R., and Gary A. Chase (1984) Struc- tural determinants of hospital closure. Medical Care (May):338-402. Longo, Daniel R., Donald E. Widmann, and Peter Van Schoonhoven (1983) lCAH forum: Compliance with JCAH standards: National findings from 1982 surveys. Quality Review Bulletin 10~3~:81-86. Ruchlin, Hirsch S., Dennis D. Pointer, and Lloyd L. Cannedy (1973) A comparison of for-profit investor- owned chain and non-profit hospitals. Inquiry lO(December):13-23. Becker, S. W., and D. Neuhauser (1976~1he ejects of management practices on hospital efficiency and quality of care. Pp. 90-107 in Shortell, S., and M. Brown (eds.), Organizational Research in Hospitals. Chicago: Blue Cross Association. Sloan, Frank A., and Robert A. Vraciu (1983) Inves- tor-owned and not-for-profit hospitals: Addressing some issues. Health Affairs 2(Spring):25-37.

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384 FOR-PROFIT ENTERPRISE IN HEALTH CARE APPENDIX Variables Entered into the Regression Variable Definition Average length of stay Average stay of inpatients during the reporting period. Community hospital All nonfederal short-term general and other special hospitals, excluding hospital units of institutions whose facilities and services are available to the public. This includes a variety of facilities including university hospitals (N = 5,9001. Multihospital system An acute care facility owned or leased by a system, including acute care facilities operated by the Veterans Administration. Investor ownership A facility that is for-profit and operated by an individual, partnership, or corporation. Region of the country Standard metropolitan statistical area Occupancy Bed size Number of registered nurses Percent Medicaid patients The nine U.S. Census regions. The seven categories designed by population size arranged in ascending order of population. Ratio of average daily census to the average number of beds, that is, statistical beds, maintained during the reporting period. Number of beds set up and staffed for use in hospitals. The number of full-time registered nurses on the hospital payroll. The number of Medicaid patients divided by the total number of pa- tients times 100. Percent Medicare patients The number of Medicare patients divided by the total number of pa tients times 100. Control The types of organization responsible for establishing policy concerning the overall operation of hospitals. ILe four major categories are gov ernment, nonfederal; nongovernment, not-for-profit; investor-owned (for-profit); and government, federal.