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Cost structure of osteopathic hospitals and their local counterparts in the USA: Are they any different?
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Social Science & Medicine 60 (2005) 1805–1814 www.elsevier.com/locate/socscimed
Cost structure of osteopathic hospitals and their local counterparts in the USA: Are they any different? Tony Sinay Health Care Administration, College of Health and Human Services, California State University, Long Beach 1250 Bellflower Boulevard, Long Beach, CA 90840-4902, USA Available online 11 November 2004
Abstract Due to the emphasis on preventive care and less invasive solutions to medical problems, osteopathic hospitals may deliver cost efficient and cost effective care. This study examines the cost structure of osteopathic hospitals and compares their performance to a local control group selected from allopathic hospitals. Osteopathic hospitals are identified in the 1999 American Hospital Association (AHA) data and matched to local allopathic hospitals with respect to location, bed size, system, for-profit and teaching status. Cost functions are estimated for both groups of hospitals, and significant differences in input, output and costs are highlighted. Results show that osteopathic hospitals are more costly and less productive in comparison to their counterparts. Inefficient production of outpatient services and high cost of medical education are two reasons for the poor performance. The study has important policy implications on two fronts: first, osteopathic hospitals are more costly to operate than their counterparts, and subsequently this requires further analysis of the osteopathic treatments and techniques. In an environment where health care revenues are shrinking and costs are rising, this is probably much needed information for osteopathic hospitals. Secondly, there is an emerging concern among osteopathic medical schools and osteopathic physicians due to the declining number of osteopathic hospitals, which translates to a smaller number of residency positions for osteopathic medical school graduates. Analyzing cost, input and output variables reveal some of the contributing factors to the decline of osteopathic hospitals and help preserve this rich tradition. r 2004 Elsevier Ltd. All rights reserved. Keywords: Osteopathic hospitals; Cost functions; Operating efficiencies; Labor and capital costs
Introduction The osteopathic medicine emphasizes the concept of ‘‘wellness and preventive medicine,’’ and focuses on each person’s health risks such as smoking, high blood pressure, excessive cholesterol levels, stress and other lifestyle factors. Due to the emphasis on preventive care and less invasive solutions to medical problems, osteopathic physicians and hospitals may deliver a cost Tel.: +1 562 985 5694; fax: +1 562 985 5536.
E-mail address: [email protected] (T. Sinay).
efficient care. Andersson et al. (1999) showed that osteopathic manipulative care and standard medical care have similar clinical results in patients with subacute low back pain; however, the use of medication and physical therapy was greater with the standard care and differences in costs were significant. The focus of this paper is a group of facilities that distinguish themselves as osteopathic hospitals. Although the classification is provided in the American Hospital Association (AHA) Annual Survey of US Hospitals, research that compares these providers to other type of hospitals is rather limited or non-existent.
0277-9536/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2004.08.042
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There is anecdotal evidence that osteopathic hospitals located in 31 states with more than 25,000 beds; about 825,000 patient admissions; 6.3 million patient days; 3.1 million outpatient visits to hospital’s outpatient departments each year for emergency and other ambulatory care, published in an American Osteopathic Association (AOA) brochure. The AOA Directory serves as a more reliable source for these facilities (The American Osteopathic Association, 1999). In 1999, there were about 129 facilities listed as osteopathic hospitals. In the 1990s, a number of osteopathic hospitals disappeared through mergers and closures, losing their unique identity. From 1989 to 1999, 44 osteopathic hospitals were eliminated from the AOA Directory, about 25 percent decrease. The slowing of the growth in reimbursement in the late 1980s forced many financially threatened hospitals to close. From 1986 to 1995, 533 hospitals went out of operation (Bass, 1997). Many of these were small, rural hospitals. Surprisingly, many closed rural hospitals were more efficient at the time of closure than non-closing hospitals. Weak rural economies and lack of demand for hospital services were found to be contributing factors for rural closures (Sinay, 1998a; Lynch & Ozcan, 1994). Most osteopathic hospitals are located in rural areas. Sixteen of 50 osteopathic hospitals included in this study were located in non-metropolitan areas, approximately 32 percent. Ten other osteopathic hospitals in this study came from small metropolitan areas where the population was between 100,000 and 500,000 (AHA Data Files, 1999; Annual Survey of US Hospitals, 1999). This study examines the cost, input, output and efficiency indicators of osteopathic hospitals, and compares their performance to a local control group selected from allopathic hospitals. Osteopathic hospitals are identified in the 1999 AHA data, and later matched to local allopathic hospitals with respect to location, bed size, system, for-profit and teaching status. Cost functions are estimated for both osteopathic and matching control hospitals, and significant differences in cost function parameters are identified. Using paired sample t-test, additional analysis was done to explain differences in price and output efficiencies. The study may have important policy implications on two fronts: first, today’s hospital managers have to identify the best strategies for dealing with the financial weakening of the industry and the environmental challenges they are likely to continue to face over the next decade. Osteopathic hospitals may represent an effective philosophy to medicine and the hospital management in particular. Operational efficiencies of osteopathic hospitals in comparison to their counterparts may trigger further investigation of the osteopathic treatments and their management practices.
Secondly, there is an emerging concern among osteopathic medical schools and osteopathic physicians that the number of osteopathic hospitals is declining which eliminates already limited residency positions for the graduates of osteopathic medical schools. Identifying the strengths and weaknesses of osteopathic hospitals may provide survival tips to the managers of osteopathic hospitals since prospects for hospital profitability continue to look poor, and the challenges facing the industry are not temporary but an ongoing phenomenon according to Moody’s Investors Service (2000). Besides the use of a matched case-control design where omitted local market and environmental effects are likely to be the same for each osteopathic and allopathic hospital, this study departs from the previous investigations of hospital efficiency studies in several ways. First, it estimates a multiproduct cost function to measure relative efficiency of osteopathic and allopathic hospitals. Using a dummy-variable approach, significant differences in cost function parameters were tested. In the descriptive part of the study, hospital labor inputs by each job category, which provides insights into labor costs, and capital costs by bed size by hospital service were analyzed. Lastly, the study developed several efficiency indicators to accurately measure and compare operating efficiencies of osteopathic and allopathic hospitals. The primary research questions addressed in the study are:
What differences in output and input price elasticities exist between osteopathic and allopathic hospitals?
What differences in labor input are observed between osteopathic and allopathic hospitals? differences in capital input are observed between allopathic and osteopathic hospitals? Do differences in labor and capital inputs affect costs of osteopathic and allopathic hospitals?
What
Osteopathic philosophy and research foundations The science of osteopathy dates back to 1874 when Dr. Andrew Taylor Still first laid out the foundations of osteopathic medicine. Dr. Still pioneered the concept of ‘‘wellness’’ about 100 years ago, and pursued holistic and patient-centered medicine with a focus on preventive care medicine. He studied each person’s health risks such as smoking, high blood pressure, excessive cholesterol levels, stress and other lifestyle factors (Lister, 1983). Osteopathic medicine is a primary-care centered approach to medical, surgical, obstetrical and other health services founded on a philosophy embracing the importance of hands-on evaluation and treatment of the total person and dedicated to improve health care for all people. Osteopathic theory focuses on the whole body
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which comprises five major points (Northup, 1975; Glover & Rivers, 2000).
unity of the body, the healing power of nature, somatic components of disease, structure–function concept and manipulative theory. The body functions as a unit with existing interrelationships among all body systems and possesses selfregulatory mechanisms which have an inherent capacity to health and repair itself. The body’s equilibrium preserves health and protects against disease. Osteopathic philosophy points out that structure and functions of body are not separable in understanding human physiology, and the musculoskeletal system is similar to a machine, serving a common function. All parts must be operable, anatomically and physiologically, for body to be healthy to fully function. The use of osteopathic manipulation where the bones, muscles, tendons and connective tissue can be manipulated to improve the range of motion and promote blood flow through tissues to enhance the body’s own healing powers become the blueprints of osteopathic medicine (Howell, 1999). Due to the holistic philosophy described above, osteopathic hospitals may have better operating efficiencies over others that result in cost effective care.
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Hospital Association (AHA) Annual Survey of U.S. hospitals suggests otherwise. Osteopathic hospitals are accredited by the AOA-Healthcare Facilities Accreditation Program whereas allopathic hospitals are accredited by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO). The requirements for accreditation by the AOA and the Joint Commission are similar. Both require licensed medical staff, which includes nurses and physicians; patient care standards regarding treatment and privacy; organizational structure involving leaders of each department with a hospital CEO, and emergency plans for natural disasters or utility failures. Although the educational background of the medical staff varies, both the AOA and the Joint Commission emphasize continuing education. Additionally, the AOA requires an osteopathic physician (s) on staff, performing an osteopathic musculoskeletal examination at osteopathic hospitals, and a committee called ‘‘Utilization of Osteopathic Methods and Concepts Committee’’ to promote effective methods for osteopathic diagnosis and treatments. This committee is expected to hold meetings quarterly (The American Osteopathic Association, 2001; The Joint Commission on Accreditation of Healthcare Organizations, 2001).
Methods Data and matching process
Literature review Many of the past efficiency studies relevant to this study compared multi-hospital system hospitals with independent hospitals on measures of hospital specific performance using descriptive statistics and multivariate regression analysis (Alexander & Morrisey, 1987; Ermann & Gabel, 1985; Levitz & Brooke, 1985; Shortell, 1988; Becker & Sloan, 1985). Economic performance of investor-owned chain and not-for-profit hospitals were also compared in several studies. Watt et al. (1986) concluded that investor-owned chain hospitals generated higher profits through more aggressive pricing practices rather than operating efficiencies. In a different study, proprietary hospitals were found to have the highest profit margins for ancillary services and not-forprofit hospitals had the highest profit margins for daily hospital services (Eskoz & Peddecord, 1985). A recent study compared pre- and post-merger efficiency of merging hospitals to a local control group in operating efficiencies, and concluded that merging hospitals had much higher labor costs in the pre-merger period than their counterparts, and similar costs after the merger (Sinay & Campbell, 2002). The literature does not distinguish osteopathic hospitals as a separate group of hospitals, but the American
First, osteopathic hospitals are identified from the 1999 AHA data and are matched to allopathic hospitals with respect to the following criteria:
have approximately the same number of staffed beds, be in the same metropolitan statistical area (MSA), have the same type of ownership, for-profit or not
for-profit (however, this assumption is later relaxed to find matching hospitals in the same MSA), have the same system affiliation status as the osteopathic hospital and have the same teaching status.
The data sample included 50 osteopathic hospitals along with 50 matching controls which are members of the AHA. Case mix index was obtained from the Federal Register. Two osteopathic and two allopathic hospitals did not report their case mix index in 1999. The 1998 case mix index table was tried to obtain the missing information but there was no luck with this attempt. Instead, we used the matching hospitals case mix index as a proxy. Area wage index is also reported in the Federal Register. One osteopathic and one allopathic hospital fail to report this information. Matching area wage index is also used to keep these hospitals in the data analysis due to the relatively small sample size.
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To control for market effects, most studies used several, randomly selected hospitals as controls for ongoing operational changes emanating from the environment. In studies where controls were selected from representative hospitals nationwide, additional variables to account for local environmental influences that pertain to each control hospital were added to the model. Because of the limitations imposed by a small sample size in this study, an alternative strategy was adopted. We started the matching process by classifying hospitals into different bed-size categories. For this purpose the AHA bed-size classification system was used which included eight size classes. The largest osteopathic hospital had 325 beds and the smallest had 12 beds. Since the largest osteopathic hospital had 325 beds, only six of the eight bed-size classes were relevant for the study (Sizes 7 and 8 are hospitals with more than 400 beds). We started out with 54 osteopathic hospitals and 6602 allopathic hospitals nationwide. One osteopathic hospital was automatically dropped from the sample due to being a specialty hospital. About 1350 specialty and federal government hospitals were discarded from the national sample. A total of 4702 allopathic hospitals were potential matches to 53 osteopathic hospitals on the basis of bed-size. A computer program was utilized to obtain perfect matches for osteopathic hospitals, which included five control factors: bed size, MSA, ownership, system and teaching status. A total of 4465 non-osteopathic hospitals were dropped from the sample for random matching. There were 18 osteopathic facilities for which a local matching hospital could not be found in the same MSA. For those facilities matching allopathic hospitals were sought by expanding their MSA to the Consolidated Metropolitan Statistical Areas (CMSA). Matches were found for 15 of the 18 osteopathic facilities within the same CMSAs. The other three facilities for which a match was not found within the same CMSA were discarded from the analysis. The final one-to-one matching was performed by randomly selecting 50 matches from a list of 237 non-osteopathic candidates. Since there was only one non-osteopathic within the 300–399 bed-size group, the non-osteopathic match for this bed-size class was automatic without random selection. Once the selection process was completed, three control hospitals were found to be selected twice by the computer program. This was handled by replacing those three control hospitals with new controls. Cost function parameters Cost functions provide theoretically grounded models for measuring efficiency (Vita, 1990; Cowing & Holtmann, 1983; Fournier & Mitchell, 1992). Multiproduct cost functions have been introduced by Grannemann,
Brown, and Pauly (1986) and Conrad and Strauss (1983), and applied to hospitals by Fournier and Mitchell (1992), Vita (1990), and Sinay (1998b). These cost functions vary from structural models to behavioral models, and the combinations of the two, hybrid translog cost functions, which allow researchers to compute ray economies of scale and scope. Baumol, Panzar, and Willig (1982) defined the theoretical foundations of multiproduct cost functions, and described scope and scale economies for economic efficiencies. The above studies served as reference for the selection of the multiproduct cost function and its variables. The following cost function is estimated for both osteopathic and matching control hospitals: Ln TOTCOST ¼ a0 þ a1 Ln TOTIPD þ a2 Ln SURG þ a3 Ln TOTOUT þ b1 Ln WAGE þ b2 Ln SUPPLIES þ Ok Ln BEDS þ y1 CASEMIX þ y2 SYSTEM þ y3 PROFIT þ y4 CONTRACT þ y5 AREAWAGE þ ; where TOTCOST is the total variable cost,1 TOTIPD is the inpatient days, TOTSURG is total inpatient and outpatient surgeries, TOTOUT is the outpatient visits; average salary is controlled by WAGE and the average price of supplies is controlled by SUPPLIES;2 BEDS is the number of staffed beds, representing the fixed capital; PROFIT and SYSTEM are dummy variables for proprietary and system affiliation status, respectively; CONTRACT is another dummy variable for hospitals under contractual arrangements; CASEMIX measures the average severity of patients at each facility, and AREAWAGE is an index to control for labor market conditions and environmental factors. Methodology The study consists of two parts. First, separate cost functions for osteopathic and allopathic hospitals are estimated. In order to test differences in estimated coefficients between these two functions, data sets of osteopathic and matching control hospitals are pooled, and an intercept dummy (DUMMY=1 for osteopathic hospitals) and a slope dummy for each estimated coefficient (DUMMY*TOTIPD, DUMMY*TOTSURG, DUMMY*TOTOUT, DUMMY*BED1 Cv is the total variable cost which includes labor and supply costs. Labor costs include total payroll expenses, employee benefits, and professional fees. 2 The average salary (WAGE) is computed by dividing total labor costs by the FTE. Average price of supplies (SUPPLIES) is obtained by dividing total supply costs by adjusted inpatient days.
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Syyyyy) are introduced (Pindyck & Rubinfeld, 1991).3 This new pooled cost function is used to test for significant differences in two cost functions estimated earlier. Theoretically, cost function parameters from the pooled model are linear combinations of those estimated in two separate functions. In the second part of the study, a descriptive analysis is performed to explain findings from the cost function estimates. The study compares hospital labor and capital inputs, and develops several efficiency indicators to descriptively measure and compare operating efficiencies of osteopathic and allopathic hospitals. Data on inputs, outputs and costs for each osteopathic and control hospitals are obtained which included:
full time equivalent employees (FTEs), the number of full time hospital personnel, grouped into certain categories (see Table 1),
the number of part time hospital personnel, grouped into certain categories (see Table 1),
FTEs per bed, average compensation per FTE (includes professional fees and benefits), supply costs (all other operating expenses divided by adjusted inpatient days, excluding depreciation and interest), total expenses (total labor and total supply costs), the number of staffed beds by service, occupancy percentage and outputs measured by inpatient days (per year) and outpatient visits (per year).
average
Means of all variables shown in Table 1 are computed for both osteopathic and allopathic hospitals, and are later compared for significant differences, using paired sample t-tests. The SPSS 11.5 Program is used to estimate the cost functions and to perform the descriptive analysis.
Results Table 2 reveals the descriptive statistics of cost function parameters for both osteopathic and matching 3 To test whether one regression model applies to both osteopathic and matching controls, we start with the null hypothesis that the regressions are identical. This is the time in which cost function parameters of osteopathic and allopathic hospitals are assumed to be identical in all aspects. In a onevariable case, this can be shown by C i ¼ a1 þ a2 Y i : The assumption of different intercept between osteopathic and allopathic hospitals is introduced by a dummy variable in the following cost function: C i ¼ a1 þ a2 Y i þ bDi where Di takes the value of ‘‘1’’ for osteopathic hospitals. When both the slope and the intercept are allowed to change, the model has still been expressed as a single equation as follows: C i ¼ a1 þ a2 Y i þ bDi þ mðDi Y i Þ:
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control hospitals. The average salary at an osteopathic hospital is significantly higher than that of a matching control by about $4,000. Likewise, average price of supplies is greater at an osteopathic hospital than an allopathic hospital, which is also a statistically significant difference at the 10% level. Major cost categories are significantly higher for osteopathic hospitals, but two factors somewhat justify the high costs: first, the case mix index of osteopathic hospitals is significantly higher than that of matching controls by about 0.05 (1.25 versus 1.20). Secondly, the area wage index is about 70 cents higher for osteopathic hospitals than their counterparts. Although these seem to be legitimate reasons for relatively higher costs, osteopathic hospitals generate significantly less inpatient days than their counterparts which draw attention to the lack of demand at osteopathic hospitals. Table 3 shows the estimated coefficients of cost function parameters for osteopathic and allopathic hospitals. The osteopathic cost function behaves much better than that of the matching controls. The adjusted R2 for osteopathic and matching control hospitals is 0.98 and 0.86, respectively. All first-order output elasticities for the osteopathic hospitals are positive and statistically significant whereas the only coefficient that is significant for allopathic hospitals is inpatient days. The estimated coefficient of outpatient visits is negative for osteopathic hospitals, but not statistically significant. The major cost predictor for allopathic hospitals is the average price of supplies with a coefficient of 0.60, which translates to one percent increase in the average price of supplies will increase total variable costs about 0.60 percent. Pooling the osteopathic and matching control samples, and estimating the same multiproduct cost function enables us to test for significant differences in cost function parameters. These estimates from the pooled sample are revealed in Table 3, and clearly, inpatient days, outpatient visits, case mix index, average salary and the price of supplies are significant factors in determining hospital costs. Interestingly, for-profit hospitals have slightly lower costs then non-profit hospitals when the pooled data is used. Only one coefficient, outpatient visits, is significantly different between osteopathic and allopathic hospitals. The interaction coefficient for outpatient services is 0.37 which means that the osteopathic hospitals costs rise by 0.37 percent more than allopathic hospitals when outpatient visits increase by one percent. There is no doubt that osteopathic hospitals have higher costs (both in average wages and price of supplies), but the results of multivariate regression analysis do not confirm this finding. There is approximately a $2 million difference in total costs between osteopathic and allopathic hospitals which could be attributed to the high cost of outpatient services according to the cost function estimates.
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Table 1 Input, output and cost variables of descriptive analysis The number of beds by service
The number of full-time employees
The number of part-time employees
Costs, outputs and efficiency indicators
Acute care 1. General acute care 2. Obstetric care 3. Pediatric care 4. Other acute care Intensive care 5. Medical/surgical care 6. Cardiac intensive care 7. Pediatric care 8. Neonatal intensive 9. Neonatal intermediate
1. Administrators 2. Physicians 3. Medical residents 4. Dentists 5. Dental residents 6. Registered nurses 7. Vocational nurses 8. Ancillary nurses 9. Physician assistants 10. Nurse practitioners 11. Medical record administrators 12. Medical record technicians 13. Pharmacists 14. Pharmacy technicians 15. Medical technologists 16. Other lab personnel 17. Dietitians 18. Dietetic technicians 19. Radiographers 20. Radiation therapy technicians 21. Nuclear medicine technicians 22. Other radiological staff 23. Occupational therapists 24. Occupational therapy assistants 25. Physical therapists 26. Physical therapy assistants 27. Recreational therapists 28. Speech pathologists 29. Audiologists 30. Respiratory therapists 31. Respiratory therapist technicians 32. Psychologists 33. Medical social workers 34. Other health professionals 35. All other personnel 36. Total hospital personnel
1. Administrators 2. Physicians 3. Medical residents 4. Dentists 5. Dental residents 6. Registered nurses 7. Vocational nurses 8. Ancillary nurses 9. Physician assistants 10. Nurse practitioners 11. Medical record administrators 12. Medical record technicians 13. Pharmacists 14. Pharmacy technicians 15. Medical technologists 16. Other lab personnel 17. Dietitians 18. Dietetic technicians 19. Radiographers 20. Radiation therapy technicians 21. Nuclear medicine technicians 22. Other radiological staff 23. Occupational therapists 24. Occupational therapy assistants 25. Physical therapists 26. Physical therapy assistants 27. Recreational therapists 28. Speech pathologists 29. Audiologists 30. Respiratory therapists 31. Respiratory therapist technicians 32. Psychologists 33. Medical social workers 34. Other health professionals 35. All other personnel 36. Total hospital personnel
1. Adjusted IPD 2. Total FTE 3. Acute care days 4. Intensive care days 5. Subacute care days 6. Outpatient visits 7. Bassinets 8. Births 9. Inpatient surgery 10. Outpatient surgery 11. Labor share in TC
10. Burn care 11. Other special care 12. Other intensive care Subacute care 13. Rehabilitation 14. Chronic disease 15. Hospice care 16. Skilled nursing 17. Psychiatric LT care 18. Alcohol dependency 19. Mental retardation 20. Total beds
Although the case mix index controls for more severe cases and perhaps, medical education costs in inpatient setting, there is no variable in the model controlling for the intensity of care in outpatient setting. The coefficient of outpatient visits may serve as a control for medical education costs taking place in the outpatient facility. In the second part of the study, a descriptive analysis is performed to explain differences between osteopathic and allopathic hospitals. As mentioned earlier, the total cost of an osteopathic hospital, on average, is about
12. 13. 14. 15. 16. 17. 18. 19.
Total cost Av. compensation Av. supply cost FTE/staffed beds IPD/FTEs Outp. visits/FTEs Occupancy rate % Full-time in FTE
$43.2 million annually whereas a local control hospital’s cost is approximately $41.3 million; not a statistically significant difference. This is especially important since an average osteopathic hospital produces less inpatient and adjusted patient days, and has a lower average daily census and occupancy rate than a local control hospital. All inpatient and outpatient surgeries, and visits are slightly lower for osteopathic hospitals. Although differences in total, labor and non-labor costs between osteopathic and allopathic hospitals are
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Table 2 Descriptive statistics of cost function parameters Osteopathic hospitals (n ¼ 50)
Control hospitals (n ¼ 50)
Paired t-statistic
Operating statistics Total beds (TOTBEDS) Average salary (WAGE) Average supply price (SUPPLIES) Inpatient days (TOTIPD) Surgical operations (SURG) Outpatient visits (TOTOUT) Total expenditures (TOTCOST)
120 $40,691 $466 23,952 3,689 76,362 $43,245,282
124 $36,768 $395 26,633 4,095 77,115 $41,294,448
.96 2.32** 1.75* 2.04** .91 .05 .50
Shift variables Case mix index (CASEMIX) Area wage index (AREAWAGE) System hospitals (SYSTEM) Contract managed hospitals (CONTRACT) For-profit hospitals (PROFIT)
1.25 $19.00 52% 14% 16%
1.20 $18.30 52% 10% 8%
2.21** 1.82* + .63 1.15
*Significant at the 10% level. **Significant at the 5% level. ***Significant at the 1% level. +Perfect match so standard error is zero.
Table 3 Estimated cost function parameters Variable
Osteopathic hospitals coefficients
Matching controls coefficients
Pooled sample coefficients
CONSTANT BEDS WAGE SUPPLIES TOTIPD TOTSURG TOTOUT PROFIT SYSTEM CONRACT CASEMIX AREAWAGE DUMMY DUMMY*BEDS DUMMY*WAGE DUMMY*SUPPLIES DUMMY*TOTIPD DUMMY*TOTSURG DUMMY*TOTOUT DUMMY*PROFIT DUMMY*SYSTEM DUMMY*CONTRACT DUMMY*CASEMIX DUMMY*AREAWAGE
4.38*** 0.09 0.11 0.39*** 0.47*** 0.16*** 0.25*** 0.07 0.08 0.11 0.14 0.01
0.35 0.33 0.50 0.60*** 0.41* 0.11 0.12 0.45* 0.05 0.19 0.84 0.02
0.35 0.33 0.50* 0.60*** 0.41** 0.11 0.12** 0.45** 0.05 0.19 0.84* 0.02 4.72 0.25 0.39 0.21 0.06 0.05 0.37*** 0.38 0.12 0.30 0.70 0.01
*Significant at the 10% level. **Significant at the 5% level. ***Significant at the 1% level.
just a random effect based on cost function estimates, efficiency indicators such as labor and supply costs per inpatient day are statistically different between these two groups of hospitals. For example, the average cost of an inpatient day at an osteopathic hospital is about $975
whereas that of the allopathic hospital is $831; a significant difference at the 5% level. The average cost of medical supplies at osteopathic and local control hospitals is $466 and $395, respectively; also a statistically significant difference. An average salary at an
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osteopathic facility is $40,691 in comparison to $36,768 at an allopathic facility. The average hourly wage index obtained from the Federal Register is 70 cents higher for osteopathic hospitals than that of allopathic hospitals which explains approximately $1,500 of the salary difference. Another possible reason for the salary difference may be related to the relatively smaller number of workers employed at osteopathic hospitals (499 FTE versus 539 FTE). Examining efficiency measures in the bottom of Table 4 reveals interesting results for the osteopathic hospitals. All average daily census, adjusted daily census and occupancy rate are lower than their counterparts which clearly suggest that these hospitals are low volume hospitals with empty beds. The lack of demand combined with higher costs at osteopathic hospitals causes almost all efficiency indicators to favor allopathic hospitals. The average cost per patient day is computed twice: AVERAGE1-defined as total cost divided by total inpatient days and AVERAGE2-defined as total cost divided by adjusted patient days. The average cost of an inpatient day becomes significantly higher for osteopathic hospitals when it is computed as total costs divided by adjusted patient days (AVERAGE2) rather than inpatient days. This implies that there may be additional efficiency problems in osteopathic hospitals associated with outpatient services and surgeries, elevat-
ing the level of financial difficulties. This finding is also confirmed by the cost function estimates. The proportion of full-time hospital employees in FTE is about 85 percent for osteopathic hospitals and 82 percent at local control hospitals which is a statistically significant difference. The staffing ratio FTE per bed is lower for osteopathic hospitals than their local controls which suggests that there is a relatively smaller number of employees at osteopathic hospitals, but they are proportionately more full time. To determine the sources of cost inefficiencies among osteopathic hospitals, labor and capital inputs are closely examined. First, total full time and part time hospital personnel, grouped into 36 categories (see Table 1) were compared. Interestingly, only five significant variables emerged from this analysis: (1) fulltime physicians and dentists, (2) full-time medical and dental residents and interns, (3) full-time equivalent physicians and dentists, (4) full-time equivalent medical and dental resident and interns and (5) full-time equivalent total medical trainees. Osteopathic hospitals employ more than twice as many physicians and dentists as their counterparts (9.76 versus 4.42), and provide training to a considerably large number of residents and interns (24.56 versus 1.42) which may have been the main cause of higher hospital costs. Likewise, the FTE of physicians and dentists, and
Table 4 Significant differences in descriptive statistics between osteopathic and allopathic hospitals Osteopathic hospitals (n ¼ 50)
Control hospitals (n ¼ 50)
Variables
Mean
Standard deviation
Mean
Standard deviation
t-stats
Output variables Total facility inpatient days (IPDTOT) Adjusted patient days (ADJPD)
23,952 41,588
19,982 32,604
26,634 47,632
19,579 33,345
2.04* 2.34*
9.76 24.56
15.24 35.49
4.42 1.42
8.38 6.37
2.41* 4.85**
10.84
15.53
6.62
12.39
2.01*
Input variables Full time physicians and dentists (FTMDTF) Full time medical and dental residents and interns (FTRES) Full time equivalent physicians and dentists (FTEMD) Full time equivalent medical and dental residents and interns (FTERES) Full time equivalent total trainees (FTETTRN)
24.64
35.60
1.82
6.83
4.72**
25.40
36.519
2.00
6.908
4.74**
Efficiency indicators Average daily census (ADC) Adjusted average daily census (ADJADC) Occupancy rate Average cost per adjusted PD (AVERAGE2) Average salary per FTE % Full-time employers in FTE Case mix index (n ¼ 46)
65.64 114.00 .49 975 40,691 .85 1.207
54.737 89.399 .18 489 12,364 .09 .21
73.02 130.52 .56 831 36,768 .82 1.268
53.681 91.355 .18 54 9,337 .11 .17
*Significant at the 5 percent level. **Significant at the 1 percent level.
2.05* 2.34* 2.60* 2.17* 2.32* 2.32* 2.22*
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medical and dental residents and interns is greater at osteopathic hospitals than their counterparts, which factors in few part time employees into the statistic. These differences in employment measures are significant at the 1% level. A large number of physicians, dentists, residents and interns do translate to a higher case mix index at the osteopathic hospitals. The average case mix index is 1.25 for osteopathic hospitals and 1.20 for allopathic hospitals. To investigate capital inputs of osteopathic and allopathic hospitals, we used 20 variables, mostly bed size by service, and surprisingly we found no statistically significant differences in means (see Table 1, column 1). An average osteopathic hospital has 120 beds whereas a local control hospital possesses 124 beds; not a significant difference. The number of bassinets, births, outpatient visits and surgeries is not significantly different between these hospitals. The number of general acute, intensive and subacute care beds and their subcategories are very similar in osteopathic and allopathic hospitals. Although these statistics are not reported here, they are available upon request.
Discussion The study investigated the cost structure of osteopathic hospitals and their local counterparts, and found substantially higher costs at osteopathic hospitals. Capital and labor inputs, and hospital costs were examined, and two major reasons emerged for relatively high costs of osteopathic hospitals: (1) a large number of full time physicians and dentists, and also full-time residents and interns employed, and (2) the relatively high cost of outpatient services. On average, an osteopathic hospital approximately spent $2 million more than an allopathic hospital ($43.2 million versus $41.2 million). This suggests that the higher cost of osteopathic hospitals is associated with medical education, not related to capital costs. Past research shows that there are significant costs associated with medical education at teaching hospitals (Thorpe, 1988; Campbell, Gillespie, & Romeis, 1991). Perhaps, financially supporting osteopathic hospitals by medical school tuition revenues may assure the survival of osteopathic hospitals in the long run. Cost function estimates revealed interesting results for osteopathic and control hospitals. All output elasticities for osteopathic hospitals are statistically significant whereas only the coefficient of inpatient days for allopathic hospitals is found to be critical, which is the industry-wide trend. The price of supplies appears to significantly contribute to the total costs in both types of hospitals. Outpatient visits are produced more costly by osteopathic hospitals and this is the only statistically significant difference in the estimated cost function parameters of
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osteopathic and allopathic hospitals. At the margin, a onepercent increase in outpatient visits may not impact the hospital costs at allopathic hospitals, but increases osteopathic hospital costs by 0.25 percent. Overall, osteopathic hospitals were found to be high cost facilities. This study rather focused on the high cost of osteopathic hospitals and possible strategies to preserve these facilities in the long run. The literature that investigated hospital mergers and closures in the past revealed that the cost structure of osteopathic hospitals, and merged or closed hospitals is similar. A recent study showed that merged hospitals had high costs and low patient volume prior to merging in comparison to their local counterparts (Sinay & Campbell, 2002). Osteopathic hospitals must emphasize improvements in cost efficiencies which may eventually lead to closing the gap between themselves and their local counterparts for survival. Otherwise, this vulnerability could result in a closure or a merger that is likely to force them to lose their osteopathic identity. According to Modern Healthcare (Bellandi, 2000) hospital administrators are often reluctant to implement cuts in staffing, eliminate beds, or reduce duplicated services. This reluctance stems from concerns that such reductions might jeopardize their market share, creates ill will in the community, or make them unattractive to doctors, patients, or managed care plans. Nonetheless, these operational changes are important methods for reducing costs when confronted with payment reductions and greater competition between hospitals in local markets. Osteopathic hospitals are faced with the same dilemma like the hospitals of late 1980s and 1990s: restructuring, eliminating clinical services and downsizing facilities or staff to improve the organization’s financial position. The use of a small sample and a matching case-control design rather than randomly selecting several control hospitals in the local market or nationally may limit the study findings. This approach puts much more weight on the performance of a single matched control observation rather than the average of several observations. Also matching does not guarantee that the matched pairs are homogeneous in other non-measured characteristics that might influence the outcome. Statistics on several other controls—occupancy rate and case mix index are not included in the initial matching process, which may have provided additional information about osteopathic hospitals and their counterparts; however, this was not the intent of the study.
Acknowledgements I am indebted to Todd Linden, President and CEO, Grinnell Regional Medical Center, Grinnell, IA for his interest and support of the project.
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I would like to thank Dr. Simon Gelletta for his computer program to match osteopathic and allopathic hospitals, and my research assistant Kathryn Borman for her efforts to prepare the data for analysis. I also would like to thank two anonymous referees for their valuable comments.
References Alexander, J., & Morrisey, M. (1987). Hospital participation in multi product systems. Advances in Health Economics and Health Services Research, 7, 157–178. Andersson, G. B., Lucente, T., Davis, A. M., Kappler, R. E., Lipton, J. A., & Leurgans, S. (1999). A comparison of osteopathic spinal manipulation with standard care for patients with low back pain. The New England Journal of Medicine, 341, 1426–1431. Bass, F. (1997). Texas led US in hospitals shutting down. Wall Street Journal, New York, NY, Jan. 15, 1. Becker, E. R., & Sloan, F. (1985). Hospital ownership and performance. Economic Inquiry, 23(1), 21–36. Baumol, W. J., Panzar, J. C., & Willig, R. D. (1982). Contestable markets and the theory of industry structure. New York, NY: Harcourt Brace Jovanovich. Bellendi, D. ‘‘More Hospitals Close’’. Modern Healthcare, (August 7, 2000): p. 22. Campbell, C. R., Gillespie, K. N., & Romeis, J. C. (1991). The effects of residency programs on the financial performance of veterans affairs medical centers. Inquiry, 28(3), 288–299. Conrad, R., & Strauss, R. (1983). A multiple output multiple input model for the hospital industry in North Carolina. Applied Economics, 341–352. Cowing, T., & Holtmann, A. (1983). Multiproduct short-run hospital cost functions: empirical evidence and policy implications from cross-section data. Southern Economic Journal, 49(3), 637–653. Ermann, D., & Gabel, J. (1985). The changing face of American health care: multihospital systems, emergency centres and surgery centres. Medical Care, 23, 401–420. Eskoz, R., & Peddecord, K. M. (1985). The relationship of hospital ownership and service composition to hospital charges. Health Care Financing Review, 6(3), 51–58. Fournier, G., & Mitchell, J. (1992). Hospital costs and competition for services: a multiproduct analysis. The Review of Economics and Statistics, 74(4), 627–634. Glover, S. H., & Rivers, P. A. (2000). Strategic choices for a primary care advantage: re-engineering osteopathic medicine for the 21st century. Health Service Management Research, (13), 156–163. Grannemann, T., Brown, R., & Pauly, M. (1986). Estimating hospital costs: a multiple output analysis. Journal of Health Economics, 5(2), 107–127.
Howell, J. D. (1999). The paradox of osteopathy. The New England Journal of Medicine, (341), 1465–1468. Sinay, T. (1998). Hospital mergers and closures: survival of rural hospitals. The Journal of Rural Health, 14, 357–365. Levitz, G., & Brooke, P. (1985). Independent versus system affiliated hospitals: a comparative analysis of financial performance, cost and productivity. Health Services Research, 20, 315–339. Lister, E. (1983). Osteopathy medicine, history and concepts. Journal of American Osteopathic Association, (83), 221–230. Lynch, J., & Ozcan, Y. (1994). Hospital closure: an efficiency analysis. Hospital and Health Services Administration, 39, 205–220. Moody’s Reports: US Not for-Profit hospitals face negative outlook; 2000 medians show continued deterioration. http:// www.moody.com/repldata/ratings/actions/pr.39113.html; 8/21/2000. Northup, G. W. (1975). History of the development of osteopathic concepts and osteopathic terminology. In: Golstein, M. (Ed.), The research status of spiral manipulative therapy. Bethesda, pp. 1101–1143. Pindyck, R. S., & Rubinfeld, D. L. (1991). Econometric models and economic forecasts (3rd ed.). New York, NY: McGraw-Hill. Shortell, S. (1988). The evolution of hospital systems: unfulfilled promises and self-fulfilling prophesies. Medical Care Review, 45, 177–214. Sinay, T. (1998a). Hospital mergers and closures: survival of rural hospitals. The Journal of Rural Health, 14(4), 357–365. Sinay, T. (1998b). Pre- and post-merger investigation of hospital mergers. Eastern Economic Journal, 24(1), 83–97. Sinay, T., & Campbell, C. R. (2002). Strategies for more efficient performance through hospital merger. Health Care Management Review, 27(1), 33–49. The American Osteopathic Association (2001). Healthcare facilities accreditation program 2000–2001. Chicago, IL: AOA Publications. The American Osteopathic Association (1999). 1999 Yearbook and directory of osteopathic physicians. Chicago, IL: AOA Publications. The Joint Commission on Accreditation of Healthcare Organizations (2001). 2001 Hospital accreditation standards. Oakbrook Terrace, IL: The Commission. Thorpe, K. E. (1988). Why are urban hospital costs so high? The relative importance of patient source of admission, competition and case mix. Health Services Research, 22(6), 821–836. Vita, M. G. (1990). Exploring hospital production relationships with flexible functional forms. Journal of Health Economics, 9(1), 1–21. Watt, J. M., Derzon, R. A., Renn, S. C., Schramm, C. J., Hahn, J. S., & Pillari, G. D. (1986). The comparative economic performance of investors-owned chain and not-for-profit hospitals. The New England Journal of Medicine, 314(2), 89–96.
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Cost structure of osteopathic hospitals and their local counterparts in the USA: Are they any different? Tony Sinay Health Care Administration, College of Health and Human Services, California State University, Long Beach 1250 Bellflower Boulevard, Long Beach, CA 90840-4902, USA Available online 11 November 2004
Abstract Due to the emphasis on preventive care and less invasive solutions to medical problems, osteopathic hospitals may deliver cost efficient and cost effective care. This study examines the cost structure of osteopathic hospitals and compares their performance to a local control group selected from allopathic hospitals. Osteopathic hospitals are identified in the 1999 American Hospital Association (AHA) data and matched to local allopathic hospitals with respect to location, bed size, system, for-profit and teaching status. Cost functions are estimated for both groups of hospitals, and significant differences in input, output and costs are highlighted. Results show that osteopathic hospitals are more costly and less productive in comparison to their counterparts. Inefficient production of outpatient services and high cost of medical education are two reasons for the poor performance. The study has important policy implications on two fronts: first, osteopathic hospitals are more costly to operate than their counterparts, and subsequently this requires further analysis of the osteopathic treatments and techniques. In an environment where health care revenues are shrinking and costs are rising, this is probably much needed information for osteopathic hospitals. Secondly, there is an emerging concern among osteopathic medical schools and osteopathic physicians due to the declining number of osteopathic hospitals, which translates to a smaller number of residency positions for osteopathic medical school graduates. Analyzing cost, input and output variables reveal some of the contributing factors to the decline of osteopathic hospitals and help preserve this rich tradition. r 2004 Elsevier Ltd. All rights reserved. Keywords: Osteopathic hospitals; Cost functions; Operating efficiencies; Labor and capital costs
Introduction The osteopathic medicine emphasizes the concept of ‘‘wellness and preventive medicine,’’ and focuses on each person’s health risks such as smoking, high blood pressure, excessive cholesterol levels, stress and other lifestyle factors. Due to the emphasis on preventive care and less invasive solutions to medical problems, osteopathic physicians and hospitals may deliver a cost Tel.: +1 562 985 5694; fax: +1 562 985 5536.
E-mail address: [email protected] (T. Sinay).
efficient care. Andersson et al. (1999) showed that osteopathic manipulative care and standard medical care have similar clinical results in patients with subacute low back pain; however, the use of medication and physical therapy was greater with the standard care and differences in costs were significant. The focus of this paper is a group of facilities that distinguish themselves as osteopathic hospitals. Although the classification is provided in the American Hospital Association (AHA) Annual Survey of US Hospitals, research that compares these providers to other type of hospitals is rather limited or non-existent.
0277-9536/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2004.08.042
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There is anecdotal evidence that osteopathic hospitals located in 31 states with more than 25,000 beds; about 825,000 patient admissions; 6.3 million patient days; 3.1 million outpatient visits to hospital’s outpatient departments each year for emergency and other ambulatory care, published in an American Osteopathic Association (AOA) brochure. The AOA Directory serves as a more reliable source for these facilities (The American Osteopathic Association, 1999). In 1999, there were about 129 facilities listed as osteopathic hospitals. In the 1990s, a number of osteopathic hospitals disappeared through mergers and closures, losing their unique identity. From 1989 to 1999, 44 osteopathic hospitals were eliminated from the AOA Directory, about 25 percent decrease. The slowing of the growth in reimbursement in the late 1980s forced many financially threatened hospitals to close. From 1986 to 1995, 533 hospitals went out of operation (Bass, 1997). Many of these were small, rural hospitals. Surprisingly, many closed rural hospitals were more efficient at the time of closure than non-closing hospitals. Weak rural economies and lack of demand for hospital services were found to be contributing factors for rural closures (Sinay, 1998a; Lynch & Ozcan, 1994). Most osteopathic hospitals are located in rural areas. Sixteen of 50 osteopathic hospitals included in this study were located in non-metropolitan areas, approximately 32 percent. Ten other osteopathic hospitals in this study came from small metropolitan areas where the population was between 100,000 and 500,000 (AHA Data Files, 1999; Annual Survey of US Hospitals, 1999). This study examines the cost, input, output and efficiency indicators of osteopathic hospitals, and compares their performance to a local control group selected from allopathic hospitals. Osteopathic hospitals are identified in the 1999 AHA data, and later matched to local allopathic hospitals with respect to location, bed size, system, for-profit and teaching status. Cost functions are estimated for both osteopathic and matching control hospitals, and significant differences in cost function parameters are identified. Using paired sample t-test, additional analysis was done to explain differences in price and output efficiencies. The study may have important policy implications on two fronts: first, today’s hospital managers have to identify the best strategies for dealing with the financial weakening of the industry and the environmental challenges they are likely to continue to face over the next decade. Osteopathic hospitals may represent an effective philosophy to medicine and the hospital management in particular. Operational efficiencies of osteopathic hospitals in comparison to their counterparts may trigger further investigation of the osteopathic treatments and their management practices.
Secondly, there is an emerging concern among osteopathic medical schools and osteopathic physicians that the number of osteopathic hospitals is declining which eliminates already limited residency positions for the graduates of osteopathic medical schools. Identifying the strengths and weaknesses of osteopathic hospitals may provide survival tips to the managers of osteopathic hospitals since prospects for hospital profitability continue to look poor, and the challenges facing the industry are not temporary but an ongoing phenomenon according to Moody’s Investors Service (2000). Besides the use of a matched case-control design where omitted local market and environmental effects are likely to be the same for each osteopathic and allopathic hospital, this study departs from the previous investigations of hospital efficiency studies in several ways. First, it estimates a multiproduct cost function to measure relative efficiency of osteopathic and allopathic hospitals. Using a dummy-variable approach, significant differences in cost function parameters were tested. In the descriptive part of the study, hospital labor inputs by each job category, which provides insights into labor costs, and capital costs by bed size by hospital service were analyzed. Lastly, the study developed several efficiency indicators to accurately measure and compare operating efficiencies of osteopathic and allopathic hospitals. The primary research questions addressed in the study are:
What differences in output and input price elasticities exist between osteopathic and allopathic hospitals?
What differences in labor input are observed between osteopathic and allopathic hospitals? differences in capital input are observed between allopathic and osteopathic hospitals? Do differences in labor and capital inputs affect costs of osteopathic and allopathic hospitals?
What
Osteopathic philosophy and research foundations The science of osteopathy dates back to 1874 when Dr. Andrew Taylor Still first laid out the foundations of osteopathic medicine. Dr. Still pioneered the concept of ‘‘wellness’’ about 100 years ago, and pursued holistic and patient-centered medicine with a focus on preventive care medicine. He studied each person’s health risks such as smoking, high blood pressure, excessive cholesterol levels, stress and other lifestyle factors (Lister, 1983). Osteopathic medicine is a primary-care centered approach to medical, surgical, obstetrical and other health services founded on a philosophy embracing the importance of hands-on evaluation and treatment of the total person and dedicated to improve health care for all people. Osteopathic theory focuses on the whole body
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which comprises five major points (Northup, 1975; Glover & Rivers, 2000).
unity of the body, the healing power of nature, somatic components of disease, structure–function concept and manipulative theory. The body functions as a unit with existing interrelationships among all body systems and possesses selfregulatory mechanisms which have an inherent capacity to health and repair itself. The body’s equilibrium preserves health and protects against disease. Osteopathic philosophy points out that structure and functions of body are not separable in understanding human physiology, and the musculoskeletal system is similar to a machine, serving a common function. All parts must be operable, anatomically and physiologically, for body to be healthy to fully function. The use of osteopathic manipulation where the bones, muscles, tendons and connective tissue can be manipulated to improve the range of motion and promote blood flow through tissues to enhance the body’s own healing powers become the blueprints of osteopathic medicine (Howell, 1999). Due to the holistic philosophy described above, osteopathic hospitals may have better operating efficiencies over others that result in cost effective care.
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Hospital Association (AHA) Annual Survey of U.S. hospitals suggests otherwise. Osteopathic hospitals are accredited by the AOA-Healthcare Facilities Accreditation Program whereas allopathic hospitals are accredited by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO). The requirements for accreditation by the AOA and the Joint Commission are similar. Both require licensed medical staff, which includes nurses and physicians; patient care standards regarding treatment and privacy; organizational structure involving leaders of each department with a hospital CEO, and emergency plans for natural disasters or utility failures. Although the educational background of the medical staff varies, both the AOA and the Joint Commission emphasize continuing education. Additionally, the AOA requires an osteopathic physician (s) on staff, performing an osteopathic musculoskeletal examination at osteopathic hospitals, and a committee called ‘‘Utilization of Osteopathic Methods and Concepts Committee’’ to promote effective methods for osteopathic diagnosis and treatments. This committee is expected to hold meetings quarterly (The American Osteopathic Association, 2001; The Joint Commission on Accreditation of Healthcare Organizations, 2001).
Methods Data and matching process
Literature review Many of the past efficiency studies relevant to this study compared multi-hospital system hospitals with independent hospitals on measures of hospital specific performance using descriptive statistics and multivariate regression analysis (Alexander & Morrisey, 1987; Ermann & Gabel, 1985; Levitz & Brooke, 1985; Shortell, 1988; Becker & Sloan, 1985). Economic performance of investor-owned chain and not-for-profit hospitals were also compared in several studies. Watt et al. (1986) concluded that investor-owned chain hospitals generated higher profits through more aggressive pricing practices rather than operating efficiencies. In a different study, proprietary hospitals were found to have the highest profit margins for ancillary services and not-forprofit hospitals had the highest profit margins for daily hospital services (Eskoz & Peddecord, 1985). A recent study compared pre- and post-merger efficiency of merging hospitals to a local control group in operating efficiencies, and concluded that merging hospitals had much higher labor costs in the pre-merger period than their counterparts, and similar costs after the merger (Sinay & Campbell, 2002). The literature does not distinguish osteopathic hospitals as a separate group of hospitals, but the American
First, osteopathic hospitals are identified from the 1999 AHA data and are matched to allopathic hospitals with respect to the following criteria:
have approximately the same number of staffed beds, be in the same metropolitan statistical area (MSA), have the same type of ownership, for-profit or not
for-profit (however, this assumption is later relaxed to find matching hospitals in the same MSA), have the same system affiliation status as the osteopathic hospital and have the same teaching status.
The data sample included 50 osteopathic hospitals along with 50 matching controls which are members of the AHA. Case mix index was obtained from the Federal Register. Two osteopathic and two allopathic hospitals did not report their case mix index in 1999. The 1998 case mix index table was tried to obtain the missing information but there was no luck with this attempt. Instead, we used the matching hospitals case mix index as a proxy. Area wage index is also reported in the Federal Register. One osteopathic and one allopathic hospital fail to report this information. Matching area wage index is also used to keep these hospitals in the data analysis due to the relatively small sample size.
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To control for market effects, most studies used several, randomly selected hospitals as controls for ongoing operational changes emanating from the environment. In studies where controls were selected from representative hospitals nationwide, additional variables to account for local environmental influences that pertain to each control hospital were added to the model. Because of the limitations imposed by a small sample size in this study, an alternative strategy was adopted. We started the matching process by classifying hospitals into different bed-size categories. For this purpose the AHA bed-size classification system was used which included eight size classes. The largest osteopathic hospital had 325 beds and the smallest had 12 beds. Since the largest osteopathic hospital had 325 beds, only six of the eight bed-size classes were relevant for the study (Sizes 7 and 8 are hospitals with more than 400 beds). We started out with 54 osteopathic hospitals and 6602 allopathic hospitals nationwide. One osteopathic hospital was automatically dropped from the sample due to being a specialty hospital. About 1350 specialty and federal government hospitals were discarded from the national sample. A total of 4702 allopathic hospitals were potential matches to 53 osteopathic hospitals on the basis of bed-size. A computer program was utilized to obtain perfect matches for osteopathic hospitals, which included five control factors: bed size, MSA, ownership, system and teaching status. A total of 4465 non-osteopathic hospitals were dropped from the sample for random matching. There were 18 osteopathic facilities for which a local matching hospital could not be found in the same MSA. For those facilities matching allopathic hospitals were sought by expanding their MSA to the Consolidated Metropolitan Statistical Areas (CMSA). Matches were found for 15 of the 18 osteopathic facilities within the same CMSAs. The other three facilities for which a match was not found within the same CMSA were discarded from the analysis. The final one-to-one matching was performed by randomly selecting 50 matches from a list of 237 non-osteopathic candidates. Since there was only one non-osteopathic within the 300–399 bed-size group, the non-osteopathic match for this bed-size class was automatic without random selection. Once the selection process was completed, three control hospitals were found to be selected twice by the computer program. This was handled by replacing those three control hospitals with new controls. Cost function parameters Cost functions provide theoretically grounded models for measuring efficiency (Vita, 1990; Cowing & Holtmann, 1983; Fournier & Mitchell, 1992). Multiproduct cost functions have been introduced by Grannemann,
Brown, and Pauly (1986) and Conrad and Strauss (1983), and applied to hospitals by Fournier and Mitchell (1992), Vita (1990), and Sinay (1998b). These cost functions vary from structural models to behavioral models, and the combinations of the two, hybrid translog cost functions, which allow researchers to compute ray economies of scale and scope. Baumol, Panzar, and Willig (1982) defined the theoretical foundations of multiproduct cost functions, and described scope and scale economies for economic efficiencies. The above studies served as reference for the selection of the multiproduct cost function and its variables. The following cost function is estimated for both osteopathic and matching control hospitals: Ln TOTCOST ¼ a0 þ a1 Ln TOTIPD þ a2 Ln SURG þ a3 Ln TOTOUT þ b1 Ln WAGE þ b2 Ln SUPPLIES þ Ok Ln BEDS þ y1 CASEMIX þ y2 SYSTEM þ y3 PROFIT þ y4 CONTRACT þ y5 AREAWAGE þ ; where TOTCOST is the total variable cost,1 TOTIPD is the inpatient days, TOTSURG is total inpatient and outpatient surgeries, TOTOUT is the outpatient visits; average salary is controlled by WAGE and the average price of supplies is controlled by SUPPLIES;2 BEDS is the number of staffed beds, representing the fixed capital; PROFIT and SYSTEM are dummy variables for proprietary and system affiliation status, respectively; CONTRACT is another dummy variable for hospitals under contractual arrangements; CASEMIX measures the average severity of patients at each facility, and AREAWAGE is an index to control for labor market conditions and environmental factors. Methodology The study consists of two parts. First, separate cost functions for osteopathic and allopathic hospitals are estimated. In order to test differences in estimated coefficients between these two functions, data sets of osteopathic and matching control hospitals are pooled, and an intercept dummy (DUMMY=1 for osteopathic hospitals) and a slope dummy for each estimated coefficient (DUMMY*TOTIPD, DUMMY*TOTSURG, DUMMY*TOTOUT, DUMMY*BED1 Cv is the total variable cost which includes labor and supply costs. Labor costs include total payroll expenses, employee benefits, and professional fees. 2 The average salary (WAGE) is computed by dividing total labor costs by the FTE. Average price of supplies (SUPPLIES) is obtained by dividing total supply costs by adjusted inpatient days.
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Syyyyy) are introduced (Pindyck & Rubinfeld, 1991).3 This new pooled cost function is used to test for significant differences in two cost functions estimated earlier. Theoretically, cost function parameters from the pooled model are linear combinations of those estimated in two separate functions. In the second part of the study, a descriptive analysis is performed to explain findings from the cost function estimates. The study compares hospital labor and capital inputs, and develops several efficiency indicators to descriptively measure and compare operating efficiencies of osteopathic and allopathic hospitals. Data on inputs, outputs and costs for each osteopathic and control hospitals are obtained which included:
full time equivalent employees (FTEs), the number of full time hospital personnel, grouped into certain categories (see Table 1),
the number of part time hospital personnel, grouped into certain categories (see Table 1),
FTEs per bed, average compensation per FTE (includes professional fees and benefits), supply costs (all other operating expenses divided by adjusted inpatient days, excluding depreciation and interest), total expenses (total labor and total supply costs), the number of staffed beds by service, occupancy percentage and outputs measured by inpatient days (per year) and outpatient visits (per year).
average
Means of all variables shown in Table 1 are computed for both osteopathic and allopathic hospitals, and are later compared for significant differences, using paired sample t-tests. The SPSS 11.5 Program is used to estimate the cost functions and to perform the descriptive analysis.
Results Table 2 reveals the descriptive statistics of cost function parameters for both osteopathic and matching 3 To test whether one regression model applies to both osteopathic and matching controls, we start with the null hypothesis that the regressions are identical. This is the time in which cost function parameters of osteopathic and allopathic hospitals are assumed to be identical in all aspects. In a onevariable case, this can be shown by C i ¼ a1 þ a2 Y i : The assumption of different intercept between osteopathic and allopathic hospitals is introduced by a dummy variable in the following cost function: C i ¼ a1 þ a2 Y i þ bDi where Di takes the value of ‘‘1’’ for osteopathic hospitals. When both the slope and the intercept are allowed to change, the model has still been expressed as a single equation as follows: C i ¼ a1 þ a2 Y i þ bDi þ mðDi Y i Þ:
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control hospitals. The average salary at an osteopathic hospital is significantly higher than that of a matching control by about $4,000. Likewise, average price of supplies is greater at an osteopathic hospital than an allopathic hospital, which is also a statistically significant difference at the 10% level. Major cost categories are significantly higher for osteopathic hospitals, but two factors somewhat justify the high costs: first, the case mix index of osteopathic hospitals is significantly higher than that of matching controls by about 0.05 (1.25 versus 1.20). Secondly, the area wage index is about 70 cents higher for osteopathic hospitals than their counterparts. Although these seem to be legitimate reasons for relatively higher costs, osteopathic hospitals generate significantly less inpatient days than their counterparts which draw attention to the lack of demand at osteopathic hospitals. Table 3 shows the estimated coefficients of cost function parameters for osteopathic and allopathic hospitals. The osteopathic cost function behaves much better than that of the matching controls. The adjusted R2 for osteopathic and matching control hospitals is 0.98 and 0.86, respectively. All first-order output elasticities for the osteopathic hospitals are positive and statistically significant whereas the only coefficient that is significant for allopathic hospitals is inpatient days. The estimated coefficient of outpatient visits is negative for osteopathic hospitals, but not statistically significant. The major cost predictor for allopathic hospitals is the average price of supplies with a coefficient of 0.60, which translates to one percent increase in the average price of supplies will increase total variable costs about 0.60 percent. Pooling the osteopathic and matching control samples, and estimating the same multiproduct cost function enables us to test for significant differences in cost function parameters. These estimates from the pooled sample are revealed in Table 3, and clearly, inpatient days, outpatient visits, case mix index, average salary and the price of supplies are significant factors in determining hospital costs. Interestingly, for-profit hospitals have slightly lower costs then non-profit hospitals when the pooled data is used. Only one coefficient, outpatient visits, is significantly different between osteopathic and allopathic hospitals. The interaction coefficient for outpatient services is 0.37 which means that the osteopathic hospitals costs rise by 0.37 percent more than allopathic hospitals when outpatient visits increase by one percent. There is no doubt that osteopathic hospitals have higher costs (both in average wages and price of supplies), but the results of multivariate regression analysis do not confirm this finding. There is approximately a $2 million difference in total costs between osteopathic and allopathic hospitals which could be attributed to the high cost of outpatient services according to the cost function estimates.
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Table 1 Input, output and cost variables of descriptive analysis The number of beds by service
The number of full-time employees
The number of part-time employees
Costs, outputs and efficiency indicators
Acute care 1. General acute care 2. Obstetric care 3. Pediatric care 4. Other acute care Intensive care 5. Medical/surgical care 6. Cardiac intensive care 7. Pediatric care 8. Neonatal intensive 9. Neonatal intermediate
1. Administrators 2. Physicians 3. Medical residents 4. Dentists 5. Dental residents 6. Registered nurses 7. Vocational nurses 8. Ancillary nurses 9. Physician assistants 10. Nurse practitioners 11. Medical record administrators 12. Medical record technicians 13. Pharmacists 14. Pharmacy technicians 15. Medical technologists 16. Other lab personnel 17. Dietitians 18. Dietetic technicians 19. Radiographers 20. Radiation therapy technicians 21. Nuclear medicine technicians 22. Other radiological staff 23. Occupational therapists 24. Occupational therapy assistants 25. Physical therapists 26. Physical therapy assistants 27. Recreational therapists 28. Speech pathologists 29. Audiologists 30. Respiratory therapists 31. Respiratory therapist technicians 32. Psychologists 33. Medical social workers 34. Other health professionals 35. All other personnel 36. Total hospital personnel
1. Administrators 2. Physicians 3. Medical residents 4. Dentists 5. Dental residents 6. Registered nurses 7. Vocational nurses 8. Ancillary nurses 9. Physician assistants 10. Nurse practitioners 11. Medical record administrators 12. Medical record technicians 13. Pharmacists 14. Pharmacy technicians 15. Medical technologists 16. Other lab personnel 17. Dietitians 18. Dietetic technicians 19. Radiographers 20. Radiation therapy technicians 21. Nuclear medicine technicians 22. Other radiological staff 23. Occupational therapists 24. Occupational therapy assistants 25. Physical therapists 26. Physical therapy assistants 27. Recreational therapists 28. Speech pathologists 29. Audiologists 30. Respiratory therapists 31. Respiratory therapist technicians 32. Psychologists 33. Medical social workers 34. Other health professionals 35. All other personnel 36. Total hospital personnel
1. Adjusted IPD 2. Total FTE 3. Acute care days 4. Intensive care days 5. Subacute care days 6. Outpatient visits 7. Bassinets 8. Births 9. Inpatient surgery 10. Outpatient surgery 11. Labor share in TC
10. Burn care 11. Other special care 12. Other intensive care Subacute care 13. Rehabilitation 14. Chronic disease 15. Hospice care 16. Skilled nursing 17. Psychiatric LT care 18. Alcohol dependency 19. Mental retardation 20. Total beds
Although the case mix index controls for more severe cases and perhaps, medical education costs in inpatient setting, there is no variable in the model controlling for the intensity of care in outpatient setting. The coefficient of outpatient visits may serve as a control for medical education costs taking place in the outpatient facility. In the second part of the study, a descriptive analysis is performed to explain differences between osteopathic and allopathic hospitals. As mentioned earlier, the total cost of an osteopathic hospital, on average, is about
12. 13. 14. 15. 16. 17. 18. 19.
Total cost Av. compensation Av. supply cost FTE/staffed beds IPD/FTEs Outp. visits/FTEs Occupancy rate % Full-time in FTE
$43.2 million annually whereas a local control hospital’s cost is approximately $41.3 million; not a statistically significant difference. This is especially important since an average osteopathic hospital produces less inpatient and adjusted patient days, and has a lower average daily census and occupancy rate than a local control hospital. All inpatient and outpatient surgeries, and visits are slightly lower for osteopathic hospitals. Although differences in total, labor and non-labor costs between osteopathic and allopathic hospitals are
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Table 2 Descriptive statistics of cost function parameters Osteopathic hospitals (n ¼ 50)
Control hospitals (n ¼ 50)
Paired t-statistic
Operating statistics Total beds (TOTBEDS) Average salary (WAGE) Average supply price (SUPPLIES) Inpatient days (TOTIPD) Surgical operations (SURG) Outpatient visits (TOTOUT) Total expenditures (TOTCOST)
120 $40,691 $466 23,952 3,689 76,362 $43,245,282
124 $36,768 $395 26,633 4,095 77,115 $41,294,448
.96 2.32** 1.75* 2.04** .91 .05 .50
Shift variables Case mix index (CASEMIX) Area wage index (AREAWAGE) System hospitals (SYSTEM) Contract managed hospitals (CONTRACT) For-profit hospitals (PROFIT)
1.25 $19.00 52% 14% 16%
1.20 $18.30 52% 10% 8%
2.21** 1.82* + .63 1.15
*Significant at the 10% level. **Significant at the 5% level. ***Significant at the 1% level. +Perfect match so standard error is zero.
Table 3 Estimated cost function parameters Variable
Osteopathic hospitals coefficients
Matching controls coefficients
Pooled sample coefficients
CONSTANT BEDS WAGE SUPPLIES TOTIPD TOTSURG TOTOUT PROFIT SYSTEM CONRACT CASEMIX AREAWAGE DUMMY DUMMY*BEDS DUMMY*WAGE DUMMY*SUPPLIES DUMMY*TOTIPD DUMMY*TOTSURG DUMMY*TOTOUT DUMMY*PROFIT DUMMY*SYSTEM DUMMY*CONTRACT DUMMY*CASEMIX DUMMY*AREAWAGE
4.38*** 0.09 0.11 0.39*** 0.47*** 0.16*** 0.25*** 0.07 0.08 0.11 0.14 0.01
0.35 0.33 0.50 0.60*** 0.41* 0.11 0.12 0.45* 0.05 0.19 0.84 0.02
0.35 0.33 0.50* 0.60*** 0.41** 0.11 0.12** 0.45** 0.05 0.19 0.84* 0.02 4.72 0.25 0.39 0.21 0.06 0.05 0.37*** 0.38 0.12 0.30 0.70 0.01
*Significant at the 10% level. **Significant at the 5% level. ***Significant at the 1% level.
just a random effect based on cost function estimates, efficiency indicators such as labor and supply costs per inpatient day are statistically different between these two groups of hospitals. For example, the average cost of an inpatient day at an osteopathic hospital is about $975
whereas that of the allopathic hospital is $831; a significant difference at the 5% level. The average cost of medical supplies at osteopathic and local control hospitals is $466 and $395, respectively; also a statistically significant difference. An average salary at an
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osteopathic facility is $40,691 in comparison to $36,768 at an allopathic facility. The average hourly wage index obtained from the Federal Register is 70 cents higher for osteopathic hospitals than that of allopathic hospitals which explains approximately $1,500 of the salary difference. Another possible reason for the salary difference may be related to the relatively smaller number of workers employed at osteopathic hospitals (499 FTE versus 539 FTE). Examining efficiency measures in the bottom of Table 4 reveals interesting results for the osteopathic hospitals. All average daily census, adjusted daily census and occupancy rate are lower than their counterparts which clearly suggest that these hospitals are low volume hospitals with empty beds. The lack of demand combined with higher costs at osteopathic hospitals causes almost all efficiency indicators to favor allopathic hospitals. The average cost per patient day is computed twice: AVERAGE1-defined as total cost divided by total inpatient days and AVERAGE2-defined as total cost divided by adjusted patient days. The average cost of an inpatient day becomes significantly higher for osteopathic hospitals when it is computed as total costs divided by adjusted patient days (AVERAGE2) rather than inpatient days. This implies that there may be additional efficiency problems in osteopathic hospitals associated with outpatient services and surgeries, elevat-
ing the level of financial difficulties. This finding is also confirmed by the cost function estimates. The proportion of full-time hospital employees in FTE is about 85 percent for osteopathic hospitals and 82 percent at local control hospitals which is a statistically significant difference. The staffing ratio FTE per bed is lower for osteopathic hospitals than their local controls which suggests that there is a relatively smaller number of employees at osteopathic hospitals, but they are proportionately more full time. To determine the sources of cost inefficiencies among osteopathic hospitals, labor and capital inputs are closely examined. First, total full time and part time hospital personnel, grouped into 36 categories (see Table 1) were compared. Interestingly, only five significant variables emerged from this analysis: (1) fulltime physicians and dentists, (2) full-time medical and dental residents and interns, (3) full-time equivalent physicians and dentists, (4) full-time equivalent medical and dental resident and interns and (5) full-time equivalent total medical trainees. Osteopathic hospitals employ more than twice as many physicians and dentists as their counterparts (9.76 versus 4.42), and provide training to a considerably large number of residents and interns (24.56 versus 1.42) which may have been the main cause of higher hospital costs. Likewise, the FTE of physicians and dentists, and
Table 4 Significant differences in descriptive statistics between osteopathic and allopathic hospitals Osteopathic hospitals (n ¼ 50)
Control hospitals (n ¼ 50)
Variables
Mean
Standard deviation
Mean
Standard deviation
t-stats
Output variables Total facility inpatient days (IPDTOT) Adjusted patient days (ADJPD)
23,952 41,588
19,982 32,604
26,634 47,632
19,579 33,345
2.04* 2.34*
9.76 24.56
15.24 35.49
4.42 1.42
8.38 6.37
2.41* 4.85**
10.84
15.53
6.62
12.39
2.01*
Input variables Full time physicians and dentists (FTMDTF) Full time medical and dental residents and interns (FTRES) Full time equivalent physicians and dentists (FTEMD) Full time equivalent medical and dental residents and interns (FTERES) Full time equivalent total trainees (FTETTRN)
24.64
35.60
1.82
6.83
4.72**
25.40
36.519
2.00
6.908
4.74**
Efficiency indicators Average daily census (ADC) Adjusted average daily census (ADJADC) Occupancy rate Average cost per adjusted PD (AVERAGE2) Average salary per FTE % Full-time employers in FTE Case mix index (n ¼ 46)
65.64 114.00 .49 975 40,691 .85 1.207
54.737 89.399 .18 489 12,364 .09 .21
73.02 130.52 .56 831 36,768 .82 1.268
53.681 91.355 .18 54 9,337 .11 .17
*Significant at the 5 percent level. **Significant at the 1 percent level.
2.05* 2.34* 2.60* 2.17* 2.32* 2.32* 2.22*
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medical and dental residents and interns is greater at osteopathic hospitals than their counterparts, which factors in few part time employees into the statistic. These differences in employment measures are significant at the 1% level. A large number of physicians, dentists, residents and interns do translate to a higher case mix index at the osteopathic hospitals. The average case mix index is 1.25 for osteopathic hospitals and 1.20 for allopathic hospitals. To investigate capital inputs of osteopathic and allopathic hospitals, we used 20 variables, mostly bed size by service, and surprisingly we found no statistically significant differences in means (see Table 1, column 1). An average osteopathic hospital has 120 beds whereas a local control hospital possesses 124 beds; not a significant difference. The number of bassinets, births, outpatient visits and surgeries is not significantly different between these hospitals. The number of general acute, intensive and subacute care beds and their subcategories are very similar in osteopathic and allopathic hospitals. Although these statistics are not reported here, they are available upon request.
Discussion The study investigated the cost structure of osteopathic hospitals and their local counterparts, and found substantially higher costs at osteopathic hospitals. Capital and labor inputs, and hospital costs were examined, and two major reasons emerged for relatively high costs of osteopathic hospitals: (1) a large number of full time physicians and dentists, and also full-time residents and interns employed, and (2) the relatively high cost of outpatient services. On average, an osteopathic hospital approximately spent $2 million more than an allopathic hospital ($43.2 million versus $41.2 million). This suggests that the higher cost of osteopathic hospitals is associated with medical education, not related to capital costs. Past research shows that there are significant costs associated with medical education at teaching hospitals (Thorpe, 1988; Campbell, Gillespie, & Romeis, 1991). Perhaps, financially supporting osteopathic hospitals by medical school tuition revenues may assure the survival of osteopathic hospitals in the long run. Cost function estimates revealed interesting results for osteopathic and control hospitals. All output elasticities for osteopathic hospitals are statistically significant whereas only the coefficient of inpatient days for allopathic hospitals is found to be critical, which is the industry-wide trend. The price of supplies appears to significantly contribute to the total costs in both types of hospitals. Outpatient visits are produced more costly by osteopathic hospitals and this is the only statistically significant difference in the estimated cost function parameters of
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osteopathic and allopathic hospitals. At the margin, a onepercent increase in outpatient visits may not impact the hospital costs at allopathic hospitals, but increases osteopathic hospital costs by 0.25 percent. Overall, osteopathic hospitals were found to be high cost facilities. This study rather focused on the high cost of osteopathic hospitals and possible strategies to preserve these facilities in the long run. The literature that investigated hospital mergers and closures in the past revealed that the cost structure of osteopathic hospitals, and merged or closed hospitals is similar. A recent study showed that merged hospitals had high costs and low patient volume prior to merging in comparison to their local counterparts (Sinay & Campbell, 2002). Osteopathic hospitals must emphasize improvements in cost efficiencies which may eventually lead to closing the gap between themselves and their local counterparts for survival. Otherwise, this vulnerability could result in a closure or a merger that is likely to force them to lose their osteopathic identity. According to Modern Healthcare (Bellandi, 2000) hospital administrators are often reluctant to implement cuts in staffing, eliminate beds, or reduce duplicated services. This reluctance stems from concerns that such reductions might jeopardize their market share, creates ill will in the community, or make them unattractive to doctors, patients, or managed care plans. Nonetheless, these operational changes are important methods for reducing costs when confronted with payment reductions and greater competition between hospitals in local markets. Osteopathic hospitals are faced with the same dilemma like the hospitals of late 1980s and 1990s: restructuring, eliminating clinical services and downsizing facilities or staff to improve the organization’s financial position. The use of a small sample and a matching case-control design rather than randomly selecting several control hospitals in the local market or nationally may limit the study findings. This approach puts much more weight on the performance of a single matched control observation rather than the average of several observations. Also matching does not guarantee that the matched pairs are homogeneous in other non-measured characteristics that might influence the outcome. Statistics on several other controls—occupancy rate and case mix index are not included in the initial matching process, which may have provided additional information about osteopathic hospitals and their counterparts; however, this was not the intent of the study.
Acknowledgements I am indebted to Todd Linden, President and CEO, Grinnell Regional Medical Center, Grinnell, IA for his interest and support of the project.
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I would like to thank Dr. Simon Gelletta for his computer program to match osteopathic and allopathic hospitals, and my research assistant Kathryn Borman for her efforts to prepare the data for analysis. I also would like to thank two anonymous referees for their valuable comments.
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