The Effects of Organizational Variables on Hospital

8/3/2019 The Effects of Organizational Variables on Hospital

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The Effects of Organizational Variables onHospital Costs in Georgia

John Fard, Roozbeh Kangari, Baabak Ashuri

ABS TR ACT

W e analyze 87 acute care hospitals in the state of Georgia and evaluate how hospitalorganizational structure variables impact normalized facility costs. W e apply Linear RegressionAnalysis method from statistics to identify the significance of major organization variables onhospital cost variations. To explain variations in hospital costs in this study, we focus on thefollowing organizational structure variables: ownership type, financial structure, location,bedsize, breadth of services, and teaching status. The results of our regression model show thatincreasing breadth of services, hospitals located in rural regions, and not-for-profit organizationsare all expected to experience higher hospital costs per bed than their counterparts. The findingsof this study can be used to make educated decisions to help hospitals in Georgia be financiallystronger and stay in business. The results can also lead to new and existing hospital organizationsto better understand how particular changes in a facility impact cost. Additionally with thisinsight, facility managers can use this knowledge to better understand cost behavior of their facilities and be more accurate in facility cost management.

K EYWORD S: Analysis, cost, Georgia, healthcare, hospital, facility, model, and research.

IN TRODUCT I O N

The issue of soaring hospital costs is a hot topic among policy makers and the health careindustry. Over the last half century, increasing hospital costs have challenged healthcare facilitiesto review and adapt to organizational practices in order to stay competitive in the business. Sincethe United States government modified Medicare and Medicaid policies to reimburse hospitals

based on fixed reimbursement numbers, cost control has become an integral focus of hospital business plans (Fournier, 1992). Furthermore, the complex nature of the hospital industry makescost control a unique and mind-boggling challenge.

Hospitals are widely considered as one of the most complex organizations to manage in theUnited States. Hospitals are constantly struggling with balancing Medicare, Medicaid, and

privately insured patients, all of which pay different amounts for services. This happens whilehospital costs persistently continue to rise. These cost and management challenges date back more than a half century ago. The cost of a day of hospital care in 1970 was 5 times the amountas in 1950; the increasing trend has continued ever since. Increasing costs have forced a widerange of actions: national healthinsurance, government grants for hospital construction andmodernization, employee training programs, and incentives for managerialefficiency(Feldstein, 1971). In order to generate revenue, hospitals are being forced to competeon varying levels in order to win patients business. Many hospitals are turning to specializedservices in order to bring in patients. However with competing to offer the specialized servicesand staff comes ever increasing costs (Fournier, 1992). Another rising concern within the


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industry is the steady increase in uncompensated care. If hospitals continue to see these costsrise, a continuous shift from patient care revenue to non-patient revenue will need to occur inorder for hospitals to stay in business(Schuhmann, 2008).

Understanding the significance of how varying factors affect hospital costs can be extremely

useful for government policy makers, private hospital developers, and hospitalfacility managers. Matching hospitals with certain promising characteristics can be used as atool to aid in setting up a feasible business plan to help facilities ultimately stay afloat andcontinue offering health care services to the public. W ith respect to facility management, a keyresponsibility any facility manager is faced with is effectively managing facility costs. This isextremely important particularly in hospitals. To effectively manage facility costs in hospitalsinvolves ensuring that funds are accessible to manage and keep the facilityserviceable. Designed as Type I structural buildings, hospitals are thought of as one of only ahandful of key facilities around the country that should be up and running at all times. The

public cannot afford to suffer any lapses in hospital service availability, whether due to improper facility cost management, natural disaster, or any other factors. W ith increasing costs and

traditionally limited profit margins, hospital facility cost management becomesincreasingly difficult. Therefore insights into how facility costs are affected can prove to bestrategic tools to lead to successful cost management. The following study looks to explainhospital costs (both facility and service costs) through organizational variables. W e attempt tounderstand how these variables drive the behavior of costs with the intent that facility managerscan use this knowledge to better understand cost behavior of their facilities. W ith insight intohow their particular organization is set up and what cost impacts are expected, facility managerscan then focus cost management attention on the worthwhile aspects of their facility. As Then (2005) states, The emphasis on appropriateness and affordability inspace demand and facilities support services, calls for a management approach thatis underpinned by a clear understanding of the drivers of business demands and the processesthat meet the business requirements.

Therefore, this research study aims to identify the most significant factors that play a role incost variations at hospitals in the state of Georgia. The study seeks to provide insight on howvariables of hospital organizations affect costs. Using regression analysis, the following researchwill identify which variables influence costs, and will quantify each variables impact on hospitalfinances. The model in this study is not set up to look at the inner workings of how afacility operates, but to find relationships between the broader structure within which a hospitalfunctions and the cost it experiences. Similar to past studies on hospital finances, this researchwill focus on variables of a hospital that make up the characteristics of its organizationalstructure. The variables investigated here are ownership status, financialstructure, location, size, complexity of services, and teaching status. To achieve our researchobjective, this paper is structured as follows: research background, methodologyand modeling, and results. The research background is summarized in the next section. A varietyof hospital cost studies and their findings are presented here. In the methodology and modelingsections, several hospital organization variables are presented as possible factors to describe costvariations and linear regression analysis is described. Finally, our hospital cost model based onthe sample of 87 Georgia hospitals is developed and presented in the results section.


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RE SEARCH BA C K GROU N D

In past research, hospital cost studies have presented different approaches to the topic. Coyne etal. (2009) examined how size of hospital and ownership type influences cost and efficiency inW ashington state facilities. In their model, the authors applied five efficiency ratios and five cost

ratios,mainly having to do with assets, occupancy, operating costs, and employee inputs. Thestudy concluded that neither size nor ownership type affected the relative costs of hospitals.

Sloan and Vraciu (1983) investigated the differences between investor owned and not-for- profit hospitals in the state of Florida. W hen comparing similar hospitals to each other, they findthat economic performances of both financial structure types are quite similar. For-profit andnot-for-profit share comparable results with respect to after-tax profit margins, percentages of Medicare and Medicaid patient days, charity care, and bad debt. As for comparing investor-owned and not-for-profit hospitals with differing services, no patterns could be detected.

Fournier and Mitchell (1992) analyzed hospital costs under levels of competition, and also

looked at how organization structure affects costs. This study examined the effects of acute andintensive care inpatient admissions, outpatient visits, maternity procedures, emergency roomvisits, and surgery minutes on hospital costs in the state of Florida. Hospital costs werecharacterized as a function of services offered, input prices, fixed capital stock,and the number of admitting physicians. Their results indicated that hospital competition for patients induce minor impacts on cost. Nonetheless, as competition intensifies, the costs associated with inpatientvisits, outpatient visits, emergency room visits, and surgery minutes are expected to increase.Onthe other hand, hospital costs associated with maternity procedures are expected to decrease inhighly competitive markets. Furthermore, Fournier and Mitchell claim for-profit proprietaryhospitals are expected to have significantly lower costs than private not-for-profit andgovernment ownedhospitals. Using the concepts of economies of scope and ray scale

economies, this study concludes that larger hospitals providing a variety of services are expectedto decrease costs, due to increased efficiency.

Another angle on hospital cost study was Feldsteins (1971) work which strived to understandand explain the sources of hospital cost inflation across the United States. He presentsa theoretical model of the use, cost, and expansion of hospital inpatient services, which are

broken up into four groups:demand relations, price adjustment, components of cost, andexpansion of capacity. Equations for demand relations showed high price elasticities for

per capita admission and mean stay per case. A larger supply of general practitioners decreasesdemand for admissions and longer length of stays,while greater supply of other practitionersincreases demand. His study determined that larger numbers of beds increases admission andlength of stay simply because beds are available and prices may be lower. Feldstein concludesthat increasing insurance coverage and personal incomes, and the availability of high paidspecialists lead to increased hospital care prices, and that these higher prices are the sources towhy costs are increasing.

Schuhmann (2008) studied the impact of uncompensated care costs on hospital profitability. Hisstudy states that the steady rise of uncompensated care costs are putting American hospitals infinancial danger. He compared the financial performance of hospitals that have different types of


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ownership control, location, and teaching (education and training providers) status. Schuhmann concluded that while government owned hospitals had the highestlevel of uncompensated care, their overall net income was healthy and not far behind private not-for-profit and for-profit organizations. In terms of location, rural hospitals have much higher rates of uncompensated care, but do a good job of balancing their other costs of care to match

urban income rates. As for teaching status, teaching-hospitals endure higher uncompensated carerates, but are better able to counter with non-patient care revenue to nearly match income rates of non-teaching hospitals. Schuhmann warns that as uncompensated care continues to rise, hospitalswill need to focus onraising revenue through non-patient activities to balance costs.

Lavy and Shohet (2007) studied the effects of certain parameters of healthcare facilities on performance of the facilities and their systems. Motivation for the study came from facilitymanagers constant challenge of reducing non-core expenditures and while keeping up with theowners expectations of improved performance. Using the results, the researchers developed adecision support model that can be used to operate facilities from a life cycle perspective. The

parameters that were used to project maintenance expenditures included: category of

environment where facility is located (marine or in-land), occupancy in number of patient beds per 1000 square meters, age of building in years, and complexity of built areas that the healthcarefacility services.

Then (2005) studied the integrative nature between real estate assets, facilitymanagement, and workplace environment. The paper claims that a proactive propertymanagement model is necessary to enable and maintain a successful corporate workplaceenvironment. The research approached reaching suitable environments from a

business perspective. A framework was developed to model the relationship between strategic business planning and facility operations. Then gathered data through an extensive literaturereview and interviews of real estate, property, and facility managers. The research claimed that

understanding an organization and its strategy was imperative to be able to understand theinteractions between organizational structure, strategic direction, work processes and enablingthe workplace environment.

The following research in this study builds upon and contributes to this body of knowledge inidentifying what organization factors are significant in influencing hospital costs. Our researchmethodology is presented in the subsequent section.

RE SEARCH METHODOLOGY

The focus of this study is to evaluate the organization factors (variables) which affect hospitalcosts. Specifically, this study looks at short term, acute care hospitals located in the state of Georgia (scope of research). In Georgia, one sees a wide variety of facilities and surroundingcharacteristics which the hospitals possess. The facilities bring together multiple and widely-ranging services, employees, and customers. The study is limited to just the state of Georgia tokeep consistency within the hospitals modeled with respect to localgovernment regulations, local financial impacts, and other regional influences. However, our research framework is general and can be applied to a large sample of hospitals across thenation. Clearly the results may be different. As of 2008, Georgia featured 89 short term, acute


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care (general and specialty) hospitals, located in over 70 different cities. This study incorporates87 of the 89 hospitals in this investigation; the remaining two hospitals lacked comprehensivedata. Based on the US Census definitions, a little more than half of the hospitals are located inurban regions and a little less than half are in rural regions. About 65% of the hospitals are

privately owned. The remaining facilities are government owned. About 70% of the hospitals are

not-for-profit, versus about 30% being for-profit. In terms of size, a little over 70% of Georgiahospitals are less than 200 beds, but range from 24 up to 644 beds. See Figure 1 for a summaryof the Georgia hospital data.

Figure 1: Georgia hospital data summary

This study analyzes hospital costs by means of normalizing overall costs with number of beds. The cost per bed metric allows various sized hospitals to be compared to oneanother. Cost data is obtained via the published data from the Centers for Medicare and Medicaid

Services (CMS) Form CMS-2552-96, W orksheet C, Part 1 (Overview Cost Reports, 2010). Thedata provides lump sum costs of a hospitals inpatient routine service, ancillaryservice, outpatient service, and other reimbursable cost centers. This impliesthat uncompensated care costs are also included in these lump sum costs. These categories aremade up of departmental treatment costs; costs include those associated with running particular service centers, rooms, and labs,equipment rentals or purchases, ambulance services, non-patientservice costs such as cafeterias and gift shops, etc. W ithin these subcategories,hospitals accountfor employee salaries, plant operation costs, building maintenance and repairs, housekeepingservices, etc. Table 1 provides a more comprehensive list of the cost breakdown. The costs usedin this analysis are from fiscal year 2008; this was the most recent complete published data thatwas available via CMS at the time of this study.

Table 1: Hospital Cost Breakdown: description of costs included in overall cost summation

Cost Breakdown

Employee Salaries Housekeeping Social Service

Employee Benefits Dietary Other General Service

Old Capital Related Costs Cafeteria Non-physician Anesthetists


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New Capital RelatedCosts Maintenance of Personnel

Nursing School

Administrative & General Nursing Administration Interns & ResidentsSalaries & Fringes

Maintenance & Repairs Central Services and Supply Interns & Residents Program Costs

Operation of Plant Pharmacy Paramedical Education

Laundry and Linen Medical Records and Library

As mentioned above, the scope of this study is limited to hospital cost-factors withinorganizational structure variables. The variables used to explain costs in this study arehospital ownership, financial structure, location, size, breadth of services, and teachingstatus. These variables are described in detail in Table 2, and summary statistics can be found inTable 3. These variables are similar to those used in past studies to evaluate hospital finances,asis reviewed in the Background section above.

Table 2: Description of Variables

Variable Description Example Source

Ownership Status Government or Private Private Healthcare CostReportInformation System

Financial Structure For-profit or Not-for- profit For-profit Healthcare Cost

ReportInformation System

Location Urban or Rural Rural 2000 US Census

Bed Size # of serviceable beds 97 Healthcare Cost

ReportInformation System

Breadth of Services Ratio of 50 mostcommonservices offered 0.45 American Hospital Directory

Teaching Status Provides medicaleducationservices Yes American Hospital Directory

Table 3: Summary Statistics

Variable Observations Mean* Std. Dev. Min Max

Hospital Costs/Bed 83 $607,896 $214,344 $255,555 $1,117,870

Log of HospitalCosts/Bed 83

13.25 0.380 12.33 13.93

Government 83 0.66 0.478 0 1

Private 83 0.34 0.478 0 1


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For-profit 83 0.28 0.450 0 1

Not-for-profit 83 0.72 0.450 0 1

Rural 83 0.46 0.501 0 1

Urban 83 0.54 0.501 0 1

Bed Size 83 174.33 151.880 32 644 Breadth of Services 83 0.36 0.162 0.08 0.72

Teaching 83 0.17 0.377 0 1

Non-Teaching 83 0.83 0.370 0 1

* For variables whose min and max is 0 and 1 respectively, the mean represents the fraction outof 1.00

for which the variable is represented in the sample.

The variable for ownership status of hospitals is divided between government and privatelyowned facilities. These data are extracted for each hospital from the Healthcare CostReport Information System (HCRIS) via CMS (Overview Cost Report, 2010).Government hospitals include facilities owned and operated by federal, state, county, and citygovernments. Private ownership entails facilities affiliated withchurch, individual, corporate, and partnership owners. Coyne et al. (2009) conclude thatownership status of not-for-profit hospitals in W ashington State does not affect costs inhospitals. However, for-profit hospitals are also included in this study considering the number of such organizations in Georgia. This study will aim to determine if government run organizationsmay be less cost-efficient than private businesses. This ownership data is taken from the most

recent HCRIS publication, in which all sampled hospitals reported ownership type between theyears 2007 and 2009. W hile the hospital costs used in this study are from 2008, this study is

based on the assumption that ownership type has not changed for any of the hospitals betweenthe years of 2007 to 2008, nor 2008 to 2009.

The variable for financial structure of a hospital is comprised of for-profit and not-for- profit organizations. These data were also extracted from HCRIS(Overview Cost Report, 2010). Not-for-profit organizations include all government hospitals, as well as voluntarily-claimed private hospitals. The remaining private hospitals are for-profit. This classification is included todetermine if cost differences exist between the for-profit and not-for-profit organizations. Paststudies by Sloan and Vraciu (1983) indicate financial structure does not affect profit margins for

similar hospitals, and that no conclusions could be made when comparing hospitals withdissimilar services. Financial structure data for Georgia is taken from the most recent HCRIS

publication, in which hospitals reported financial structure between the years 2007 and 2009. Asabove, it is assumed that financial structure has not changed for any of the hospitals betweenthe years of 2007 to 2008 nor 2008 to 2009.

Location of the facility is used in this study to determine if region affects a hospitals costs. Sitelocation is described as being either urban or rural. Due to incomplete data from HCRIS, rural


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and urban hospital reports are based on 2000 United States Census data (United States CensusBureau, 2009). Urban regions are defined by the Census Bureau as densely settled blocks or groups of blocks that have a population density of at least 1000 people per square mile andsurrounding regions that have at least a density of 500 people per square mile. Under certaincircumstances, other less dense regions may also be classified as urban. All other areas are

defined as rural (United States Census Bureau, 2009). Location is believed to inducehospital costdifferences due to the demand of experienced or skilled staff, varying demographics, and varyingeconomic environments. This investigation assumes that rural and urban classifications of hospital locales in Georgia have not changed between 2000 and 2008.

Bed size is used as a variable to measure the size of the facility. W ith over 70% of the sampledhospitals servicing less than 200 beds, a histogram shows bed size data skewed towards thesmaller sized hospitals. However the other 30% of hospitals range from 200 to 644 beds. Thisstudy seeks to find out how size impacts cost. Past studies have indicated mixed results. Coyneet al. conclude that size does not affect costs in W ashingtonhospitals; while Fournier andMitchell (1992) conclude that larger hospitals lead to decreased costs in Florida. Bed size data is

from HCRIS (Overview Cost Report, 2010), and is specific to year 2008 measurements.

In order to determine how well a hospital provides access to different treatments, breadth of services offered is included in this study. Here, the breathof services, or service complexity, ahospital offers is characterized by a ratio computed from how many services a hospital providesout of the 50 mostcommon procedures that hospitals generally offer. W ith the ratio ranging

between 0 and 1, a higher ratio signifies that the hospital provides a more comprehensive portion of the 50 services. The 50 most common procedures are provided by the AmericanHospital Directory (American HospitalDirectory, 2010). The AmericanHospital Directory (AHD) then determines if a hospital has a specific service only if 10 or moreoccurrences are recorded within a year. Table 4 shows additional information on services

offered. Fournier and Mitchell (1992) conclude that larger facilities that offer a variety of services are expected to have lower costs, due to increased efficiency. Breadth of service data isgenerated from clinical service reports that are recorded by AHD from a hospitals most recentMedicare Cost Reports, Medicare inpatient claims data, Medicare outpatient claims data, andother related sources, many of which are from 2009. It is assumed that the hospitals analyzed inthis study have not varied their services substantiallybetween 2008 and 2009.

W e use this ratio definition of complexity due to the limitations of multiple regressionanalysis. An unsupported alternative to the ratio score would be to simply include

binary variables for each individual service. This would allow the model to investigate theinfluence on cost for each individual service.However, due to the limited sample size, regressionanalysis would not be able to accommodate an unlimited number of independent variables to bemodeled. The model may not perform well due to lacking a sufficient number of degrees of freedom.

Teaching status is the final organizational structure variable included to model hospitalcosts. A hospitals teaching status signifies whether or not the organization employs medicalcollege interns and residents to learn and work in the facility. This variable is included in thestudy due to the added costs of employing and training interns and residents, as well as the fact


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that Schuhmann (2008) concludes that teaching facilities experience higher uncompensated carecosts than non-teaching facilities. A little over 15% of the hospitals sampled were teachinghospitals. This information was also extracted from AHD for the year 2009, and is not believedto have substantially changed from 2008 to 2009. In the next Section, we summarize thedevelopment processes of the hospital cost model.

Table 4: Breadth of Services: 50 most common services according to AHD

HO SP I T AL CO ST MODEL IN G

This study uses regression analysis to model hospital costs. Regression is one of the mostcommon and popular modeling methods used in research, and has been used in past hospital cost

studies. The technique allows modeling multiple independent variables. In this model, hospitalcost per bed(dependent variable) is estimated as a function of organizational structurevariables (independent variables). The results of the analysis explain how variations in theindependent variables influence the dependent variable, and also give the level of statisticalsignificance. Ordinary Least Squares (OLS)is used in this study, being that it is a basic andcommon approach to regression analysis. OLS is implemented using Stata/SE 8.0software (Stata/SE 8.0, 2003). Table 5 shows the list of regression model variables.


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Table 5: List of Regression Model Variables

Model I nput/Output Variable Value

Hospital Cost Cost/Bed Cost of inpatientroutine, ancillary, outpatient, and other reimbursable cost centers per bed

Ownership Status V1 = priva te 1 if private, 0 if government

Financial Structure V2 = f or-pr o fit 1 if for-profit, 0 if not-for-profit

Location V3 = rural 1 if in rural region, 0 if in urban

Bed Size V4 = size number of beds serviced

Breadth of Services V5 =com pl exity Ratio of services offered, range: 0.00 - 1.00

Teaching Status V6 = teaching 1 if hospital provides teaching services, 0otherwise

As an initial check to avoid collinearity, Pearson correlation is checked between the independentvariables. W hen two variables have perfect, positive increasing linear relationships, the correlation is +1; when two variables have perfect, negativedecreasing relationships, the correlation is 1; correlation of 0 shows absolutely no linear relationship between two variables. Any other values between 1 and +1 indicate the dependencydegree between two variables. The closer the correlation is to either 1 or +1 the stronger thecorrelation between two variables. Table 6 shows the outcomes of the Pearson correlationresults between the variables, and indicates strong positive correlation between size and

complexity. Therefore the best subsets regression analysis is performed to determine which of these two variables should be retained in the regression model and which one should beremoved. The results indicate that complexity develops a better model and therefore, size should

be dropped from the equation. See Table 7 for results of the best subsets analysis.

Table 6: Pearson Correlation Results

P rivate For- P rofit Rural Size Complexity Teaching

P rivate 1.00 - - - - -

For- P rofit 0.45 1.00 - - - -

Rural 0.09 0.10 1.00 - - - Size -0.10 -0.17 0.23 1.00 - -

Complexity -0.02 -0.15 0.32 0 .80 1.00 -

Teaching -0.08 -0.06 0.10 0.66 .48 1.00

Table 7: Best Subsets Analysis: Size and Complexity


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Variable R2 R2-adjusted

Complexity 37.5 36.8

Size 20.9 19.9

Complexity & Size 37.9 36.4

Taking these considerations into account, the regression equation to model hospital cost per bedcan be estimated as:

log (cost/bed ) = 0 + 1V1+ 2V2 + 3V3 + 4V4 + 5V5

In this equation the parameters are defined as: cost/bed = hospital cost per bed; log = commonlogarithm; 0 = intercept of linear equation; i = an integer number between 1 and 5representing variables; i = designated constant for variable i; V1 = private (variable1 indicating ownership status, 1 if private, 0 if government as shown in Table 5); V2 = for

profit; V3 = rural; V4 = complexity; and V5 = teaching. Table 5 summarizes the values of these

variables.

From sampling alternative options to represent the best relationship between the independentvariables and hospital cost, the log of hospital cost per bed is used. This relationship explains theinfluence of the independent variables on costs through growth rates or percentagechanges. Growth rate is an easily identifiable and understandable measure to explain changes incosts.

W e need to check the significance of our linear regression model for our specific dataset to justify our choice. To confirm residual normality, a normality test is performed. Figure 2shows the results of a quantile-quantile plot of the residuals. The data points fit the line decently

well except for a few outliers outside the 95% confidence interval. The null hypothesis of thenormality test states that the residuals are normal. The alternative hypothesis statesotherwise. The test gives a p-value of 0.085. Therefore the null hypothesis cannot be rejected at a5% significance level, and residual normality can be concluded. However to provide a

better, more normal population, the 4 outliers discussed above are removed from the sample population. Therefore,the sample is made up of 83 Georgia hospitals. The resulting quantile-quantile plot is presented in Figure 3. The resulting p-value of this normality test is 0.378, and sothere is a strong case not to reject that the residuals are normally distributed.


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Figure 2: QQ-Plot for Residual Normality

Figure 3: QQ-Plot for Residual Normality without Outliers


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SUMM ARY OF RE SULT S

Results of the Ordinary Least Squares (OLS) regression analysis using Stata/SE 8.0 providesome interesting insights on hospital costs. The findings show some expected results, as wellas some that require deeper interpretation. Table 8 illustrates the results of the analysis. Theresults of the OLS analysis show that as servic e com pl exity increases, hospital costs per bedincrease. W ith other variables remaining constant, as a hospitals complexity ratio increases by0.01, cost per bed is expected to increase by 1.56%. This result can be explained throughconsidering a hospital that adds services or treatments will also incur increases in trainedstaff, equipment, maintained space, and other services. The regression results indicate that thecoefficient for complexity is statistically significant at a 1% significancelevel. Therefore, this coefficient can be taken with high confidence. The 95%confidence intervalfor complexity is [1.147, 1.981].

Table 8: Regression Analysis Results

Variable Coefficient Std. Err. t P>| t | 95% Conf. I nterval

priva te 0.039 0.065 0.60 0.553 -0.09 0.168

or-pr o fit -0.117 0.080 -1.69 0.09 6 -0.256 0.021

rural 0.115 0.060 1.93 0.0 58 -0.004 0.233

com pl exity 1.564 0.209 7.47 0 .000 1.147 1.981

teaching 0.035 0.084 0.41 0.679 -0.132 0.201

Observations = 83; R-squared = 0.596; and R-squared adjusted = 0.570

As for l oca t ion, the regression model indicates that rural hospitals are expected to have costs11.5% higher than urban hospitals. W ith a p-value of0.058, the effect is significant at a10% level and has a 95% confidence interval of [-0.004, 0.233]. W ith similar representation inthe samplepopulation, there are stark contrasts between the characteristics of rural and urbanhospitals in Georgia. Rural hospitals generally service many more beds than urbanfacilities. The average and median size of rural hospitals are 214 and 170 beds respectively, asopposed to 140 and 80 beds at urban hospitals. Similarly, service complexity follows the sametrend. The average and median complexity scores at rural facilities are 0.42 and 0.44respectively, versus 0.31 and 0.28 at urban facilities. Further, 21% of rural hospitals incorporatedteaching services, as opposed to 13% at the lower costing urban hospitals.

In terms of financial structure, the OLS model yields that for-profit organizations are expected tohave lower costs per bed than not-for-profit hospitals.For-profit organizations are expected tohave costs 11.7% lower than not-for-profit facilities. This effect is statistically significant at a10% level, and has a 95% confidence interval of [-0.256, 0.021]. W hen looking at for-profit andnot-for-profit organization data closer, one sees a trend with complexity. The average and


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median complexity scores of for-profit hospitals are both 0.32, as opposed to 0.38 and 0.37 atnot-for-profit hospitals.Additionally, only 13% of for-profit organizations provide teachingservices, as opposed to 18% of not-for-profit facilities.

The results of the regression model indicate moderate change in hospital costs between different

ownership statuses. The OLS model yields that privately owned hospitals are expected toexperience 3.9% higher facility costs per bed. However, with a p-value of 0.55 and a95% confidence interval of [-0.091, 0.168], the results of this study indicate that the coefficientfor ownership status is not statistically significant at any meaningful level.Private andgovernment owned Georgia hospitals in this sample shared similar characteristics. Eachrepresented an average and median complexity score of about 0.36 and 0.35 respectively. As for size, private hospitals had a median of 120 beds versus 108 beds at governmenthospitals. The 95%confidence interval of bed size for private and government ownedfacilities was [40, 479] and [37, 552] respectively.

Similarly, the model claims that teaching status does not impact hospital costs at a significant

level either. The model yields that teaching hospitals incur a 3.5% increase in costs. Thiscost increase was expected as the cost of hiring and training interns and residents can besignificant. However, the p-value for the coefficient of teaching status sits at 0.679 and has a95% confidence interval of [-0.132, 0.201]. Less than 17% of the sampled hospitals providedteaching services.

The results of the regression model report an R-squared value of 0.596, meaning the independentvariables explain almost 60% of the variation for hospital costs. This value may notseem high, however considering what the model is trying to explain and the scope of theexplanatory variables, this R-squared value seems accurate and sufficient. As mentioned

before, hospitals are highly complex organizations with a plethora of variables that can affect

relative costs. Considering this study evaluates only organizational variables and obtains an R-squared value of 0.596, it makes sense that plenty of other variables concerning how the hospitalis operated could account for explaining the remaining 40% of variation.

In order to confirm that the models independent variables provide overall significance of theregression, an F-test was computed. The null hypothesis states that coefficients for all independent variables are equal to 0; the alternative states otherwise. The F statistic is about23, with 5 parameters and 77 degrees of freedom. The p-value is 0.000, and thus the nullhypothesis is strongly rejected. Therefore, we confirm that the variables in the regression modeldo jointly help explain variation in hospital costs.

To make sure the random variables maintain constant variance and so to certify any statisticalsignificance is accurate, the model is checked for heteroskedasticity. The model is runto compute heteroskedastic robust standard errors. Results are displayed in Table 9. The resultsof the robust model are very similar to the results of the original revised model, and all the pastsignificant independent variables remain significant at even stronger levels. Therefore it isconcluded that heteroskedasticity is not present. Regardless, to further test for evidenceof heteroskedasticity, the Special Case for the W hite Test was implemented. Results of the testresulted in an F statistic of 1.07, which is far less than the original critical F statistic of


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22.73.Therefore the null hypothesis is not rejected and there is strong evidence of no sign of heteroskedasticity. Therefore, the statistically significant coefficients can be accepted.

Table 9: Regression Results with Robust Standard Errors

Variable Coefficient Std. Err. t P>| t | 95% Conf. I nterval

riva te 0.039 0.068 0.57 0.571 -0.10 0.174

or-pr o fit -0.117 0.068 -1.71 0.091 -0.254 0.019

rural 0.115 0.055 2.09 0.040 -0.005 0.224

com pl exity 1.564 0.211 7.42 0.000 1.144 1.983

teaching 0.035 0.070 0.49 0.623 -0.105 0.175

Observations = 83; R-squared = 0.596

CO N CLU S I O N S

This study investigated how the organizational structure of Georgia hospitals affects the costseach facility incurs. Regression analysis was used to evaluate how costs were affected asorganizational variables were varied with other variables remaining constant. The model showedthe impact of eachvariable on cost, and the statistical significance level of eachvariables impact. Using Ordinary Least Squares (OLS), the model maintained that

complexity, location, and financial structure affect hospital costs at statistically significant levelsof 10% or lower. It is concluded that the Complexity variable is the most statisticallysignificant organization variable to describe hospital cost variations in the State of Georgia.

Results from the regression model conclude that as a hospitals ratio of complexity scoreincreases by 0.01, cost per bed is expected to increase by 1.56%. This result is contrary toFournier and Mitchells (1992) findings that hospitals which provide more services experiencelower costs due to greater efficiency. Although the data in this study show that complexity is

positively correlated with size, it is interesting that the results do not find Georgia hospitals toexhibit economies of scale characteristics with respect to complexity. Adding services andtreatments to a hospital clearly lead toincreasing costs. Facilities may need new equipment, newmedicines or drugs, additional rooms, and additional staff or may need to spend moneytraining existing staff. The regression model evaluated here implies that these additional costsmay outweigh the savings that economies of scale generate in Georgia hospitals. Further, thisconclusion helps strengthen Feldsteins findings that high paid specialists ultimately help driveup hospital costs. This can be valuable information for hospital developers and hospitalexecutives who may be assessing cost-benefit analyses for their organizations. The statistic can

be used to make educated decisions on the addition or exclusion of any potential services tooffer. The insight could be used to gauge whether the expected revenue of added patients will be


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enough to counter the expected expense of additional services? Facilitymanagementprofessionals can use this information to understand the impact thathigher complexity has on costs and to better prepare for such costs. Considering such astatistically significant result, perhaps facility managers will focus more on determining whichservices engender the highest costs so that energy can be directed to facility cost management of

those specific treatments.

As for l oca t ion, results of the model declared that hospitals located in rural regions are expectedto experience 11.5% higher costs than those in urban regions. As mentioned above, ruralGeorgia hospitals generally service many more beds and provide more treatments than urbanhospitals. This may be due to low density of hospitals in rural regions. W ith fewer surroundinghospitals, organizations may be motivated or feel a responsibility to offer a larger variety of services to treat patients. However a lack of increased efficiency from these larger organizationsseems to depict higher costs in rural facilities.Further, the higher rate of teaching hospitals inrural regions may also be supporting higher costs. Rural hospitals and costs present an interestingrelationship, and a significant one considering almost half of all Georgia hospitals are located in

rural regions. This information can be used to warn future rural located facilities to prepare for the added costs in their regions. W ith such a large discrepancy in costs between rural and urbanhospitals,facility management professionals working in rural facilities may use this insight tolook for reasons why the gap is so large. In turn, this practice could lead to possible solutions toclose the gap.

W hen evaluating financial structure of hospitals, the regression model concluded that for- profit organizations are expected to experience 11.7% lower costs than not-for- profit organizations. Similar to the hospital location variable, key differences between for-profitand not-for-profit hospitals may be triggering the disparity in cost. For-profit facilities in Georgiaare generally smaller, provide fewer services, and include fewer hospitals that offer

teaching services.

The OLS model found that the remaining independent variables, ownership status and teachingstatus, lack statistical significance in affecting hospitalcosts at any meaningful levels. A common

belief concerning ownership is that private organizations generally function more efficiently andare morebusiness-goal oriented than government-run organizations, resulting in lower relativecosts. However, the organizational characteristics of government and private hospitals were verysimilar, as were the costs per bed. Therefore the model had a difficult time differentiating thetwo, and so ownership status could not be proven to statistically affect relative costs. This issimilar to the findings from Coyne et al.s (2009) work of hospital costs inW ashington. Meanwhile, the lack of teaching hospitals in the population sample may have leadto the model not being able to conclude how teaching status affects costs. Less than 17% of hospitals sampled provided teaching services.

In studying relative costs of Georgia hospitals, this study evaluates the influence and statisticalsignificance of each individual variable on cost. The results can be extremely useful for makingdecisions on individual aspects of a hospital organization. Particularly for existingorganizations, these findings can provide insight on how particular changes in a variablewill impact their costs. The study also provides a foundation for facility management


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professionals to better understand how their particular facility behaves with respect to cost. Thiscan be a significant aid in better managing facility costs. Moreover knowing which variables arestatistically significant, this information can help hospital developers, executives, and facilitymanagers focus on the difference makers when attempting to adjust or manage costs.

FUTURE RE SE ARCH

The evaluation of how organizational structure variables impact hospital costs in Georgia raisesissues to be considered for future research. For future work, it should be noted that 87Georgia acute care hospitals and six independent variables were examined in thisstudy. However, increasing this sample size would increase the degrees of freedom andcould help to develop a more complete regression model. Other types of hospitals besides justacute care facilities could be included if the study intended to look at different types of hospitals. Also depending on the intent of the research,neighboring states or states with similar or contrasting qualities with Georgia could be included in the study to increase the samplesize. Along the samelines of limited degrees of freedom, future work could lead to a method for

categorizing a hospitals breadth of services into individual variables. This would yieldan individual impact on cost for each offered hospital service. As it stands in this study, themodel cannot differentiate between adding or excluding distinct treatments. This limits themodels use and insight especially when services have very different costimplications. Future work could also include using a different classification for location, besides the U.S. Census definitions for urban and rural. These density-basedclassifications result in sometimes puzzling results for certain locations. Additionally, a morerecent analysis of location than the 2000 Census numbers may result in moreinsightful results. Metropolitan Statistical Areas based estimated statistics of recent years could

be a possibility.

The results of this research also prompt new possible studies in other fields. Further work can bedirected at the apparent lack of economies of scale in Georgia hospitals when adding servicecomplexity. Hospitals already are thought of as complex organizations to manage, so doesadding more and more services in fact make management harder and actually decreaseefficiency? Could larger, more complex organizations lead to less personal management andface-time, and thus lower efficiency and higher costs? A few other potential research topicscome from the fact that rural hospitalsexperience 11.5% higher costs than urban hospitals. W iththe appeal of living in urban areas, are rural hospitals forced to compete with urbanorganizations to bring in skilled staff? Should policy makers explore offering aid torural hospitals to help ease the burden of these costs? Should policy makers motivate or incentivize more training of skilled workers in the healthcare field? On the other hand, is therural versus urban issue one of productivity and management? W ith fewer hospitals insurrounding areas and therefore less competition, rural hospitals may lack motivationto minimize costs. If this is the case, this data may prove to convince rural hospital executives toinvest in organizational productivity upgrades to try to reduce the high costs.

REFERE N CE S


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