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Finance, Accounting and Business Analysis 
Volume 4 Issue 2, 2022 

http://faba.bg 

Analysis of Financial Indicators Compared with Main Characteristics of 

Hospital Based Medical Care in Bulgaria  

Velimira Georgieva Chupetlovska  

Department of Finance, University of National and World Economy, Sofia, Bulgaria 

Info Articles  
 

Abstract 

Keywords:  
Financial indicators, hospital based 

medical care, economic dependencies  

 Purpose: The purpose of the study is to analyze whether there is a relationship 
between fundamental financial indicators of hospital based medical care (HBMC) 

depending on ownership, location, and type. The indicators are as follows: total 
revenue, short-term assets and liabilities, working capital, equity capital, and its 
components. A relationship was found between the control of hospitals and short-
term liabilities, location, and income. Moreover, dependence troughs individual 
financial indicators was also investigated. 

Methodology: The study examines 15 research units of hospital based medical care. 
The sample contains hospitals1 with the highest revenues based on contracts with 
the National Health Insurance Fund of 2021. Each of the following districts: Sofia, 
Burgas, Varna, Plovdiv, and Stara Zagora is represented by three HBMC. Specific 
analyses (Variance, Regression, and Descriptive statistics) were conducted by 
Verified statistical software – SPSS version 22. 

Results: Correlation between the hospital’s location and the income value was 
found. Moreover, a statistically significant relationship between ownership and the 
amount of short-term assets was recognized. On the other hand, a strong 
correspondence between the size of the fixed capital and total revenue has been 
proven. A moderately strong, positive association determines the interaction 
between income and short-term liabilities. No significant results were found in the 
analysis of all other variables. 

Conclusion: The results of the conducted study can be taken as bases for in-depth 
analyzes firstly in the field of financial stability, secondly on the importance of the 
distinguishing hospital’s characteristics. 

 

  

   

*Address Correspondence:   
E-mail: velimira.georgieva@unwe.bg 

 

 

 
1 In the text, the words hospital and hospital based medical care (HBMC) are used synonymously. 



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INTRODUCTION 

 

Several studies are researching the main sources of revenue for hospital based medical care. All of 

them are consolidated that the National Health Insurance Fund (NHIF) has an important role in the 

organization of health services provided around the country. The Ministry of Health is another immutable 

factor that provides additional payments regarding more specific health goods and services, classified as 

emergency medical care and significant illnesses, vaccinations, etc. The Municipal structures take care of 

their own hospitals and finance their activities according to the defined legal needs. In addition to these 

three sources, there are also payments from insurance companies, in connection with health insurance. 

Studies and reports regarding the government of the health sector in the country reach the general 

conclusion that a large share of its financing is due to direct payments by patients (Dimova et. al. 2019; 

Ministry of Finance 2005; IBRD 2015). This research examines the relationship between the incomes 

generated by HBMC from different sources, related to their allocation, ownership, and type. A 

correspondence between the created time lag of received revenues and coverage of expenses in the activity 

of private, government, and municipal health insurance companies is being researched. The different 

sources of income and the separate types of accrued liabilities, as well as the financial result as an indicator 

of the overall management of the HBMC, are been taken into consideration. Relations between the listed 

indicators are more likely to be observed while analyzing their ownership. This fact is connected to the 

specific management in government, municipal and private hospitals. 

The results of the conducted study aims to reveal correspondence between the current financial 

government of Bulgarian hospital based medical care and the management of the hospitals that provide it. 

Moreover, through this research, a competitive analysis diverted by regions regarding that kind of services 

as total for the segment. The principles used in controlling and financing the hospitals are well researched 

in order to show effectiveness in management, as well as taking into consideration their ownership.   

 

LITERATURE REVIEW 

 

Studies are proving a large number of hospitals per 100,000 population. It has been proven that in 

Bulgaria their number is 50% more than the average for the EU-27 member states. According to some of 

the studies, the pointed fact can accrue as a problem that should be solved by the government leadership 

and as a result should stop the uncontrollable number increase of hospitals. Another problem that the 

author examines is the valuation of the funds paid for the treatment performed (Nikolova 2013). Analyzes 

show that in 10 years, from 308 hospitals, they reached 348 (12.9% growth), while the number of private 

hospitals doubled - from 47 to 111 (Delcheva 1994). Hospital care in Bulgaria is provided by public and 

private medical institutions. The number of private hospitals is growing significantly, and in 2016 they 

were nearly 1/3 of the total number in Bulgaria (Dimova et. al. 2019). The correlation between the 

financing methods and the results of the hospitals were investigated. In the scientific work on the sources 

of financing (Ivanova 2020), the conceptual features of healthcare financing in Bulgaria are clarified. The 

implemented health reform and the problems arising from it are evaluated, and the need to apply a 

scientific approach to the management of the financial resource in health institutions if justified. As a result 

of the scientific research, the main trends and deviations in the financing of healthcare in Bulgaria, a result 

of the applied financing model in Bulgaria, have been identified. In similar developments, the contributions 

of the well-organized and financially stable healthcare sector are considered, with the view that the stable 

health profile of the citizens helps the development of the other sectors, through the workforce, and from 

there the economy as a whole (Petrov 2015). The development is combined with the funding sources listed 

by the Ministry of Health, divided into public and private. Budget financing, expressed through taxes, is 

one of the main public financing methods, followed by social and health insurance, which is the third pillar 

of healthcare financing. Its idea is that the insured bears the incurred payments for health services, through 

the insurance contribution already paid into the system. Another source is private health insurance, which 

on a voluntary basis collects funds from the insured and again covers the incurred health payments for 

health services received. The last group of private sources is the personal funds of citizens or the so-called - 

private payments. A large share is formed by the costs of medicines (Georgieva et. al 2016). The ratio of 

public-private expenses in Bulgaria is extremely unfavorable, and in recent years the share of the latter has 

been between 41 and 48% (Rohova 2016). The donation, which can be implemented in several different 

forms – corporate, institutional, and individual (Ivanova 2018), is an additional considered source. 

Most of the results involving the hospitals in the country reach similar conclusions regarding them and 

the general condition of the sector as a whole. One of the main problems of each individual department 

can be found in the big percentage of private payments. During the considered period of 2020, Bulgarians 

are burdened with more than 40% of the total healthcare costs (Gercheva 2020). An important aspect of 

this type of expenditure is that, in most cases, they are unregulated payments by households (Ministry of 



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110 

 

Finance 2005). This leads to impoverishment of the population after they incur their health care costs 

(IBRD 2015). About 25% of households with the lowest incomes delay visiting a doctor for a health 

problem. The problems arising from the payment of health services by public funds and the transfer of the 

burden directly to households were examined (Rohova 2016). According to a 2010 study, informal 

payments were made by 13% of patients in pre-hospital care and 1/3 of patients in hospitals (Atanasova 

2013).  

Having easier access to the healthcare system is one of the main characteristics of HBMC and can be 

considered as much quicker way of getting help. It has been separately analyzed that 10% of hospital 

admissions could be avoided with better quality care "at the entrance" of the health system (Gercheva 

2020), referring to outpatient care. According to an Analysis of the health system (Dimova et. al. 2019), 

there is a lack of a clear regulatory framework for the formation of prices for health services. Pricing is not 

based on actual costs, but rather on available resources in the NHIF budget. The lack of policies and tools 

for the efficient allocation of public resources has a negative impact on the market behavior of healthcare 

providers. On the other hand, it is determined by the financing methods that stimulate the number of 

services and goods provided, i.e. the utilization of financial resources. 

The majority of published studies examine the health system as a whole, but not separately for each 

type of institution involved in the process of providing health services. The final results are analyzed, such 

as the number of private, municipal, and government hospital based medical care, but not their need and 

distribution by region in the country. Separately, their type is also information that is taken for granted, 

and the need for and dependence on different types of hospitals is not explored. On a global scale, there are 

studies examining the ownership of hospitals as a factor in ensuring the health of the population (Gabriel 

et. al. 2018). The main conclusion of the conducted study is that in the US, non-profit medical institutions 

are more likely to make efforts for the health of the population than public and private ones. There is a 

detailed analysis of the Greek public hospitals that have followed earnings management techniques to 

influence reported earnings and which accrual accounts are appropriate to explain discretionary accruals. 

Covering the period 2009–2019, the analysis reveals that Greek public hospitals are trying to report small 

surpluses. Accrual-based accounts and changes in their value between successive years provide evidence of 

relevance for earnings management (Malkogianni 2022). According to a study in England, their is no 

quality differences between hospitals specializing in planned treatments and other hospitals, nor between 

for-profit and not-for-profit private hospitals. However, a distinction has been made in that private 

hospitals in the country accept for treatment milder cases, as well as patients in need of specialized health 

care. Private hospitals treat patients with fewer comorbidities and past hospitalizations. Controlling for 

observed patient characteristics and treatment type, private hospitals have fewer emergency readmissions 

(Moscelli 2018). In Norway, studies show similar results. The association between quality of care and 

hospital ownership is mixed since private nonprofit hospitals both offer shorter waiting times and shorter 

lengths of stay (Bjorvatn 2018). 

The degree of indebtedness of medical institutions is of essential importance for the health services 

they provide. According to an analysis conducted to investigate whether financial leverage moderates the 

relationship between working capital and profitability for publicly listed European hospitals. The results 

reveal that increasing the length of the cash conversion cycle for hospitals with high financial leverage 

reduces profitability. On the contrary, increasing the length of the cash conversion cycle for those with low 

leverage increases profitability. The findings of this study suggest that leverage influences the relationship 

between the cash conversion cycle and profitability. The results were derived through regression analysis 

(Dalci 2018). 

 

Development of hypotheses and research methods 

Based on the reviewed literature there is a lack of evidence regarding the problems that are been put in 

the next two hypotheses: 

Hypothesis 1: The ownership, location, and specialization of the hospitals lead directly to their overall 

financial stability. The investigated financial indicators - total revenue, revenue from NHIF, revenue from 

private services, working capital, financial result, accumulated profit/loss, equity, and fixed capital are 

related to the three main characteristics. 

Hypothesis 2: Revenue can be taken as the main indicator of the volume of activity and its interactions 

with the rest of the investigated financial indicators. The direction of the relationship with the short-term 

liabilities is been looked into. This finance indicator shows the management of working capital and the 

invested fixed capital. 

The statistical analysis aims to detect a correlation between HBMC performing the same basic activity, 

but distinguished by different characteristics such as location, ownership, and type, according to Law2. The 

 
2 Law on Medical Institutions, Art. 9, Paragraph 2 



Finance, Accounting and Business Analysis 4 (2) 2022 

111 

 

described characteristics of the hospitals are prerequisites for the formation of relationships between the 

individual species, and a statistical approach will be used to check whether they are dependent and indicate 

an influence or insignificant. The data for the subsequent analysis were collected by the Ministry of Health 

(for government-owned hospital based medical care) and the Commercial Register at the Registration 

Agency (for municipal and private hospitals). The sample was made by collecting information from the 

National Health Insurance Fund about the amounts paid to HBMC who performed hospital care services. 

Based on the data for 2021 hospitals are ranked according to income from the NHIF. Through the 

published data from the last census by the National Statistical Institute (NSI) as of 2021, the 5 regional 

centers with the largest number of inhabitants were taken. This approach was chosen because these areas 

would have the most residents who would need hospital care and thus the values in the reports of the 

HBMC covering these locations would be the most significant. According to NSI data, these are the 

districts: Sofia-city, Burgas, Plovdiv, Varna, and Stara Zagora. Based on the information published by the 

NSI and the NHIF, the 3 HBMC with the most payments from the NHIF for each of the 5 regions have 

been selected, given the importance of providing the health needs of the most populated regions with 

hospital medical care. The following elements were investigated taken into consideration their type (see 

Table 1). 

 

Table 1 

Number of monitored district 5 

Number of hospitals from each district 3 pieces 

Number of total monitored hospital based medical care 1*2 15 pieces 

Observation period 2019-2021 3 years 

Total sample size 3*4 45 pieces 

Qualitative variables 4 pieces 

Quantitative variables (additional) 10 pieces 

 

The study aims to draw general conclusions about the type, ownership and the importance of the 

location of hospital based medical care in Bulgaria. At the beginning of the analysis, descriptive statistics 

were used, through which a general view was shown based on the statistical sample. 

Descriptive statistics aims to draw a generalized picture of the movement of the considered indicators 

and the possible dependencies between them. First, the distribution by ownership of the emitted units is 

examined (see Table 2). 

Table 2 

CODE_SOB 

 Frequency Percent Valid Percent Cumulative Percent 

Valid Government _ property 6 40.0 40.0 40.0 

Municipal _ property 2 13.3 13.3 53.3 

Private property 7 46.7 46.7 100.0 

Total 15 100.0 100.0  

 

From the descriptive part, it can be seen that the private hospitals included in the sample are a larger 

percentage than the government and municipal ones. 

By region, they are distributed equally in number, due to the methodology of sample selection. 

Regarding the type of hospitals, according to the classification of the Law on Medical Institutions, the 

distribution is as follows (see Table 3). 

Table 3: It can be seen that the multi-profile hospitals for active treatment - MPHAT prevail, and there 

are two specialized hospitals for active treatment (SBAL). 

The distribution according to the form of management (see Тable 3) under which the hospitals operate 

shows the main form - EOOD - 6 units, followed by OOD - 4 units. Government hospitals mostly operate 

under the legal form of a joint-stock company (AD or EAD). Private and municipal HBMC prefer OOD 

and EOOD. 

  



Finance, Accounting and Business Analysis 4 (2) 2022 

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Table 3 

legal_form 

 Frequency Percent Valid Percent Cumulative Percent 

Valid OOD 2 13 , 3 13 , 3 13 , 3 

EOOD 6 40.0 40.0 53.3 

AD 3 20.0 20.0 73.3 

EAD 4 26.7 26.7 100.0 

Total 15 100.0 100.0  

 

In addition to the distribution of these 3 qualitative characteristics, it was checked how the main 

quantitative characteristics of the sample units were distributed. The first is the value of working capital: It 

is calculated according to the formula 

Working Capital = Current Liabilities – Current Assets (Raikov 2013) 

The results show that 33.3% of the surveyed HBMC operate with negative working capital, of which 

slightly over 60% are public and the rest are private. This may speak of financing fixed assets with short-

term liabilities. 

When reviewing the revenues generated by the hospitals, they are grouped into four groups. The first 

represents the HBMC with annual revenues of less than BGN 50,000K, which have the largest share - 

57.8% of them, 42.3% are private, and 34.6% are government-owned, the remaining 23.1% are municipal. 

The distribution by regions shows that in Sofia-city there are mainly HBMC with an income between 

50,000K and 150,000K BGN. Hospitals with more than 150,000K BGN total revenues are government 

and are in the regions of Varna and Plovdiv. According to their purpose, specialized hospitals are classified 

in the first group - below BGN 50,000K annual turnovers. 

The second indicator examined is the National Health Insurance Fund payments as part of the 

hospital's revenues. Private hospitals have the highest percentage of hospitals receiving income from the 

NHIF up to BGN 100,000K. Government hospitals have an average percentage of up to this amount and 

represent with the most incomes from NHIF- over BGN 100,000K. The distribution by regions shows that 

Sofia-city has mostly high revenue - over BGN 50,000K, in Burgas and Stara Zagora up to BGN 50,000K, 

Varna has representatives in all groups up to BGN 150,000K, Plovdiv hospitals receive most -often 

revenues from the cash register between 50,000K and 100,000K BGN per year. Analogous to the revenues, 

given that the receipts from the NHIF are a part of them, the specialized hospitals are ranked in the group 

up to BGN 50,000K. 

The health services provided, directly to patients, i.e. paid by households or from voluntary health 

insurance funds, mostly go to private hospitals and are distributed relatively evenly across regions. The 

highest percentage is observed in Sofia-city. Over 37% of HBMC with income between BGN 5,000K and 

15,000K are located in this area. 

The indicators of financial result, accumulated loss/profit, equity, and fixed capital will be analyzed in 

parallel, due to their connection. Mainly public hospitals realize a negative financial result - a loss. With 

private ones, this is rather an exception. Mainly, medical institutions report up to BGN 10,000K in profit, 

this is 71.1% of the surveyed hospitals (up to BGN 5,000 K - 48.9%). They operate at a loss mostly in 

Burgas and Varna, and the most stable in their results are those in Plovdiv. Worst case scenario specialized 

medical institutions are ranked with a loss of around BGN 5,000K, on the other hand, the ones that have 

profit are around BGN 5,000K. Private medical institutions most often operate a fixed capital of less than 

100K BGN and own capital up to BGN 50,000K. Even among them, there are those with negative own 

capital and they are a larger percentage than government medical institutions. Also, over 70% of them 

have an accumulated loss of up to BGN 5,000K, and 11.1% even greater. Public hospitals mainly operate 

with fixed capital of up to BGN 20,000K. At government hospitals, also observed representatives in the 

range from BGN 40,000 K to BGN 60,000K - 27.78%. and are mainly in Sofia-city and Varna. Negative 

equity with them is more of a rarity than a trend. Regarding the financial result, however, 22% of 

government hospitals and 66% of municipal in the group of negative values (up to BGN 5,000 K). 72.2% of 

government-owned HBMC have a positive result of up to BGN 10,000K. However, the accumulated losses 

are significant - 88.9% of the government hospitals have values up to BGN 150,000K. The municipal ones 

have mixed results, there are losses and profits of up to BGN 5,000K. The equity capital of the presented 

public hospitals is mainly within BGN 25,000K. 

The distribution by regions shows that the HBMC with the worst financial results are in Varna and the 

best in Sofia-city. In terms of accumulated losses and profits, however, Sofia-city is one of the leading 

regions, together with Stara Zagora and Burgas. 

For a more comprehensive view of the relationships between the quantitative and qualitative traits, an 

analysis of variance was performed. 

 



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3.1 Analysis of variance 

The presence of a relationship between total income and ownership is investigated. The null 

hypothesis (H0) states that there is no relationship between total revenue and hospital ownership. The 

alternative hypothesis (H1) confirms the presence of dependence. 

As shown in the ANOVA table the Significance value is 0.066, while the risk of error  is 0.05 (Table 

4). Therefore, the null hypothesis (H0) can be accepted. The conclusion of the results is as follows: there is 

no statistically significant relationship between the value of total revenues from hospital medical care and 

their ownership. 

Table 4 

ANOVA 

Total Revenue ( Binned ) 

 Sum of Squares df Mean Square F Sig . 

Between Groups 14,300 2 7,150 2,898 ,066 

Within Groups 103,611 42 2,467   

Total 117,911 44    

 

The income from the NHIF is similarly not significantly related to the ownership of the hospitals. Such 

a conclusion is logically laid out following the statement that the mentioned income is part of total 

revenue. 

After proving the missing connection regarding the ownership, it is examined how total income and 

the area in which the hospital is located are related. The null hypothesis (H0) rejects the existence of a 

relationship between total income and location. The alternative hypothesis (H1) confirms such a correlation 

between the two variables. 

The following results are obtained: 

 

Table 5 

ANOVA 

Total revenue 

 Sum of Squares df Mean Square F Sig . 

Between Groups 46501979046,110 4 11625494761.527 7,631 ,000 

Within Groups 60940740234,727 40 1523518505,868   

Total 107442719280.837 44    

 

Table 6 

Descriptives 

Total revenue 

 N Mean 
Std . 

Deviation 
Std . Error 

95% Confidence Interval 

for Mean 
Minimum Maximum 

Lower 

Bound 

Upper 

Bound 

Burgas 9 35617.72 17591.696 5863.899 22095.54 49139.89 17870 75798 

Varna 9 70547.22 52963.979 17654.660 29835.50 111258.94 29987 160769 

Plovdiv 9 100680.06 63368,398 21122.799 51970.80 149389.33 32718 211392 

Sofia city 9 109158.67 20574.086 6858.029 93344.03 124973.31 75740 144525 

Stara 

Zagora 
9 31122.89 8005,926 2668,642 24968.99 37276.79 22915 48415 

Total 45 69425.31 49415.382 7366,410 54579.29 84271.34 17870 211392 

 

From the ANOVA table (Table 5), a value of Sig. is observed <0.05 ( the risk of error). This means 

that the null hypothesis (H0) is rejected and the alternative (H1) is accepted. There is a statistically 

significant relationship between the value of total revenues and the district in which the HBMC is located. 

Values were checked for normality of distribution by the Kolmogorov-Smirnov test. As a result, the 

level of Sig. <0.05, which means that income is not normally distributed. If this condition is not met, non-

parametric Kruskal-Wallis analysis should be applied. It is clear from it that the alternative hypothesis 

should be accepted. Namely that there is a statistically significant relationship between the area and the 

revenue of the HBMC. 

The proven connection between these two indicators is logical, since the distribution of residents, 

respectively those in need of hospital care, depends on the population of the district. From the descriptive 

table (Table 6), through the value of the average values, it can be seen that Sofia-city is in first place with 



Finance, Accounting and Business Analysis 4 (2) 2022 

114 

 

BGN 109,158K, followed by Plovdiv with BGN 100,680K. Stara Zagora district is ranked last with BGN 

31,122K. The values are arranged logically about the population data. 

The next two indicators tested for the presence of a relationship are short-term assets and hospital 

ownership. 

Table 7 

ANOVA 

Short-term liabilities 

 Sum of Squares df Mean Square F Sig . 

Between Groups 1186719904,343 2 593359952,172 3,260 ,048 

Within Groups 7644710758,558 42 182016922,823   

Total 8831430662,902 44    

 

Table 8 

Descriptives 

Short-term liabilities 

 N Mean 
Std . 

Deviation 
Std . Error 

95% Confidence Interval 

for Mean 
Minimum Maximum 

Lower 

Bound 

Upper 

Bound 

Government 

property 
18 21234.61 13562.944 3196,817 14489.92 27979.31 7065 51016 

Municipal property 6 4995.50 2587,701 1056,424 2279.87 7711.13 2210 8629 

Private property 21 17227.05 14973.345 3267,452 10411.26 24042.83 1660 52665 

Total 45 17199.20 14167.369 2111,947 12942.85 21455.55 1660 52665 

 

ANOVA table (Table 7.1), shows a value of Sig. < 0.05, from which the alternative hypothesis (H1) 

should be accepted , i.e. that there is a statistically significant relationship between short-term liabilities 

and the ownership of hospital based medical care. From the conducted One-Sample Test, it can be seen 

that a normal distribution is not present, therefore a non-parametric Kruskal-Wallis analysis was applied. 

It confirms the acceptance of the alternative hypothesis - a statistically significant relationship exists 

between ownership and short-term liabilities of hospitals. From the descriptive characteristics (Table 7.2) 

when applying the dispersion analysis, it can be seen that the government hospitals have the highest share, 

on average BGN 21,234.61K, followed by the private ones with BGN 17,199.20K. 

3.2 Regression analysis 

Through regression analysis, two main variables have been analyzed - whether the income affects the 

value of short-term liabilities and on the other hand whether the value of the invested fixed capital 

indicates a significant change in the generated income. 

Researching the first two variables, the following hypotheses were defined: 

H0: There is no relationship between total revenues and short-term liabilities in hospital based medical 

care. 

H1: There is a relationship between total revenue and short-term liabilities. 

Risk of error: = 0.05 

The results indicate (Tables 8.1 and 8.2) that there is a relationship between the two factors taking in 

mind that the value of Sig. is less than the risk of error . The model explains only 34.3% of the relation 

between the two variables. The strength of the positive association expressed by the correlation coefficient 

R is moderately strong.  

Constructed model can be presented as: Y = 34 303.42 + 2.042 X (Table 8.3) 

It can be concluded that: If total revenues change by 2.042, short-term liabilities will change by 1. 

     Table 9 

Model Summary 

R R Square 

Adjusted R 

Square 

Std . Error of the 

Estimate 

,585 ,343 ,327 40524,291 

 

The independent variable is Short-term liabilities. 



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Table 10 

ANOVA 

 Sum of Squares df Mean Square F Sig . 

Regression 36827339278,694 1 36827339278,694 22,425 ,000 

Residual 70615380002,143 43 1642218139,585   

Total 107442719280.837 44    

 

The independent variable is Short-term liabilities. 

Coefficients 

 

Unstandardized Coefficients 

Standardized 

Coefficients 

t Sig . B Std . Error Beta 

Short-term liabilities 2,042 ,431 ,585 4,736 ,000 

( Constant ) 34303,422 9565.591  3,586 ,001 

 

Researching the second pair of variables – total revenues and fixed capital, the following hypotheses 

were defined: 

H0: There is no statistically significant relationship between total revenues and the value of the fixed 

capital of hospital based medical care. 

H1: There is a relationship between total revenue and fixed capital. 

Risk of error: = 0.05 

From the obtained results it is clear (Tables 9 and 10) that there is a relationship between the two 

indicators, since Sig. < . The coefficient of determination shows that 39.8% of the elements can be 

explained with this model. The strength of the positive association is strong 0.706 shown by the correlation 

coefficient.  

 

Table 11. Model Summary 

R R Square Adjusted R Square Std . Error of the Estimate 

,706 ,498 ,487 35399.140 

. 

Table 12. The independent variable is Fixed capital 

ANOVA 

 Sum of Squares df Mean Square F Sig . 

Regression 53559458196.039 1 53559458196.039 42,742 ,000 

Residual 53883261084.798 43 1253099094.995   

Total 107442719280.837    44    

 

Table 13. The independent variable is Fixed capital 

Coefficients 

 

Unstandardized Coefficients 

Standardized 

Coefficients 

t Sig . B Std . Error Beta 

Fixed capital 1,602 ,245 ,706 6,538 ,000 

( Constant ) 45684.207 6405,763  7,132 ,000 

 

From the performed regression analysis, it is clear that there is a relationship between the total 

revenues and the fixed capital used in HBMC. The proven hypothesis can serve as a basis for analyzing the 

financial indicators of the hospitals in question. Almost 40% of the revenue increase is explained by the 

increase in fixed capital. 

Constructed model can be presented as: Y = 45684.2 + 1.602 X (Table 9.3) 

It can be concluded that: When fixed capital changes by 1, total revenues increase by 1.602. 

The rest of the relationships between the investigated qualitative and quantitative indicators do not 

lead to significant conclusions and will not be described in detail. 

 

4CONTRIBUTES AND FUTURE STUDIES 

 

The study contributes by giving a general idea of how the main financial indicators of HBMC interact 

one by another and on the other hand can be taken as bases for subsequent in-depth analyses. The main 

qualitative characteristics of the studied hospitals were examined - ownership, location, type, and legal 



Finance, Accounting and Business Analysis 4 (2) 2022 

116 

 

form. Preferred legal forms under which HBMC operate have been established, depending on their 

ownership. The information that is generated based on the legal form of the hospitals does not entail 

significant dependencies. The specialization of the hospitals mainly determines the size of the financial 

indicators. Considering that in the sample the municipal hospitals are the only representatives of other 

than multi-specialty hospitals. That’s the reason why significant conclusions cannot be taken into 

consideration. Concerning ownership, through the analyzes carried out, a conclusion can be made that 

there is no statistical correlation between a quality feature and the generated revenues of the HBMC, 

deduced through the applied dispersion analysis. Through the presented descriptive analysis, it is shown 

that public healthcare hospitals (government and municipal) are characterized by worse financial 

indicators than private ones, or in other words, private healthcare hospitals are in a more stable financial 

condition. The regional centers that are the subject of development generate higher revenues, based on the 

larger number of the population in them. 

After the variables have been deduced, using dispersion analysis, the districts were arranged 

analogously to the published data from the NSI for the number of the population in 2021. The studied 

dependence property - short-term liabilities was chosen due to the high percentage of hospitals with 

negative working capital. A statistically significant relationship was found between the two indicators – 

total revenue and short-term liabilities. Moreover, after an analysis of the reviewed financial statements, it 

was concluded that due to the main source of income for public medical institutions - the NHIF (about 

82% of the total income). The delay of cash flow appears as one of the main factors that lead to 

transferring of the payments to short-term liabilities. On the other hand, short-term trade payables have the 

largest share. As a summary, it can be concluded that HBMC uses direct payments for pay rows and tax 

obligations. While all other duties generate high values of short-term liabilities, due to delayed revenues 

from the main source - NHIF. Another explanation for that reason can be the financing of fixed assets by 

hospitals at the expense of "cheap" trade credit from suppliers. 

Through regression analysis, two variables were derived, namely, the increase in short-term liabilities 

by one unit was provoked by the increase in the HBMC's income by 2.042. This leads to the current 

financial management status of the hospitals. The increase of the performed activities and the generation 

of more income inevitably brings indebtedness, and low values of the working capital (while maintaining 

the studied trends). The second dependence draws the possibilities to generate certain incomes, given the 

invested fixed capital. It was concluded that the increase in capital by units will generate prerequisites for 

the increase in income by 1.602. 

In conclusion of all the investigated indicators and variables, although similar in their activities, 

HBMC has its peculiarities. To reach firm conclusions about financial management and opportunities for 

improvement, all of the hospitals should be examined. 

 

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