1. INTRODUCTION:

Whenever a person makes use of a product or services, he/she forms his or her opinion regarding the quality of that product or service. There a host of direct and indirect factors that impact a person’s perception regarding the product or service quality. The person’s willingness to re-use the product or service depends on his/her satisfaction with the product or service quality. Even in case of products, the quality of interaction with the sales staff during the purchase process plays a key role in determining the customers’ satisfaction. In case of services, due to its intangible nature, the quality of experience at the point of service delivery is the most important factor determining the satisfaction of clients and in today’s competitive scenario; no service can exist without having a satisfied customer base.

This is extremely important in case of health care services because everyone considers their health as one of their most valuable wealth. Realizing this, almost all private health care players are striving hard to become a proffered health care destination by offering the best possible services to their clients. But, considering the cost of private health care services, only financially well-off people can have the luxury of utilizing the same. What happens with the health care of the major chunk of our population who are economically backward? No doubt! They are completely dependent on the services offered by public health care system. Now the question is are they really satisfied with the quality of services offered at government hospitals or are they relying on them as they have no other option. It is very important for the authorities to know this because people have the right to quality health care services and authorities cannot afford to compromise on the quality of services provided to them. This paper looks at the satisfaction level of patients with the services offered at government hospitals across Kerala.

2. REVIEW OF LITERATURE:

Satisfaction is a psychological state resulting when the emotion surrounding disconfirmed expectations is coupled with consumer’s prior feelings about the consumption experience (Emadi et al, 2009).

While patient satisfaction has been defined as the degree of congruency between a patient’s expectations of ideal care and his /her perception of the real care he /she receives (Aragon and Gesell, 2003).

Patient is the best judge since he/she accurately assesses and his /her inputs help in the overall improvement of quality health care provision through the rectification of the system weaknesses by the concerned authorities (Baba, 2004).

Patient Satisfaction encompasses every aspect of the of health services, from system approach perspective. People’s use of health services is influenced by arrange of psychological, social, cultural, economic and political forces. Much literature is available about different variables pertinent to the topic such as cost, behavior, competence and communication skills of the care-providers, cleanliness, waiting time, consultation time etc. Cost is the foremost concern of service providers and an important impediment to overcome. Furthermore, other family members accompanying the patient contribute to multiply the costs (Donoghue, 1999).

Providers’ behavior and attitude, especially respect and politeness, was as much important as the technical competence of the provider. Moreover a reduction in waiting time was more important to clients than a prolongation of the quite short consultation time with 75% of clients being satisfied (Guadagnino, 2003).

Patient satisfaction is reportedly a useful measure to provide a direct indicator of quality in healthcare, hence needs to be measured frequently (Farooqi, 2005).

Thus, patient’s satisfaction is an important issue both for evaluation and improvement of healthcare services. User evaluations educate medical staff about their achievements as well as their failure, assisting them to be more responsive to their patients’ needs. Patient’s assessment, therefore, suggests guidelines for improving the attitudes of doctors and other paramedic staff in better serving the patients thereby improving the health services (Al-Qatari and Haran, 2008).

The above literature clearly indicates the importance of understanding and ensuring patient satisfaction with health care service quality.

3. METHODOLOGY:

The researcher adopted a descriptive approach while conducting the study. Data were collected from inpatients at various district and general hospitals across Kerala. A Pre-tested structured questionnaire was administered among a sample of 330 patients from various district hospitals across Kerala selected based on the convenience of the researcher. The questionnaire tried to solicit the opinion of respondents on various aspects of care delivery process like the behaviour of doctors and nurses, administrative effectiveness, facilities at the hospital etc. so as to measure their satisfaction with the quality of care provided at these hospitals as well as to identify scope for improvement.

Factor analysis tries to bring inter-correlated variables together under more general, underlying variables. More specifically, the goal of factor analysis is to reduce “the dimensionality of the original space and to give an interpretation to the new space, spanned by a lower number of new dimensions which are supposed to underlie the old ones” or to explain the variance in the observed variables in terms of underlying latent factors (Rietveld and Van Hout, 1993). In the present paper, factor analysis was used to analyze the key variables influencing the satisfaction level of patients with the services rendered at government hospitals. These variables were reduced into certain factors based on common properties.

Multiple regression is a statistical technique that allows us to predict the value of one variable on the basis of values of several other variables. There will be two set of variables – predictor variables which are helpful in predicting the values of other variables and the criterion variables for which the values are predicted based on the values of predictor variables. This statistical technique can be used while exploring linear relationships between the predictor and criterion variables. Multiple regression analysis helps us to understand the significance level of different dependent variables in relation to one or more independent variables also to identify the most significant factor(s) (Brace et al, 2006). In the present study, multiple regression was performed to identify the most important factors impacting the satisfaction level of patients as far as gender of respondents was concerned.

4. RESULTS AND DISCUSSION:

Table 1: Patients: Overall service quality: KMO and Bartlett's test

Variables	Initial	Extraction
Behaviour of doctors.	1.000	.725
Knowledge and proficiency of doctors.	1.000	.487
Duty consciousness of doctors.	1.000	.537
Sincerity of doctors.	1.000	.540
Help of support staff to doctors.	1.000	.582
Team spirit of doctors.	1.000	.492
Facilities at the hospital.	1.000	.667
Administration of the ward/unit.	1.000	.623
Follow up system.	1.000	.609
Communication between doctors.	1.000	.561
Behaviour of nurses.	1.000	.469
Behaviour of support staff.	1.000	.461

Source: Survey Data

The KMO test is conducted to assess the adequacy of a given sample. KMO value varies between 0 and 1. A value of 0 indicates that factor analysis is inappropriate for the data and a value of 1 indicates that factor analysis will yield distinct and reliable results. A value of 0.5 or above means that the sample is adequate and we can proceed with factor analysis whereas if it is below 0.5 we have to collect more data (Field, 2000). As seen in Table 1 the KMO value for this set of data is 0.869 which indicates that the data is adequate and we can proceed with factor analysis.

For factor analysis to work there has to be some kind of relationship between the variables and this is tested using the Bartlett’s Test of sphericity. This test indicates whether factor analysis is appropriate for a given set of data. Factor analysis can be considered appropriate for a data only if the significance value is less than 0.05 (Field, 2000). As the significance value for the present data as shown in Table 1 is 0.000, factor analysis is appropriate for this data.

As the present data set satisfies both KMO test and Bartlett’s test, factor analysis is appropriate.

Table 2: Patients: Overall service quality: Communalities

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.869
Bartlett's Test of Sphericity	Approx. Chi-Square	1.550E3
	Df	66
	Sig.	.000

Extraction Method: Principal Component Analysis' Source: Survey Data

Table 2 showed the communalities before and after extraction. Principal component analysis works on the assumption that all variance is common. So before extraction all communalities are 1. Column two, i.e., the extraction column indicates the percent of common variance associated with each variable. Hence from Table 2, we can say that 72.5 percent of variance associated with the variable ‘Behaviour of doctors’ is common, 48.7 percent of variance associated with the variable ‘Knowledge and proficiency of doctors’ is common and so on. The table clearly shows the percent of common variance associated with each variable. The highest degree of common variance was in the case of ‘Behaviour of doctors’ and the lowest common variance was in case of ‘Behaviour of support staff’.

Table 3: Patients: Overall service quality: Total variance explained

Components	Initial Eigen values			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
Components	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	4.890	40.749	40.749	4.890	40.749	40.749	3.841	32.010	32.010
2	1.863	15.523	56.272	1.863	15.523	56.272	2.911	24.261	56.272
3	.875	7.289	63.560
4	.712	5.931	69.491
5	.640	5.335	74.826
6	.591	4.929	79.755
7	.516	4.298	84.053
8	.487	4.055	88.108
9	.415	3.455	91.563
10	.398	3.317	94.879
11	.321	2.678	97.558
12	.293	2.442	100.000

Extraction Method: Principal Component Analysis, Source: Survey Data

Table 3 lists out the eigenvalues with respect to each factor before extraction, after extraction and after rotation. Before extraction there were twelve eigenvalues as there were twelve variables included in the analysis. The eigenvalues associated with each factor shows the variance associated with each factor. It also shows eigenvalues in terms of percent of variance. For e.g. the first factor, i.e., ‘Behaviour of doctors’ explains 40.75 percent of variance. It is clear from Table 3 that the first few factors explains relatively larger amount of variations in comparison to the later ones. SPSS then takes out those factors with eigenvalues greater than 1, which leaves us with 2 factors which are shown in the second part of Table 3 labeled as ‘Extraction Sums of Squared Loadings.’ The values in this part of the table are same as the values before extraction except that the values for factors other than those with eigenvalues greater than 1 are ignored. The last part of the table, i.e., ‘Rotation Sums of Squared Loadings’, displays the eigenvalues of factors after rotation. Rotation more or less optimises the factor structure leading to equalisation of importance of all factors. Before rotation the first factor accounted for 40.75 percent of variance while the second factor contributed to 15.52 percent of variance whereas after rotation both the factors contributed more or less equally thereby optimising the importance of all factors.

Table 4: Patients: Overall Service Quality: Rotated component matrix

Variables	Component
	1	2
Behaviour of doctors.	.849
Help of support staff to doctors.	.759
Behaviour of nurses.	.677
Duty consciousness of doctors.	.674
Behaviour of support staff.	.665
Sincerity of doctors.	.649
Knowledge and proficiency of doctors.	.611
Facilities at the hospital.		.807
Administration of the ward/unit.		.776
Follow up system.		.748
Communication between doctors.		.669
Team spirit of doctors.	.443	.544

Etraction Method: Principal Component Analysis

Rotation Method: Varimax with Kaiser Normalization

Rotation converged in 3 iterations.

Source: Survey Data

Table 4 showed the rotated component matrix which is the matrix of factor loadings for each factor into each variable. 0.4 was used as the cut-off for factor loading. The factors converged at 3 iterations. The variables were listed in the descending order of size of their factor. As evident from Table 4, factor rotation resulted in the extraction of 2 factors as significant determinants of patients’ perception regarding overall satisfaction with service quality at government hospitals. Factor 1 loaded across seven variables, i.e., ‘Behaviour of doctors’, ‘Help of support staff to doctors’, ‘Behavior of nurses’, ‘Duty consciousness of doctors’, ‘Behavior of support staff’, ‘Sincerity of doctors’ and ‘Knowledge and proficiency of doctors’ which will jointly be termed as ‘Knowledge, sincerity and behavior of doctors, nurses and support staff’. Second factor loaded across five variables namely ‘Facilities at the hospital’, ‘Administration of the ward/unit’, ‘Follow up system’, ‘Communication between doctors’ and ‘Team spirit of doctors’ which will hereafter be referred to as ‘Facilities and administrative effectiveness at the hospital as well as communication and team spirit of doctors’.

Hence the twelve variables included in the analysis converged to two factors namely ‘Knowledge, sincerity and behavior of doctors, nurses and support staff’ and ‘Facilities and administrative effectiveness at the hospital as well as communication and team spirit of doctors.’

The factor scores were then subjected to regression analysis. Regression analysis was performed at 5 percent significance level by taking gender of the respondents as dependent factor to test the following hypotheses.

H1: There is no significant difference in the perception of male and female patients regarding the knowledge, sincerity and behaviour of doctors, nurses and support staff.

H2: There is no significant difference in the perception of patients regarding facilities and administrative effectiveness at the hospital as well as communication and team spirit of doctors.

From regression results (Table 5) it was concluded that the second factor that emerged after principal component analysis was found to be insignificant (P>0.05) as far as gender of respondents was considered. Hence, it was concluded that there was no significant difference in the perception of male and female respondents regarding facilities and administrative effectiveness at the hospital as well as communication and team spirit of doctors and thus H2 was accepted.

However the first factor that emerged after principal component analysis, i.e.; ‘Knowledge, sincerity and behavior of doctors, nurses and support staff’ was found to be significant (p=0.021 ;< .05) at 5 percent significance level as far as gender of respondents was considered. Hence, H1 was rejected and it was concluded that there was significant difference in the opinion of respondents regarding knowledge, sincerity and behavior of doctors, nurses and support staff as far as gender of respondents was considered.

5. CONCLUSION AND LIMITATIONS OF THE STUDY:

The above discussion clearly brings out the fact that ‘Knowledge, sincerity and behavior of doctors, nurses and support staff’ and ‘Facilities and administrative effectiveness at the hospital as well as communication and team spirit of doctors’ were the two factors that most crucially impacted the satisfaction of patients with the quality of services offered at government hospitals. While there was no significant difference in the perception of male and female patients regarding the facilities, administrative effectiveness, communication effectiveness and team spirit of doctors, there existed variations in the perception of male and female patients regarding knowledge, sincerity and behavior of doctors, nurses and support staff. Hence, the authorities concerned should pay attention to these aspects in order to ensure satisfaction of patients with service quality at government hospitals. However, as the findings of the study are purely based on the inputs received from the surveyed patients, proper care should be taken before generalizing the same.

6. REFERENCES:

1. Al-Qatari, G. M., and Haran, D. (2008). Determinants of satisfaction with primary health care settings and services among patients visiting primary health care centres in Qateef, Eastern Saudi Arabia. Middle East Journal of Family Medicine, 6(7), 3-7.

2. Aragon, S. J., and Gesell, S. B. (2003). A patient satisfaction theory and its robustness across gender in emergency departments: a multigroup structural equation modeling investigation. American Journal of Medical Quality, 18(6), 229-241.

3. Baba, I. (2004). Experiences in quality assurance at bawku hospital eye department, Ghana. Community Eye Health, 17(50), 31.

4. Brace, N., Kemp, R., and Snelgar, R. S. (2006). SPSS for psychologists: a guide to data analysis using SPSS for Windows (versions 12 and 13). Palgrave Macmillan.

5. Donoghue, M. (1999). People who don't use eye services:‘making the invisible visible’. Community Eye Health, 12(31), 36.

6. Emadi, N. A., Falamarzi, S., Al-Kuwari, M. G., and Al-Ansari, A. (2009). Patients' satisfaction with primary health care services in Qatar. MEJFM, 7(9), 4-9.

7. Farooqi, J. H. (2005). Patient expectation of general practitioner care, focus group discussion and questionnaire survey in an urban primary health centre, Abu Dhabi-UAE (A Pilot Study). Middle East J Fam Med, 3(3), 6-9.

8. Field, A. (2000). Discovering statistics using SPSS for Windows: Advanced techniques for beginners (Introducing Statistical Methods series).

9. Guadagnino, C. (2003). Role of patient satisfaction. Physician’s News Digest. Retrieved on December, 25(2015), 1-12.

10. Van Hout, R., and Rietveld, T. (1993). Statistical Techniques for the Study of Language and Language Behaviour. Berlin and New York: Mouton de Gruyter.

Received on 23.03.2016 Modified on 05.04.2016

Asian J. Nur. Edu. and Research. 2016; 6(3): 381-385

DOI: 10.5958/2349-2996.2016.00071.9

Model		Unstandardised Coefficients		Standardised Coefficients	t	Sig.
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	1.476	.027		53.934	.000
	Knowledge, sincerity and behaviour of doctors, nurses and support staff	-.063	.027	-.127	-2.313	.021
	Facilities and administrative effectiveness at the hospital as well as communication and team spirit of doctors	.024	.027	.049	.887	.376