Role of Empathy in Ensuring
Patient Satisfaction at Government Hospitals in Kerala: An Investigation
Rehin K. R. 1*, Dr. P. T. Raveendran2
1Research Scholar, Department of Management
Studies, Kannur University, Kerala India
2Professor, Department of Management
Studies, Kannur University, Kerala India
*Corresponding Author E-mail: rehinkr@gmail.com
ABSTRACT:
Human beings always look for concern and consideration
from others. The desire for empathy persists in every individual throughout
his/her life. This need for empathy intensifies at times when a person is
facing some difficulty. Whenever a person is having any problem, he/she expects
people to empathize with him. This is very much true in case of patients.
Whenever a person visits a hospital with any kind of illness, he/she expects
the doctors, nurses and even the support staff to show care and concern towards
him/her or rather empathize with him/her. Even a slightest feeling of lack of
empathy may cause great dissatisfaction to patients. So the present paper tries
to identify the most crucial factors impacting the satisfaction of patients
with the empathy of staff members at government hospitals in Kerala. Factor
analysis of the data collected from 240 patients across various government
hospitals, through field survey, indicated that ‘patience, care and concern of
doctors and nurses’ and ‘doctors’ interest in patients and the comfort provided
by them were the vital factors impacting patients’ satisfaction with empathy of
staff members. Regression analysis indicated that there existed difference in
perception of male and female patients regarding these factors.
KEY WORDS: Comfort, concern, empathy, patience.
Even for the slightest issues, it is human tendency to complain
that others are not empathizing with them. People usually expect empathy from
all possible sources like from parents, friends and relatives, teachers,
colleagues etc. In short we can say that empathy is a feeling that is most
desired for by every individual. The case of patients is also the same. As
patients generally come to a hospital with an unstable physical and mental
condition, empathy is the most important element that they look for. The
concern of nurses and doctors towards patients is one of the prime determinants
of patient satisfaction.1 They expect the doctors, nurses as well as
support staff to empathize with them throughout the course of treatment. The
perception of patients’ regarding the degree of empathy shown towards them by
the staff members, including doctors, at a hospital generally influences their
satisfaction with the care delivery process. As such, it is very important to
have a clear idea about the vital factors influencing patients’ perception
about the empathy of staff members at hospitals in order to improve their
satisfaction with the extent of empathy shown to them and thereby to improve
their overall satisfaction. Hence, the present aims at identifying the crucial
factors impacting the perception of patients with regard to the extent of empathy
at government hospitals in Kerala.
REVIEW OF LITERATURE:
Empathy is defined as a primarily cognitive (as opposed to an
affective) trait that involves appreciating (rather than feeling) of a
patient’s concerns, experiences, pain, and suffering combined with a capacity
to communicate this understanding and a willingness to help.2 This
definition makes a clear differentiation between empathy (a cognitive
attribute) and sympathy (an affective response). Such
a distinction is imperative in the case of patient care because an
overabundance of sympathy, due to its affective nature, can be unfavourable to patients as well as physicians. However,
empathy, because of its cognitive character, even in excess, is always
beneficial to patient care .3
Empathic engagement in patient care results in improved patient
care outcomes. Various studies confirmed the association between higher levels
of physician empathy and greater disease control.4
At the psychosocial level, empathic engagement lays the base for a
trusting relationship. Constraints in communication will reduce when a trusting
relationship is formed. In the security of a trusting relationship, the patient
feels free to explain his or her illness and associated without concealment.
This in turn leads to a more precise diagnosis and greater conformity, which
ultimately will result in better quality care.5
The above literature clearly signifies the role of empathy in
enhancing quality of care at hospitals. Hence, the present paper attempts to
assess the perception of patients about empathy of staff members at government
hospitals in Kerala.
SIGNIFICANCE OF THE STUDY:
In case of hospitals the need to provide personalized attention to
patients is even more important.
Whenever a patient comes to a hospital, the patient as well as his or
relatives will obviously be tensed and anxious and they expects the doctors as
well as the nurses and support staff to be equally anxious about the health
condition of the patients. In short the patients and the bystanders expect the
doctors, nurses and support staff to empathize with them. If they feel that the
doctors, nurses and support staff are not anxious about the health condition of
the patients, they may feel dissatisfied and may look for other alternatives.
As such, it is very important for hospitals to create a feeling in patients
that the doctors, nurses and support staff are genuinely interested in
improving the health of the patients in order to have loyal set of patients
which is possible only by providing individualized care and attention to
patients. As such, it is very important to identify the key factors influencing
the perception of patients regarding the extent personalized services provided
by doctors, nurses and support staff. Hence, this paper attempts to find out
the key factors impacting the perception of patients regarding the empathy
shown by doctors, nurses and support staff.
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 240 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 relating to empathy of
staff members like the behaviour of doctors and nurses, care and concern shown
by doctors, nurses and support staff so as to measure their satisfaction 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.6 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) 7. In the present study, multiple regression was performed
to find out whether there existed significant difference in the in the
perception regarding factors effecting empathy of staff members as far as
gender of respondents was concerned.
RESULTS AND DISCUSSION:
Table 1. KMO and Bartlett's Test
|
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. |
660 |
|
|
Bartlett's Test of Sphericity |
Approx. Chi-Square |
180.746 |
|
Df |
10 |
|
|
Sig. |
000 |
|
Source: Survey Data
Table 2. Communalities
|
|
Initial |
Extraction |
|
Doctor's Interest in Patients'
as a Person |
1.000 |
.667 |
|
Doctors Made Patients Feel
Comfortable |
1.000 |
.734 |
|
Doctors were Patient |
1.000 |
.573 |
|
Caring Attitude of Doctors |
1.000 |
.487 |
|
Nurses Showed Concern for the
Worries and Anxieties of Patients |
1.000 |
.528 |
Extraction Method: Principal Component Analysis
Source: Survey Data
Table 3 Total Variance Explained
|
Component |
Initial Eigenvalues |
Extraction Sums of Squared Loadings |
Rotation Sums of Squared Loadings |
||||||
|
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
|
|
1 |
1.968 |
39.364 |
39.364 |
1.968 |
39.364 |
39.364 |
1.520 |
30.403 |
30.403 |
|
2 |
1.020 |
20.406 |
59.770 |
1.020 |
20.406 |
59.770 |
1.468 |
29.367 |
59.770 |
|
3 |
.833 |
16.668 |
76.438 |
|
|
|
|
|
|
|
4 |
.648 |
12.960 |
89.398 |
|
|
|
|
|
|
|
5 |
.530 |
10.602 |
100.000 |
|
|
|
|
|
|
Extraction Method: Principal Component Analysis.
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 bellow 0.5 we have to collect more data8. As seen
in Table 1 the KMO value for this set of data is 0.66 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
0.058. 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 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 percentage of common variance associated with
each variable. Hence from Table 2, we can say that 66.7 percentage of variance
associated with the variable ‘Doctor's interest in patients' as a person’ is
common, 73.4 percentage of variance associated with the variable ‘Doctors
Made Patients Feel Comfortable’ is common and so on. The table clearly
shows the percentage of common variance associated with each variable. The
highest degree of common variance was in the case of ‘Doctors Made Patients
Feel Comfortable’ and the lowest common variance was in case of ‘Caring
Attitude of Doctors’.
Table 3 lists out the eigenvalues with
respect to each factor before extraction, after extraction and after rotation.
Before extraction there were five eigenvalues as
there were five 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 percentage of
variance. For e.g. the first factor, i.e., ‘Doctor's Interest in Patients'
as a Person’ explains 39.36 percentage 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 labelled 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 optimizes the factor structure
leading to equalization of importance of all factors. Before rotation the first
factor accounted for 39.36 percentage of variance while the second factor
contributed to 20.4 percentage of variance
whereas after rotation both the factors contributed more or less equally
thereby optimizing the importance of all factors.
Table 4 Rotated Component Matrix
|
|
Component |
|
|
|
1 |
2 |
|
Doctors were Patient. |
0.733 |
|
|
Nurses Showed Concern for the
Worries and Anxieties of Patients |
0.720 |
|
|
Caring Attitude of Doctors |
0.606 |
|
|
Doctors Made Patients Feel
Comfortable |
|
0.855 |
|
Doctor's Interest in Patients'
as a Person |
|
0.756 |
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser
Normalization
Rotation converged in 3 iterations
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 empathy of doctors, nurses and
support staff at government hospitals. Factor 1 loaded across three variables,
i.e., ‘Doctors were Patient’, ‘Nurses Showed Concern for the Worries and
Anxieties of Patients’ and ‘Caring
Attitude of Doctors’ which will jointly be termed as ‘Patience, Care and Concern of Doctors and
Nurses’. Second factor loaded across two variables namely ‘Doctors Made Patients Feel Comfortable’
and ‘Doctor's Interest in Patients' as
a Person’ which will hereafter be referred to as ‘Doctors’ Interest in Patients and the
Comfort provided by them’.
Table.5: Regression Coefficients
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
1.476 |
0.027 |
|
54.211 |
0.000 |
|
Patience, Care and Concern of
Doctors and Nurses |
-0.054 |
0.027 |
-0.108 |
-1.987 |
0.048 |
|
|
Doctors’ Interest in Patients
and the Comfort provided by them |
-0.065 |
0.027 |
-0.129 |
-2.368 |
0.018 |
|
Dependent Variable: Gender
Source: Survey Data
Hence the five variables included in the analysis converged to two
factors namely ‘Patience Care and Concern of Doctors and Nurses’
and ‘Doctors’ Interest in Patients and the Comfort provided by them’.
To further refine the results, the factor scores were subjected to
regression analysis at 5 percent significance level by taking gender of the
respondents as dependent factor to test the following hypothesis.
H1: There is no significant difference in the perception of male
and female respondents regarding the patience, care and concern of doctors and
nurses at government hospitals in Kerala.
H2: There is no significant difference in the perception of respondents
regarding doctors’ interest in patients and the comfort provided by them across
gender.
From regression results (Table.5) it was concluded that the both
the factors that emerged after principal component analysis were found to be
significant (P = 0.048 and 0.018; < 0.05) as far as gender of respondents
was considered. Hence H1 and H2 were rejected and it was concluded that there
was significant difference in the perception of respondents regarding
‘Patience, Care and Concern of Doctors and Nurses’ and ‘Doctors’ Interest in
Patients and the Comfort provided by them’ as far as gender of respondents was
considered.
CONCLUSIONS AND LIMITATIONS OF THE STUDY:
From the above discussion, we can conclude that ‘Patience, Care
and Concern of Doctors and Nurses’ and ‘Doctors’ Interest in Patients and the
Comfort provided by them’ were the two most prominent factors impacting the
satisfaction of patients with the empathy of staff members at government
hospitals in Kerala. We can also conclude that there existed significant
difference in the perception of male and female patients regarding these
factors. As such, the authorities concerned should try to ensure that doctors
and nurses show interest and concern for the patients and also make them feel
comfortable at the hospitals in order to enhance their satisfaction. However,
as the conclusions of the study are completely based on the data provided by
the patients surveyed, there is always a possibility of the conclusions getting
influenced by biasness of inputs provided by the patients. Hence, the findings
of the study should be generalized with caution. However, there is always a
possibility of conducting similar studies in private and co-operative sectors
in Kerala as well as of extending the study to other states or to the nation as
a whole.
REFERENCES:
1. Rehin KR and Raveendran
PT. Antecedents of patients’ satisfaction at government
hospitals in Kerala: an exploration. Commerce
Spectrum, 1(1); 2013:73-82
2. Del
Canale, et al. The relationship between physician
empathy and disease complications: an empirical study of primary care
physicians and their diabetic patients in parma,
Italy. Academic Medicine, 87(9);
2012: 1243-1249.
3. Hojat, et al. Empathic and sympathetic orientations toward
patient care: conceptualization, measurement, and psychometrics. Academic Medicine, 86(8); 2011:
989-995.
4. Hojat, et al. Editorial: Empathy and Health Care
Quality. American Journal of
Medical Quality, 28(1); 2013:
6-7.
5. Heinrichs M and Domes G. Neuropeptides
and social behaviour: effects of oxytocin and
vasopressin in humans. Progress in
Brain Research, 170; 2008: 337-350.
6. Rietveld T and Van Hout
R. Statistical techniques for the study of language behaviour. Berlijn, Mouton de Gruyter. 1993.
7. Brace,
et al. SPSS for Psychologists. Hampshire, England. 2003
8. Field
A. Discovering statistics using SPSS for Windows: Advanced techniques for
beginners (Introducing Statistical Methods series). 2005.
Received on 19.10.2013 Modified on 01.11.2013
Accepted on 05.11.2013 © A&V Publication all right reserved
Asian J. Management 5(1):
January–March, 2014 page 45-48