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2018 | Buch

Service Quality in Indian Hospitals

Perspectives from an Emerging Market

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Über dieses Buch

This book offers an elaborate and empirical look at service quality of hospitals in the emerging market of India. The poor quality of service is a major issue in a large number of hospitals (particularly in government hospitals), which forces patients to opt for private hospitals that are generally much more expensive than government hospitals. This book provides a comprehensive understanding of service quality antecedents in Indian hospitals. It focuses on patient satisfaction and includes valuable insights and implications for hospital management and government. The book is theoretically grounded in SERVQUAL literature and uses appropriate and sophisticated techniques and tools to analyse data. It highlights causal model development with Structural Equation Modelling (SEM) and introduces a classification model, developed using Artificial Neural Networks (ANNs), in order to benchmark specialty cardiac care. The book also deals with Support Vector Machines (SVMs) and compares the error rates between SVM and ANN to find the best classification technique among the two. Overall, this book is a timely and relevant work that contributes to the theory, practice and policy of service quality in hospitals.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
The service industry has always been a topic of interest to researchers due to its multifaceted nature and continuous evolution. In particular, health-care services attract researchers due to rapid growth in the current scenario. Health-care industry is a wide and intensive form of services which are related to the well-being of human beings. Health care is a social sector, and it operated at state level with the help of central government in India. Health-care industry covers hospitals, health insurances, medical software, health equipments and pharmacy. The Indian health-care industry is estimated to reach USD 155 billion in terms of revenues by 2017, according to a study by LSI Financial Services (Web link 1). Over the next 5 years, the size of the health-care industry is expected to almost double driven by rise in per capita spending on health care, change in demographic profile, transition in disease profile, increase in health insurance penetration and fast-growing medical tourism market. The major inputs of health-care industries are as listed below:
I.
Hospitals
 
II.
Medical insurance
 
III.
Medical software
 
IV.
Health equipments
 
Sanjay Mohapatra, K. Ganesh, M. Punniyamoorthy, Rani Susmitha
Chapter 2. Literature Review
Abstract
The literature review has been organized in three stages. The first stage is about the literature of the demographical indices, the second stage is about the literature on the SERVQUAL and the third stage is the literature about the data mining applications in service industries.
Sanjay Mohapatra, K. Ganesh, M. Punniyamoorthy, Rani Susmitha
Chapter 3. Research Gap, Objectives and Scope
Abstract
From Chap. 2 it is evidenced that the piled up literature has some issues resulting in research gaps. So, the issues which require further study are identified from the literature, and the scope of research is elaborated. Based on these identified issues, the objectives have been framed for the research.
Sanjay Mohapatra, K. Ganesh, M. Punniyamoorthy, Rani Susmitha
Chapter 4. Methodology
Abstract
This chapter gives us an understanding of how the data has been collected and used for our study. It provides a brief overview of each of the three techniques for the model considered in measuring patient satisfaction. The three techniques used in this context are structural equational modelling, artificial neural network and support vector machines.
Sanjay Mohapatra, K. Ganesh, M. Punniyamoorthy, Rani Susmitha
Chapter 5. Analysis of Demographical Indices
Abstract
Demographics are the quantifiable statistics of a given population. Demographics are also used to identify the study of quantifiable subsets within a given population which characterize that population at a specific point in time. Demographical indices play a major role in every survey method of data collection procedure. They give us the basic yet broad segmentation of the data. The demographical indices used in this study are gender and age. The overall demographical classification indices are depicted in Tables 5.1.
Sanjay Mohapatra, K. Ganesh, M. Punniyamoorthy, Rani Susmitha
Chapter 6. Developing a Causal Model Using SEM
Abstract
SEM is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions. This definition of SEM was articulated by the geneticist ‘Sewall Wright’ (1921), the economist ‘Trygve Haavelmo’ (1943) and the cognitive scientist ‘Herbert Simon’ (1953) and formally defined by ‘Judea Pearl’ (2000) using a calculus of counterfactuals.
Sanjay Mohapatra, K. Ganesh, M. Punniyamoorthy, Rani Susmitha
Chapter 7. Developing a Classification Model Using ANN
Abstract
An artificial neural network is a mathematical model inspired by biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. In most cases, a neural network is an adaptive system changing its structure during a learning phase. Neural networks are used for determining complex relationships between inputs and outputs or to find patterns in data. The inspiration for neural networks came from examination of the central nervous system. In an artificial neural network, simple artificial nodes, called ‘neurons’, ‘processing elements’ or ‘units’, are connected together to form a network which mimics a biological neural network.
Sanjay Mohapatra, K. Ganesh, M. Punniyamoorthy, Rani Susmitha
Chapter 8. Developing a Classification Model Using SVM
Abstract
Support vector machines have established themselves as a standard data mining and machine learning tool. It is based on advances in statistical learning theory and finds a wide range of application in real-world situations like text categorization, handwritten character recognition, image classification, bio-sequences analysis, etc. The original SVM algorithm was invented by ‘Vladimir N. Vapnik’, and the current standard incarnation (soft margin) was proposed by ‘Vapnik and Corinna Cortes’ in 1995.
Sanjay Mohapatra, K. Ganesh, M. Punniyamoorthy, Rani Susmitha
Chapter 9. Summary and Conclusion
Abstract
In this chapter, we analyse and discuss the results from statistical analysis.
Sanjay Mohapatra, K. Ganesh, M. Punniyamoorthy, Rani Susmitha
Backmatter
Metadaten
Titel
Service Quality in Indian Hospitals
verfasst von
Sanjay Mohapatra
K. Ganesh
M. Punniyamoorthy
Rani Susmitha
Copyright-Jahr
2018
Electronic ISBN
978-3-319-67888-7
Print ISBN
978-3-319-67887-0
DOI
https://doi.org/10.1007/978-3-319-67888-7

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