A Framework for Ranking Hospitals Based on Customer Perception Using Rough Set and Soft Set Techniques

A Framework for Ranking Hospitals Based on Customer Perception Using Rough Set and Soft Set Techniques

Arati Mohapatro, S.K. Mahendran, T. K. Das
DOI: 10.4018/IJHISI.2020010103
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Abstract

Hospital ranking is a cumbersome task, as it involves dealing with a large volume of underlying data. Rankings are usually accomplished by comparing different dimensions of quality and services. Even the quality care measurement of a hospital is multi-dimensional: It includes the experience of both clinical care and patient care. In this research, however, the authors focus on ratings based only on customer perception. A framework which consists of two stages—Stage I and Stage II—is designed. In the first stage, the model uses a rough set in a fuzzy approximation space (RSFAS) technique to classify the data; whereas in the second stage, a fuzzy soft set (FSS) technique is employed to generate the rating score. The model is employed for comparing USA hospitals by region using annual HCAHPS survey data. This article shows how ranking of the healthcare institutions can be carried out using the RSFAS (rough set in a fuzzy approximation space) and fuzzy soft set techniques.
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1. Introduction

The choice of hospital is very critical for a family—particularly if any family members are facing a serious or complex health problem. To select a best choice from among an array of options is an arduous task. This process becomes even more strenuous when the evaluation criteria are vague or qualitative and when the objectives vary in importance and scope. Additionally, the types of healthcare providers, their functionality, the specialists involved, and the facilities provided are distinct. Hence, there must be a decision tool which would augment the task of searching hospitals when needed. In hospital ranking, healthcare providers and medical centres are assessed by speciality—i.e., cardiology, cancer, ENT, urology, diabetes, neurology, pulmonology, nephrology, gynaecology, orthopaedic, ophthalmology, gastroenterology, etc. Besides calculating which hospitals provide the finest care for the most serious or complicated medical conditions, there is a need for focusing on those hospitals with a perfect record of common care (which is defined as care involving relatively commonplace conditions and procedures).

It is clear from various readings that better service quality boosts customer satisfaction (Radwin, 2000; Gremler, Gwinner, & Brown, 2001; Kumar, Smart, Maddern, & Maull, 2008). The impact of service quality on customer satisfaction has been extensively discussed by many authors (Lee, 2012; Bohm, 2013; Chia-Wen, Ting-Hsiang, & Woodside, 2013; Prabhakar, 2014). Perceived quality of service has a direct influence on satisfaction (Lee, Lee, & Yoo, 2000; Ladhari, 2009).

Today’s patients are taking active role in selecting healthcare providers. Accurate ratings of hospitals are essential—as such measures regard health and well-being. However, there is no agreement between the reports of leading healthcare rating agencies. These agencies agree neither on the top-ranking hospital nor on the bottom-ranking hospital (Rothberg, Morsi, Benjamin, Pekow, & Lindenauer, 2008). This is due to variations in methods used by the rating systems. Austin et al. (2015) compare the reports of four national rating systems. The finding is that there is a lack of agreement among their ratings. This is because each system has its own rating method; and each system has a different measure of outcomes.

A study by Beukers, Kemp, and Varkevissar (2014) revealed that in a setting where prices do not matter for patients due to health insurance coverage, travel time is most significantly impactful when choosing a hospital, followed by the hospital’s quality ratings and wait time. Studies on the ranking of hospitals by evaluating the quality of service in those hospitals have been done in different countries. A few of them are listed below:

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