ABSTRACT
During this pandemic outbreak of COVID-19, the whole world is getting severely affected in respect of population health and economy. This novel virus has brought the whole world including the most developed countries to a standstill in a very short span like never before. The prime reason for this unexpected outburst of COVID-19 is lack of effective medicine and lack of proper understanding of the influencing factors. Here, the authors aim to find the effect of epidemiological factors that influence its spread using a fuzzy approach. For the same, a total of nine factors have been considered which are classified into risk and preventive factors. This fuzzy model supports to understand and evaluate the impact of these factors on the spread of COVID-19. Also, the model establishes a basis for understanding the effect of risk factors on preventive factors and vice versa. It is worth mentioning that this is the first attempt to analyze the effect of clinical and epidemiological factors with respect to COVID-19 using a fuzzy approach.
ABSTRACT
Purpose Hospital service quality is important amidst the raise of healthcare consumerism. It is of prime concern in telemedicine, medical tourism and appointment booking. More over a good hospital service quality enhances the brand image of the hospital by which the hospital is able to attract more patients and in turn lead to increase hospital revenues. Healthcare consumerism offers more choices in healthcare to patients and helps patients to make informed choices. The digital world helps healthcare consumerism by allowing patients to research health issues online, share information and participate in their healthcare decisions. Healthcare information technology is also enhancing the growth of healthcare consumerism for it arranges the platform for information exchange. The two broad categories of hospitals in India are public and private hospitals. They differ in service quality perception of patients, hospital infrastructure and the socio economic profile of the patients. This study aims to arrive at the relationship of hospital service quality and healthcare consumerism in both public and private hospitals. Design/methodology/approach A structured questionnaire is administered to out-patients for collecting data. Then using exploratory factor analysis the constructs are formed and using regression analysis the relationship is established. Findings The study concludes that healthcare consumerism has a significant relationship with hospital service quality for both public and private hospitals. The coefficient of regression line equation in public hospital for healthcare consumerism is negative and in private hospital it is positive Originality The data collection for the study is done in the beginning of COVID 19 pandemic. This study brings out the role of healthcare consumerism on hospital service quality in India. Research limitations/implications This study is limited to out-patients in India. Only regression analysis is used in this study to establish the relationship between healthcare consumerism and healthcare service quality. Practical implications Academicians and people in healthcare industry should keep paying attention to rise in healthcare consumerism and its influence on healthcare service quality in public and private hospitals when formulating strategies.
ABSTRACT
The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which was later declared as a global pandemic in view of its exponential rise and spread around the world. Resultantly, the scientific and medical research communities around the globe geared up to curb its spread. In this manuscript, authors claim competence of AI-mediated methods to predict mortality rate. Efficient prediction model enables healthcare professionals to be well prepared to handle this unpredictable situation. The prime focus of the study is to investigate efficient prediction model. In order to determine the most effective prediction model, authors perform comparative analysis of numerous models. The performance of various prediction models is compared using various error metrics viz. Root mean square error, mean absolute error, mean square error and R2. During comparative analysis, Auto seasonal auto regressive integrated moving average model proves its competence over comparative models. © 2021, Korean Spatial Information Society.