ABSTRACT
OBJECTIVES: The recent Covid-19 pandemic has burdened the healthcare facilities, especially in the presence of limited infrastructure. We aimed at applying a queuing model to the Covid-19 screening area so as to optimize the screening services and ensuring that no patient is refused the service. METHODS: The mean arrival time of patients, number of physicians, mean screening time and queue characteristics were observed and entered in the M/M/c/K queuing model using R programming to optimize the number of physicians required in the screening area. RESULTS: Considering the mean arrival of 7 patients in 10 minutes and screening of 3 patients in 10 minutes by 1 physician, 2 physicians were assigned. At this capacity, the probability of saturation of the system was 15% with patient loss rate of 1.05 per 10 minutes. Queuing simulation with 3 physicians reduced the patient loss rate to 0.013 per 10 minutes and a saturation probability of 0.2%. However, an increase of arrival rate from 10 to 20 led to an early saturation of the system. CONCLUSION: Queuing models offer an opportunity for the healthcare providers and hospital administrators to optimize patient care services, especially in critical areas with an ever-changing situation such as the current pandemic.
ABSTRACT
Purpose>This paper attempts to provide significant information on the increased growth of literature of chatbots and virtual assistants. Technological changes are dynamic and keep changing at regular intervals. Therefore, it becomes highly crucial to review the performance of chatbots and virtual assistants. This paper aims to review the literature by eminent researchers in the form of authors, keywords and major contributing organizations.Design/methodology/approach>Systematic literature review and bibliometric analysis is used to analyze the growth of literature on chatbots. A sum of articles has been extracted from Scopus database with selected keywords and with certain filters. VOSviewer software is used for analysis of data. A total of 130 documents are extracted from Scopus database from 2017 to 2021 (31st August).Findings>This study provides a significant contribution to an existing literature and provides the understanding of research in the area of chatbots and virtual assistants. The authors with maximum number of citations are Yan, Zaho, Bengio, Weizenbaum, Song, Zhou and Maedche with jointly 180 citations. Research is been contributed by different countries where the United States is the country with highest number of documents published. The United States contributes 17% of the total production in the area of chatbots and virtual assistants. The analysis shows that the area is gaining momentum as contribution in this area is been increasing in last few years.Research limitations/implications>The study shows that several branches of chatbots are also in mainstream like natural language processing, e-learning, behavioural research, conversational agents, virtual assistants, human–computer interaction, natural language and so on. It provides a wider scope to authors and researchers to gain useful insights. The bibliometric study will provide a broader spectrum in this area.Social implications>The developments in technology and also the effect of COVID-19 pandemic is boosting the adoption of technology in different sectors. Deployment of technology will uplift the economy and social infrastructure. Society can avail different services from their comfort area and also in real time. This will help in reducing wastage of resources like people visiting offices for routine jobs which can be easily availed from their workplace. Society may access better services without much human interaction.Originality/value>This paper adds significant contribution to the existing literature by analysing the published papers from Scopus database. The study contains new and significant information as this study covers all industries where chatbots and virtual assistants are being applied whereas in previous literature only specific industry has been taken into consideration.
ABSTRACT
BACKGROUND: In the wake of the novel coronavirus pandemic, the closure of educational institutions has imposed a situation of potential gap in learning. Since training of medical and paramedical students is vital in creation of our army of these frontline health-care workers, this study was aimed at comparing the asynchronous and synchronous methods of online teaching for imparting training to students of medical laboratory technology (MLT). MATERIALS AND METHODS: This cross-sectional study included 33 students of the 1st-year batch of MLT course at a tertiary level hospital. Ten lectures each from the subject of biomedical statistics were delivered through asynchronous (lecture shared on WhatsApp group) and synchronous (online live lecture) methods followed by a brief examination for each topic. A short survey was designed to assess the students' perception of clarity of concepts, confidence of solving the examination, and their preference for one of these methods. Appropriate statistical tests were applied to the data. RESULTS: Synchronous method of online teaching was preferred by majority of the students (P < 0.001). Students' clarity in understanding of the concepts (P < 0.001) and confidence of the ability to solve examination questions (P < 0.05) was higher after synchronous teaching. The examination scores after synchronous online teaching were significantly higher (P = 0.0156) than those for topics covered through asynchronous method. CONCLUSION: Online teaching, especially the synchronous method, offers an opportunity of continuum of training during crisis situations such as the ongoing novel coronavirus pandemic. The wide availability of internet services and the ever-changing global situation mandates readiness for this modality of teaching, both for the teachers and the students.