ABSTRACT
IntroductionMulti-criteria decision analysis (MCDA) is a useful tool in complex decision-making situations and has been used in medical fields to evaluate treatment options and drug selection. We aimed to provide valuable insights on the use of MCDA in health care through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain using a scientometric and bibliometric analysis.MethodsPublications related to MCDA in health care were identified by searching the Web of Science Core Collection on 14 July 2021. Three bibliometric software programs (VOSviewer, Bibliometrix, and CiteSpace) were used to conduct the analysis.ResultsA total of 410 publications were identified from 196 academic journals (average yearly growth rate of 32% from 1999 to 2021), with 23,637 co-cited references by 871 institutions from 70 countries or regions. The USA was the most productive country (n=80), while the Universiti Pendidikan Sultan Idris (n=16), Université de Montréal (n= 13), and Syreon Research Institute (n=12) were the most productive institutions. The biggest nodes in every cluster of author networks were Aos Alaa Zaidan, Mireille Goetghebeur, and Zoltan Kalo. The top journals in terms of number of articles (n=17) and citations (n=1,673) were Value in Health and the Journal of Medical Systems, respectively. The research hotspots mainly included the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. In the recent literature there was more emphasis on coronavirus disease 2019 (COVID-19) and fuzzy Technique for Order Preference by Similarities to Ideal Solution (TOPSIS). Big data, telemedicine, TOPSIS, and the fuzzy AHP, which are well-developed and important themes, may be the trends in future research.ConclusionsThis study provides a holistic picture of the MCDA-related literature published in health care. MCDA has a broad application in different topic areas and would be helpful for practitioners, researchers, and decision makers working in health care when faced with complex decisions. It can be argued that the door is still open for improving the role of MCDA in health care, both in its technologies and its application.
ABSTRACT
Objective: Multicriteria decision analysis (MCDA) is a useful tool in complex decision-making situations, and has been used in medical fields to evaluate treatment options and drug selection. This study aims to provide valuable insights into MCDA in healthcare through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain. Methods: A bibliometric analysis was conducted on the publication related to MCDA in healthcare from the Web of Science Core Collection (WoSCC) database on 14 July 2021. Three bibliometric software (VOSviewer, R-bibliometrix, and CiteSpace) were used to conduct the analysis including years, countries, institutes, authors, journals, co-citation references, and keywords. Results: A total of 410 publications were identified with an average yearly growth rate of 32% (1999-2021), from 196 academic journals with 23,637 co-citation references by 871 institutions from 70 countries/regions. The United States was the most productive country (n = 80). Universiti Pendidikan Sultan Idris (n = 16), Université de Montréal (n = 13), and Syreon Research Institute (n = 12) were the top productive institutions. A A Zaidan, Mireille Goetghebeur and Zoltan Kalo were the biggest nodes in every cluster of authors' networks. The top journals in terms of the number of articles (n = 17) and citations (n = 1,673) were Value in Health and Journal of Medical Systems, respectively. The extant literature has focused on four aspects, including the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. COVID-19 and fuzzy TOPSIS received careful attention from MCDA applications recently. MCDA in big data, telemedicine, TOPSIS, and fuzzy AHP is well-developed and an important theme, which may be the trend in future research. Conclusion: This study uncovers a holistic picture of the performance of MCDA-related literature published in healthcare. MCDA has a broad application on different topics and would be helpful for practitioners, researchers, and decision-makers working in healthcare to advance the wheel of medical complex decision-making. It can be argued that the door is still open for improving the role of MCDA in healthcare, whether in its methodology (e.g., fuzzy TOPSIS) or application (e.g., telemedicine).
Subject(s)
COVID-19 , Bibliometrics , Decision Support Techniques , Delivery of Health Care , Humans , Technology Assessment, Biomedical , United StatesABSTRACT
Over the last several decades, no emerging virus has had a profound impact on the world as the SARS-CoV-2 that emerged at the end of 2019 has done. To know where severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated from and how it jumped into human population, we immediately started a surveillance investigation in wild mammals in and around Wuhan when we determined the agent. Herein, coronaviruses were screened in the lung, liver, and intestinal tissue samples from fifteen raccoon dogs, seven Siberian weasels, three hog badgers, and three Reeves's muntjacs collected in Wuhan and 334 bats collected around Wuhan. Consequently, eight alphacoronaviruses were identified in raccoon dogs, while nine betacoronaviruses were found in bats. Notably, the newly discovered alphacoronaviruses shared a high whole-genome sequence similarity (97.9 per cent) with the canine coronavirus (CCoV) strain 2020/7 sampled from domestic dog in the UK. Some betacoronaviruses identified here were closely related to previously known bat SARS-CoV-related viruses sampled from Hubei province and its neighbors, while the remaining betacoronaviruses exhibited a close evolutionary relationship with SARS-CoV-related bat viruses in the RdRp gene tree and clustered together with SARS-CoV-2-related bat coronaviruses in the M, N and S gene trees, but with relatively low similarity. Additionally, these newly discovered betacoronaviruses seem unlikely to bind angiotensin-converting enzyme 2 because of the deletions in the two key regions of their receptor-binding motifs. Finally, we did not find SARS-CoV-2 or its progenitor virus in these animal samples. Due to the high circulation of CCoVs in raccoon dogs in Wuhan, more scientific efforts are warranted to better understand their diversity and evolution in China and the possibility of a potential human agent.