Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteris...Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals.Medical equipment information management is an important part of the construction of a modern hospital,as it is linked to the degree of diagnosis and care,as well as the hospital’s benefits and growth.The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare,as well as to conduct an empirical review of the impact of organizational,environmental,and individual factors on RFID adoption in the healthcare industry.In contrast to previous research,the current study focuses on individual factors as well as organizational and technological factors in order to better understand the phenomenon of RFID adoption in healthcare,which is characterized as a dynamic and challenging work environment.This research fills a gap in the current literature by describing how user factors can influence RFID adoption in healthcare and how such factors can lead to a deeper understanding of the advantages,uses,and impacts of RFID in healthcare.The proposed study has superior performance and effective results.展开更多
The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clin...The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.展开更多
基金This work was supported by the Institute for Social and Economic Research(ISER),Zayed University,Under Policy Research Incentive Plan,2017。
文摘Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals.Medical equipment information management is an important part of the construction of a modern hospital,as it is linked to the degree of diagnosis and care,as well as the hospital’s benefits and growth.The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare,as well as to conduct an empirical review of the impact of organizational,environmental,and individual factors on RFID adoption in the healthcare industry.In contrast to previous research,the current study focuses on individual factors as well as organizational and technological factors in order to better understand the phenomenon of RFID adoption in healthcare,which is characterized as a dynamic and challenging work environment.This research fills a gap in the current literature by describing how user factors can influence RFID adoption in healthcare and how such factors can lead to a deeper understanding of the advantages,uses,and impacts of RFID in healthcare.The proposed study has superior performance and effective results.
基金supported in part by Zayed University,office of research under Grant No.R17089.
文摘The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.
