Patient Satisfaction: The Role of Artificial Intelligence in Healthcare

Abstract

Applications of artificial intelligence (AI) can be seen in almost every aspect of the healthcare system, as it has potential to affect almost every facet of the healthcare, from detection of ailments and serious or complex chronic diseases to their control, prevention and cure. With technological innovations, upgradation and adoption in the field of healthcare, healthcare professionals are required to be well prepared to accept the continuously evolving technology and its application to provide best healthcare facilities, which gave rise to the various studies on the role of the machine learning (ML), AI, deep learning (DL), etc., in the field of healthcare. Similarly, the rise in digitalised hospitals, medical facilities, records and data has resulted in the improvisation in the field of healthcare, which in turn has increased the need of experts, professionals, experienced and digitally literate workforce teams in the field of entire healthcare system. Understanding the roles of these advanced technologies, impacts being created on the health, lifestyle and the entire healthcare system, along with the perception of the patients towards it, will shape the way for the improvements and the applications of AI and its outcomes to be achieved, resulting in healthier world for the patients and the society. The objective of the study is to create a patient satisfaction model and validate it with respect to factors influencing patient satisfaction of several patients undergoing AI treatment factors. In the study, the United States, Canada, Australia, UAE and China were chosen as a place of survey, as these are advanced countries and the use of AI is highest in these countries compared to other countries, and survey was done with the help of structured questionnaire. In our earlier study, exploratory factor analysis (EFA) was performed for initial knowledge development on the construct of patients undergoing AI treatment. Patient satisfaction rests on six broad dimensions: First factor is personal touch (PT), second factor is comprehensive gap (CG), third factor is answerability (AB), fourth factor is nerve racking (NR), fifth factor is wrong reporting (WR) and sixth factor is enlightened (EL). With the help of confirmatory factor analysis (CFA) and structured equation modelling (SEM), it has emerged from the study that patient satisfaction level of the construct suggests that PT will have a greater impact on patient satisfaction, and it is the most significant factor of patient satisfaction compared to other constructs. Thus, we can conclude that PT still remains the most important factor in the minds of patients before undergoing AI treatment.