Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diag
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Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS) Wenjuan Fan1,2 · Jingnan Liu1 · Shuwan Zhu1 · Panos M. Pardalos3
© Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract Compared to the booming industry of AIMDSS, the usage of AIMDSS among healthcare professionals is relatively low in the hospital. Thus, a research on the acceptance and adoption intention of AIMDSS by health professionals is imperative. In this study, an integration of Unified theory of user acceptance of technology and trust theory is proposed for exploring the adoption of AIMDSS. Besides, two groups of additional factors, related to AIMDSS (task complexity, technology characteristics, and perceived substitution crisis) and health professionals’ characteristics (propensity to trust and personal innovativeness in IT) are considered in the integrated model. The data set of proposed research model is collected through paper survey and Internet survey in China. The empirical examination demonstrates a high predictive power of this proposed model in explaining AIMDSS adoption. Finally, the theoretical contribution and practical implications of this research are discussed. Keywords AIMDSS · UTAUT · Adopt intention · Initial trust
1 Introduction In China, misdiagnosis and low efficiency in medical diagnosis are the main trigger factors of the contradiction between patients and doctors. In most cases, health professionals have to make their decision based on various medical examination data generated by kinds of technical means, such as the X ray film, endoscopic image, etc. However, it is not a simple job to identify the lesion position, and besides, the efficiency and accuracy may decrease largely after the doctors have read too many images. The complexity and workload of diagnosis have
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Wenjuan Fan [email protected]
1
School of Management, Hefei University of Technology, Hefei 230009, China
2
Key Laboratory of Minister of Education on Process Optimization and Intelligent Decision-making, Hefei, China
3
Department of Industrial Systems and Engineering, University of Florida, Gainesville, FL, USA
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Ann Oper Res
created pressure on health professionals for a long time. There are no authoritative statistics of the clinical misdiagnosis rate. However, it is a consensus that the average clinical misdiagnosis rate is about 30%, and for malignant cancers (Leukemia, pancreatic, etc.) is even up to 40%. 1 Therefore, it creates a great demand and significance in improving the accuracy of medical diagnosis. At present, some artificial intelligence products have aroused lot of attention in the medical field, which are mainly developed for disease detection and diagnosis on basis of patients’ examination data, including the forms of image and text. Applying the artificial intelligence technology in the problem of medical diagnosis is expected to assist physicians in their routine work to improve the diagnostic level and al
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