Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study

Abstract

Background Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied or disembodied conversational agents that allow chatting with a technical system in a natural language. However, only little comprehensive research is conducted about patients' perceptions and possible applications of virtual assistant in healthcare with cancer patients. This research aims to investigate the key acceptance factors and value-adding use cases of a virtual assistant for patients diagnosed with cancer. Methods Qualitative interviews with eight former patients and four doctors of a Dutch radiotherapy institute were conducted to determine what acceptance factors they find most important for a virtual assistant and gain insights into value-adding applications. The unified theory of acceptance and use of technology (UTAUT) was used to structure perceptions and was inductively modified as a result of the interviews. The subsequent research model was triangulated via an online survey with 127 respondents diagnosed with cancer. A structural equation model was used to determine the relevance of acceptance factors. Through a multigroup analysis, differences between sample subgroups were compared. Results The interviews found support for all factors of the UTAUT: performance expectancy, effort expectancy, social influence and facilitating conditions. Additionally, self-efficacy, trust, and resistance to change, were added as an extension of the UTAUT. Former patients found a virtual assistant helpful in receiving information about logistic questions, treatment procedures, side effects, or scheduling appointments. The quantitative study found that the constructs performance expectancy (ß = 0.399), effort expectancy (ß = 0.258), social influence (ß = 0.114), and trust (ß = 0.210) significantly influenced behavioral intention to use a virtual assistant, explaining 80% of its variance. Self-efficacy (ß = 0.792) acts as antecedent of effort expectancy. Facilitating conditions and resistance to change were not found to have a significant relationship with user intention. Conclusions Performance and effort expectancy are the leading determinants of virtual assistant acceptance. The latter is dependent on a patient’s self-efficacy. Therefore, including patients during the development and introduction of a VA in cancer treatment is important. The high relevance of trust indicates the need for a reliable, secure service that should be promoted as such. Social influence suggests using doctors in endorsing the VA.

Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review

“…Publication types included 23 (72%) journal papers ( Tables 2 and 3 ) [ 10 , 12 , 15 , 17 , 34 , 37 , 39 , 41 , 43 , 45 - 49 , 52 - 62 ] and 9 (28%) conference papers ( Table 4 ) [ 13 , 35 , 36 , 38 , 40 , 42 , 44 , 50 , 51 ]. Study designs included quantitative research (22/32, 69%) [ 12 , 13 , 15 , 34 , 37 , 39 , 40 , 42 - 44 , 47 - 52 , 54 , 55 , 57 - 59 , 61 ], qualitative research (2/32, 6%) [ 35 , 60 ], and mixed methods studies (4/32, 12%) [ 38 , 41 , 45 , 46 ], in addition to systematic reviews (4/32, 12%) [ 17 , 36 , 53 , 56 ]. Most studies chose general practice (8/32, 25%) [ 34 , 37 , 40 , 41 , 46 , 49 , 54 , 55 ] as the target medical field.…”

“…In a total of 8 (25%) studies [ 17 , 36 , 39 , 41 , 45 , 46 , 51 , 60 ], users felt that AI lacked empathy and was impersonal. Among them, users in 2 (6%) studies [ 45 , 46 ] felt that AI was unable to understand emotion-related issues, especially mental health problems, and 2 (6%) studies [ 41 , 60 ] pointed out that the information-conveying method of AI, such as transmitting complex disease information without human presence [ 60 ] and explaining the disease from the perspective of “how bad it is” [ 41 ], could also lead users to think that AI is indifferent and inhumane.…”

Cutaneous melanoma in older patients

“…In this peculiar setting, the involvement of general practitioners in the diagnosis, hopefully supported by “virtual” tele-dermatology, may provide “at-home” monitoring of at-risk lesions, ultimately promoting secondary prevention strategies [ 46 ]. A Cochrane systematic review found that more than 93% of malignant skin lesions may be confidently assessed by tele-dermatology [ 47 ], and this digital opportunity may play a crucial role in diagnostic anticipation.…”

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

“…25 Other frameworks include the information success system model (ISSM), 26 task-technology fit -TTF, 27 Extended Technology Acceptance model -TAM2, 28 Fit between individuals, tasks, and technology (FITT), and unified theory of acceptance and use of technology -UTAUT. [29][30][31][32] These have been collectively employed in the health arena to drive the enhancement and adoption of HIS for health care applications. 19,26 Conceptual and research frameworks were developed along the same line.…”