Human values assessment toward AI-based patient state prediction

Abstract: Recent advances in artificial intelligence (AI) technology are remarkable, and AI may predict a patient’s future state (prognosis) using large amounts of data in the future. However, patients are not always satisfied with the prediction results. In order for a patient to accept prediction results and change behaviors in life, prediction results should reflect his/her values. AI-based patient state prediction should be implemented in a way that is not only data-driven but also integrated with domain knowledge about human values. This paper reports a case study where we assessed data related to values toward building a domain knowledge for AI-based patient state prediction.We designed a rehabilitation exercise service based on a person-centered principle [1] and intervened with one patient [2]. From the preliminary stage of service design, we attempted to build rapport and motivate her. We expected her to self-disclose through repeated dialogues. While understanding her life backgrounds and values, we defined rehabilitation goals for her independent living.12 rehabilitation exercise sessions were held for three months, and questionnaires and semi-structured interviews were conducted before the first session and after the final session. Even after the experiment ended, we continued to have conversations with her and obtained information about her life after the experiment ended. Measurements taken after the experiment showed no noticeable effect on her physical functions. Regarding her self-disclosure, as the experiment progressed, she revealed more of her true feelings, including negative ones. Before the experiment, she negatively commented about daily activities that she could not do well, but after the experiment, she positively did with focusing on the things she could do, although she was not perfect. We confirmed her active participation in social activities, such as going out wearing a clothe made by sewing, a task that she had viewed negatively before the experiment. Factors that influenced her behavior changes include building rapport through repeated conversations and self-disclosure as an effect of the experimenter’s interest in and empathy for her. Regarding self-disclosure, at the beginning of the experiment, most disclosures were positive-sounding superficial ones, but gradually they changed to deeper disclosures that included negative content.The patient’s value data suggested by the self-disclosure in this study is still insufficient to form a value domain knowledge system. However, we believe that the acquisition of value data based on a patient’s self-disclosure and behavioral changes will help realize AI state prediction that will lead to patient acceptance in the future. Future work will include a detailed analysis of the factors that influenced self-disclosure and the construction of a value assessment framework.[1] Kitwood, T. and Bredin, K. (1992) Towards a theory of dementia care: Personhood and well-being, Ageing and Society, Vol.12, No.3, pp.269-287.[2] Yukihira, T et al. (2023). Toward an online rehabilitation exercise service based on personal independent living goals and risk management. Human Systems Engineering and Design (IHSED 2023): Future Trends and Applications, Vol. 112, pp.187-194.

Keywords: value, self-disclosure, motivation, behavior change, service design, patient state prediction

DOI: 10.54941/ahfe1004600

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