Employing Conversational Agents in Palliative Care: A Feasibility Study and Preliminary Assessment

Recording of patient-reported outcomes (PROs) enables direct measurement of the experiences of patients with chronic conditions, including cancer; thus, PROs are a critical element of high quality, person-centered care for cancer patients. A growing body of literature reports on the feasibility of using electronic tools for the collection of Patient reported Outcomes (ePROs), although the usability of available solutions does affect their acceptance and use. In parallel, recent advancement in artificial intelligence, machine learning and speech recognition have led to the growing interest in conversational agents, i.e. software applications that mimic written or spoken human speech. In the present manuscript we provide a review of current developments regarding the implementation of conversational agents and their application in the domain of palliative care for oncology patients and also present (i) a methodology for the implementation of a conversational agent able to collect ePRO health data and (ii) initial evaluation results from a relevant feasibility study. Our approach differs from other available systems since the conversational agent reported in the present work is not based on rules, but rather uses machine learning algorithms and more specifically recurrent neural networks (RNN) for identifying appropriate answers. Evaluation results of user experience provided promising results and highlight that users gave positive responds when interacting with the system. Based on the User Experience Questionnaire, pragmatic quality and overall quality were categorized as excellent and hedonic quality was categorized as good. The result of this research can be used as reference for the future development and improvement of the conversational agents in the healthcare domain.