**2. Related works**

The paradigm of value-based healthcare represents a shift toward more efficient and more effective medical care. However, it requires additional sources of data to improve shared decision-making and enable more personalized decision-making. Therefore, conversational intelligence can significantly contribute to patient activation and engagement [24]. The technology is based on spoken language technologies (SLT), i.e., NLP, ASR, chatbot, and TTS, that enables machines to interact with humans in very natural way, using mobile or web platforms [25]. In healthcare, this started already in 1966 with ELIZA [26]. Nowadays, conversational agents have been

#### *Multilingual Chatbots to Collect Patient-Reported Outcomes DOI: http://dx.doi.org/10.5772/intechopen.111865*

used to solve much more complex tasks, such as booking tickets and acting as customer service agents [27]. In healthcare, conversational agents can provide patients with, e. g. personalized health and therapy information and relevant products and services. Additionally, they can connect them with healthcare providers, suggest diagnoses, and even recommended treatments based on patient symptoms and reports. Namely, multilingual communication, cost-effectiveness, and 24/7 availability make embodied conversational agents (ECAs) very useful for all those patients who have major medical concerns outside of doctor's operating hours. Several studies show that patients can perceive ECAs as interaction partners instead of human physicians and are able to trust them. Thus, they are willing to disclose medical information report more symptoms, etc. [28]. In oncology setting, CI (conversational intelligence) focuses mostly on (speech-enabled) chatbots [29]. They can contribute to lifestyle changes [30], to screening (i.e., iDecide [31]) and improving mental health state through managing psychological distress [32–34]. Therefore, chatbots are already well recognized as an enabler for adherence, active patient engagement, and satisfaction increase [35, 36]. However, the chatbots still tackle the long-term adherence with sustainable quality of the reported data [37]. In [36], they reported that active use of this technology drops already after 14 days. Namely, patients' understanding, their ability to remember the details, and perceived trustworthiness are the main factors of patient adherence [38]. Therefore, in the system of the PERSIST project, an ECA is additionally introduced. ECAs can undoubtedly increase this long-term adherence by engaging with users in interaction that is enriched by incorporating nonverbal communication [37]. Since ECA is autonomous and intelligent software entity with an embodiment used to communicate with the user [39], it can provide a system with symmetric multimodality based on speech, gesture, and facial expression. Embodiments can be designed as virtual human characters, animals, or robots [40–42]. Such fully symmetric interaction opens up the opportunity to introduce human-like qualities and significantly improves the believability of the humanmachine interfaces [43]. ECAs in healthcare can be used for the treatment of mood disorders, anxiety, psychotic disorders, autism, substance use disorders, etc. [44]. In [17], ECAs already proved a promising tool for persuasive communication in healthcare. While in [42], technological and clinical possibilities of less complex ECAs were investigated, and ECAs are also shown to be a solution for routine applications in the means of rapid development, testing, and application. Stal in [45] also found out that the agents' textual output and/or speech as well as its gaze and facial expressions are the most important features. In general, for healthcare, ECA studies focused mainly on physical activity [46–48], stress [30], nutrition [49, 50], blood glucose monitoring [41], and sun protection [51]. However, there are several other studies that focus on speech, facial, and gaze expressions as the main design features [45]. ECAs in healthcare are mostly 2D-based, since gestures and appearance are not considered as main design features, and only a few studies addressed gestures.

In the PERSIST system, therefore, we use two 3D embodied conversational agents, female or male that can interact with patients in the following six languages: Slovenian, English, Spanish, French, Russian, and Latvian. ECAs are able to represent facial expressions and exploit gestures in order to enhance user experience. Namely, in this way, it is possible to better support verbal counterparts, regulate communicative relationships, and maintain clarity in the discourse.

In [52], the conversational agents are designed as a prototype, while the contribution to health-related outcomes is evaluated without relevant statistical significance. Further, Sayeed et al. in [53] describe an approach to create a patient-centered health system that is based on the FHIR standard and applications that can make requests and reports of HL7 FHIR resources.
