Healthcare with Conversational AI: Strategies and Examples
Further research is needed to examine the cost-effectiveness and value of these agents in health care, both in their current and potential states. Higher-quality studies—with more consistent reporting of design methods and better sample selection—are also needed to more accurately assess the usefulness and identify the key areas of improvement for current conversational agents. A more holistic approach to the design, development, and evaluation of conversational agents will help drive innovation and improve their value in health care.
Initially, studies that did not have an explicit design were classified as qualitative or interpretative studies. Therefore, these studies were coded as other and assessed using the AXIS tool for cross-sectional studies, which was deemed to provide the most systematic evaluation of the various elements of the studies [30]. The quality of these studies was assessed as best as possible; however, the judgments should be considered in the context of these limitations. For conversational agents to be successful in health care, it is crucial to understand the effectiveness of current agents in achieving their intended outcomes. User-identified problems will need to be addressed if conversational agents are to have a significant impact on health care, because their impact depends on people being willing to use them and preferring to use them over alternatives. The information gathered in this review identifies the current issues with conversational agents that need to be overcome and can be used to help determine which elements of the agents are most likely to be successful and useful in various aspects of health care.
Health Tracking & Management
That’s why Interactions Intelligent Virtual Assistants enable you to provide your members with more — effective self-service options that enhance the customer experience while still lowering costs. Given the potential for adverse outcomes, it becomes imperative to ensure that the development and deployment of AI chatbot models in healthcare adhere to principles of fairness and equity (16). Achieving this can promote equitable healthcare access and outcomes for all population groups, regardless of their demographic characteristics (20). As AI chatbots increasingly permeate healthcare, they bring to light critical concerns about algorithmic bias and fairness (16). AI, particularly Machine Learning, fundamentally learns patterns from the data they are trained on Goodfellow et al. (17). If the training data lacks diversity or contains inherent bias, the resultant chatbot models may mirror these biases (18).
The United States had the highest number of total downloads (~1.9 million downloads, 12 apps), followed by India (~1.4 million downloads, 13 apps) and the Philippines (~1.25 million downloads, 4 apps). Details on the number of downloads and app across the 33 countries are available in Appendix 2. Twenty of these apps (25.6%) had faulty elements such as providing irrelevant responses, frozen chats, and messages, or broken/unintelligible conversational ai in healthcare English. Three of the apps were not fully assessed because their healthbots were non-functional. However, Conversational AI will get better at simulating empathy over time, encouraging individuals to speak freely about their health-related issues (sometimes more freely than they would with a human being). Woebot, a chatbot therapist developed by a team of Stanford researchers, is a successful example of this.
Continuous Learning and Adaptation
As the technology advances and integrates more seamlessly into healthcare operations, its applications will likely continue to expand. AI chatbots and virtual assistants are pivotal in remote and underserved areas, offering basic medical advice and emergency guidance. Bots like WoeBot provide mental health support, demonstrating how AI bridges the gap in healthcare accessibility. In healthcare institutions, access to electronic medical records which include patient profiles, previous treatments and allergies make a big difference. By integrating into these systems, the conversational AI can provide users and patients with more relevant and personalised responses. Due to the number of conversational agents in development and/or those that did not progress to the evaluation stages of development, studies that were solely descriptive were excluded.
Related to this issue, as the conversational agents often had to ask questions more than once to be able to process the response, users in 3 studies noted disliking the repetitive conversations with the agents [13,36,37]. Both of these issues are key areas of improvement for future research and development of conversational agents because they represent limitations in the usability of the agents in a real-world context. There were a wide variety of areas of health care targeted by the conversational agents of the included studies.
