Chatbot use cases in the Covid-19 public health response PMC

We gathered information on these to (a) derive a comprehensive set of chatbot public health response use cases and (b) identify their design characteristics. Our study adds to an emerging literature on the use of chatbots in healthcare in general (see chatbot use cases in healthcare Car et al.12 and Montenegro et al.13 for reviews), and in the Covid-19 response in particular (see Almalki and Azeez14 for a review). Woebot is among the best examples of chatbots in healthcare in the context of a mental health support solution.

A study performed on Woebot, developed based on cognitive behavioral therapy, showed that depressive symptoms were significantly reduced, and participants were more receptive than in traditional therapies [41]. This agreed with the Shim results, also using the same type of therapy, which showed that the intervention was highly engaging, improved well-being, and reduced stress [82]. When another chatbot was developed based on the structured association technique counseling method, the user’s motivation was enhanced, and stress was reduced [83]. Similarly, a graph-based chatbot has been proposed to identify the mood of users through sentimental analysis and provide human-like responses to comfort patients [84]. Vivobot (HopeLab, Inc) provides cognitive and behavioral interventions to deliver positive psychology skills and promote well-being.

Chatbot use cases

Because the last time you had the flu and searched your symptoms on Google, it made you paranoid. You can also leverage outbound bots to ask for feedback at their preferred channel like SMS or WhatsApp and at their preferred time. The bot proactively reaches out to patients and asks them to describe the experience and how they can improve, especially if you have a new doctor on board.

Now, if NLP allows the system to understand and reply back in human language, machine learning, a set of techniques that enables machines to learn from past and current data, optimizes processes for more accurate results. By combining these two, conversational AI systems recognize various phrasings of the same intent, including spelling mistakes, slang and grammatical errors and provide accurate responses to user queries. A well built healthcare chatbot with natural language processing (NLP) can understand user intent with the help of sentiment analysis. Based on the understanding of the user input, the bot can recommend appropriate healthcare plans. Healthcare providers are relying on conversational artificial intelligence (AI) to serve patients 24/7 which is a game-changer for the industry.

A new era in healthcare: Embracing AI for enhanced care

Our tech team has prepared five app ideas for different types of AI chatbots in healthcare. The Physician Compensation Report states that, on average, doctors have to dedicate 15.5 hours weekly to paperwork and administrative tasks. With this in mind, customized AI chatbots are becoming a necessity for today’s healthcare businesses. The technology takes on the routine work, allowing physicians to focus more on severe medical cases. Healthcare providers can handle medical bills, insurance dealings, and claims automatically using AI-powered chatbots. Chatbots also support doctors in managing charges and the pre-authorization process.