The 21st-century patient wants it all: a digital experience that provides swift access to healthcare solutions but with a human touch. Available 24/7, modern human-like chatbots are making automated self-service and conversational AI popular today for consumers. The demand from patients is growing, with expectations leaning towards synergised platforms that can be a one-stop shop that not only provides answers but also services.
This is where an insurance chatbot steps in. It is a virtual assistant powered by artificial intelligence that can serve insurance customers by customising their needs and facilitating communication with the insurance firm.
When both solutions are looked at independently, a recent poll of healthcare practitioners found that 77 per cent of users believe chatbots will be able to treat patients within the next 10 years. The healthcare chatbot market is predicted to reach US$944.65 million by 2032 from US$230.28 million in 2023. Additionally, chatbots have helped firms cut US$8 billion in expenditures by 2022, according to a report in CNBC.
Related: AI backed hyper personalisation fuels health insurtech’s future
On the other hand, insurtech is gaining momentum as dedicated start-ups are testing cutting-edge digital solutions to improve client experience, back-end operations, and everything in between. The global insurtech market size was valued at US$5.45 billion in 2022 and is expected to expand at a compound annual growth rate (CAGR) of 52.7 per cent from 2023 to 2030.
When both solutions are synergised, it can lead to a more patient-centric healthcare experience, where individuals receive personalised medical guidance and insurance support seamlessly within a single platform. For example, Lemonade, a US-based artificial intelligence-powered insurance firm, has created a chatbot that guides policyholders through the entire customer journey. Users can use the bot to apply for policies, pay bills, process claims, and get progress updates without making a single phone call. Maya, Lemonade's chatbot, set a world record a few years ago by processing and settling a US$979 claim in under three seconds.
Anushka Patchava, Co-founder and Chief Product Officer of Wellx, a Dubai-based insurtech platform that optimises wellness experiences built on insurance, tells us more about how the integration between chatbots and insurtech can optimise patient experience.
Chatbots and insurtech: The perfect pairing?
Along with natural language processing and generative AI models such as Med-PaLM2 (Google) and OpenAI's ChatGPT, chatbots have demonstrated their ability to provide medical advice by integrating multiple data and evidence sources and concisely assimilating the information, all while appearing human-like.
“Initial tests have shown that these tools are more accurate than the standard 'Dr. Google' search that we have grown accustomed to. For instance, when used in initial claims assessment and insurance triage, it can significantly improve getting people to the right place at the right time and cost. This can help alleviate the workforce shortage and flow issues in clinics and hospitals. By guiding patients to primary care or digital care before secondary treatment, an integrated insurance and health chatbot, for example, can lower the needlessly high cost of care while also continuously evaluating fraud, waste, and abuse,” said Patchava.
The role of insurtech chatbots in healthcare
Insurance chatbots today are divided into two types: rule-based and AI-driven. Rule-based chatbots, which are based on pre-created processes, can provide clients with consistent responses based on a collection of commonly asked questions, with reportedly high effectiveness in resolving simple concerns.
The development of AI-driven chatbots, on the other hand, opens new possibilities by facilitating underwriting help, customer service, fraud detection, and a variety of other operations.
Related: Prevalence of insurtech in healthcare
“Insurance chatbot uses include simple claims processing and settlement. It can be used to report incidents, fill out forms, and track claim settlement; provide multi-language support without the need to hire this capability; and offer higher security by pre-populating fields through automated workflows so the employee does not have access to customer-sensitive data, only the chatbot does,” explained Patchava.
Although such models are still in the early stages of research, they can be improved through repeated learning. Patchava concluded that whether they are accepted for widespread deployment in healthcare will depend on legislation and physician tolerance.
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