Next-gen chatbots — powered by AI and natural language processing — are growing in demand in healthcare due to their ability to understand user input with ease and respond to complex human queries.
Beyond voice-enabled chatbots or general virtual assistants like Siri and Alexa, GenAI chatbots can produce images, sounds and high-quality texts depending on the large language models (LLMs) they are trained on. These new-age superpower chatbots have been responding to multilingual human queries across industries.
It's essential to note that traditional healthcare chatbots lack in-depth semantic understanding and contextual awareness, which is crucial for effective patient communication. GenAI is poised to enhance these aspects, promising to transform these chatbots into more intuitive and responsive tools for patient care.
The traditional chatbots relied heavily on Natural Language Processing (NLP) techniques like pattern matching, keyword recognition and decision trees. They operated by recognizing specific keywords or phrases in user inputs and responding with pre-programmed answers.
This approach required extensive manual scripting and was limited in handling varied and complex user interactions, as it depended on anticipating and programming for every potential conversation path.
Improving patient and employee experience
In the context of healthcare, next-gen chatbots can help a patient review personal records, such as x-rays, while ‘therapeutic’ chatbots have even been rolled out to address the growing mental health crisis.
The healthcare industry has embraced AI-enabled chatbots to handle an increase in patient queries, including information on the consumption of medicines, appointment with doctors, health tips (in the absence of doctors) and pre- and post-surgery consultations.
Healthcare providers have been using chatbots to free up valuable employees engaged in answering questions and deploying them in areas they are more required. AI-enabled chatbots are now creating new opportunities for doctors, hospitals, medical devices providers and caregivers.
For example, a prominent US-based medical devices and healthcare company that addresses a spectrum of healthcare needs wanted to devise an effective and engaging solution to replace their previous offline premedical questionnaire process.
It looked forward to an AI-powered conversational agent (chatbot) that could engage with patients effectively with responses to queries and fill out the form based on the collected data automatically.
While leveraging chatbots to ease daily work is a growing trend, it requires careful consideration of guardrails, especially when AI and data are involved.
Addressing all the challenges the client faced, HCLTech developed a GenAI-based agent using an LLM — similar to ChatGPT — to address patients’ queries.
The patients’ voice inputs are transcribed into text with machine comprehension and conditional prompting to generate domain-specific responses. The chatbot is also well-versed with conversion challenges and responses based on the patients’ age, gender and speech-related difficulties (if any).
“LLMs like GPT are revolutionizing healthcare chatbots with their superior abilities in both processing and creating relevant healthcare information. Their prowess in understanding a wide range of data, both structured and unstructured, significantly improves the functionality of patient-centric chatbots. This leads to better diagnosis, tailored patient education and advanced medical data analysis,” says Dr. Mohsen Amiribesheli, Senior Technical Architect, AI Lead (Cloud Native and AI Labs) at HCLTech.
“However, the key to fully realizing their potential in healthcare lies in establishing rigorous frameworks that ensure their ethical and responsible application in patient care and clinical decision-making processes,” adds Dr. Amiribesheli.
HCLTech helped the client with streamlining the premedical questionnaire process, eliminating manual form-filling and freeing up its workers with a 40% time saving with the agent’s intervention.
As a result of this initiative, doctors gained valuable insights into patient health trends and patterns, enabling informed decision-making and improved treatment strategies.
