Introduction

Conversational AI is a pivotal aspect of digital transformation in the healthcare industry. It can allow patients and the doctors to maintain a safer distance, automate various processes, improve communication, respond faster and help optimize time in urgent situations. Covid has triggered increased investment in AI. ABI Research has predicted that spending on AI in the healthcare and pharmaceutical industries is going to be more than US$ 2 billion in the next five years. In this article we look at some of the key trends driving the growth of Artificial Intelligence in healthcare and how we can transform the patient experience in different ways using Conversational AI.

Market Trends

• Global conversational AI including chatbots, virtual agents and voice assistants is projected to be US$ 18.4 billion by 2026
• CAGR of 21.8% in the next 4 years
• Chatbots adoption rates are expected to double over the next 2-5 years (Forbes)
• Conversational AI had the highest number of patents filed in 2020
• By 2023, 70% of white-collar workers will interact regularly with conversational platforms (Gartner)

• Advanced chatbots will drive chatbot eCommerce transactions to US$ 112 billion by 2023 (Juniper Research)
• Over 50% of enterprises spend more amount per annum on chatbots than on traditional mobile apps (Gartner)
• 75-90% of queries will be handled by chatbots, resulting in cost savings of US$ 0.70 per interaction (CNBC)
• Conversational chatbots can help to reduce customer service costs by around 30% (Chatbots Magazine)

What is Conversational AI?

• Conversational AI is the set of technologies behind automated messaging and speech-enabled applications that offer human-like interactions between computers and humans
• Conversational AI recognizes speech and text, understands intent, deciphers different languages, and responds in a way that mimics human conversation
• Solutions can be offered over both text and voice modalities

How does Conversational AI work?

Conversational AI uses various technologies such as Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Natural Language Generation (NLG), Advanced Dialog management, and Machine Learning (ML) to understand, react and learn from every interaction.

Conversational AI vs. Traditional Chatbots

A real AI chatbot conversation requires conversational AI, which does not need a script but rather progressively teaches itself through reinforcement learning.

Varying Levels of Complexity

1. Chatbot - basic answer and response machines, where you must type the exact keyword required to receive the appropriate response. They do not use NLP or dialog management or machine learning to improve over time.
2. Virtual Personal Assistant – e.g. Amazon Alexa, Apple’s Siri, and Google Home. They serve a general purpose and are linear. Do not carry context from one conversation to the next. These assistants use ASR and NLP, but have simple dialog management.
3. Virtual “Customer” Assistant - more advanced systems that serve a specific purpose and have specialized dialog management. Able to carry context from one interaction to the next for better user experience.
4. Virtual Employee Assistant - These applications are purpose-built, specialized, and automate processes, also called Robotic Process Automation. Used to streamline enterprise operations.

Importance of Conversational AI in Healthcare

Valuable features of conversational AI in healthcare:

• Anonymity: especially in sensitive and mental health issues
• Monitoring: awareness and tracking of user’s behaviour, anxiety, weight changes, to encourage developing better habits
• Personalization: Some applications make use of measurements of
• Physical vitals (oxygenation, heart rhythm, body temperature) via mobile sensors
• Patient behaviour via facial recognition
• Real time interaction: immediate response, notifications and reminders
• Scalability: ability to interact with numerous users

Uses of Conversational AI in Healthcare

1. Virtual Bedside Assistant

• A virtual bedside assist empowers hands-free, patient-provider communication
• AI-powered request prioritization and smart routing requests the right resource to meet the patient’s needs
• Can be used by a patient and healthcare workers to call for support
• FAQs, reminders, entertainment, and news - improve the patient experience

2. Telemedicine Chatbots

• Telemedicine chatbots can be used by healthcare providers to diagnose, treat patients, and provide clinical services remotely
• These chatbots can convey the instructions and procedures to follow by patient’s relatives until help arrives.
• NLP-enabled healthcare chatbots can help doctors to retrieve critical information quickly avoiding complex CRM tools
• It helps increase the doctors’ effectiveness in administering the medications

Qdoc is a prescriptive chatbot for telemedicine that mimics a doctor i.e. asks intelligent questions, understands test reports and suggests further investigations.

3. Virtual Waiting Room

• Banner Health operates 30 hospitals and hundreds of clinics across six US states. The patient waiting room posed a social distancing challenge during the pandemic, so a “virtual waiting room” solution from Lifelink Systems was deployed.
• A conversational assistant reaches out to patients in advance of their appointments and guides them through the forms and instructions that are traditionally handled on paper, in person at the clinics.
• Patients submit the forms using interactive mobile messaging and are given appointment logistics support
• Chatbot instructs them to go directly to their exam room when ready, thus bypassing the waiting area completely

4. Pre-op Counselling

• Northwell Health has used Conversational AI to reduce 40% “no-shows” for colonoscopies for colorectal cancer diagnosis
• The personalized chatbot encourages patients by addressing misunderstandings and concerns about the procedure, delivering information in a responsive, conversational way over email or text
• Researchers can monitor patient satisfaction, cancellations, no-shows, and successfully completed exams

5. Home Care

• Coupled with the growth of wearables and IoT devices, conversational AI systems can enable hospitals to care for patients in their homes before they even have a need to visit
• This will free up the care teams who can focus on treatment for the more critical cases and emergencies in the hospital

6. First Point of Contact

• A talking or texting smart chatbot may be the first contact point for primary care
• Patients will not get in touch with physicians or nurses or any medical professional with every one of their health questions but will turn to chatbots first
• If the little medical helper cannot comfortably respond to the raised issues, it will transfer the case to a real-life doctor

7. Urgent Care

• Conversational AI can collect all the necessary patient information and provide it to the doctor immediately and accurately
• In urgent situations, such information is sent to the doctor using a messaging interface
• A study of patients at risk for stroke used AI to put in the details of their symptoms that helped in early detection and informing the doctors urgently. The detection alert was 87.6% accurate.

8. AI-Enabled Counselling

• Conversational AI systems handle the first layer of emotional support
• Use notifications, daily check-ins and gamification of positive habits. The more extreme cases can then be handled by qualified psychiatrists.
• Woebot chatbot uses Cognitive Behavioural Therapy (CBT) techniques to listen and advise
• Woebot offers daily support and techniques to cope with mental wellbeing
• It can be used on Facebook Messenger

9. Triage Chatbots

• Help patients seeking answers when searching their symptoms
• Patients can input their symptoms, answer questions about them and then have medically supported information and resources offer guidance in a curated response

Babylon Health offers AI consultation based on personal medical history and common medical knowledge

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• Users report the symptoms of their illness to the app, which checks them against a database of diseases using speech recognition, and then offers an appropriate course of action.