What is conversational AI?
Conversational AI is an advanced technology that allows machines to understand, process, and respond to human language in a way that is both contextually relevant and interactive.
Within the healthcare sector, this technology is increasingly deployed to automate routine tasks and deliver a more robust and personalized patient experience. Conversational AI leverages a combination of machine learning, natural language processing, and occasionally deep learning to interpret and respond to user inputs, with a conversational user interface that mimics human interaction. Digital health companies deploy conversational AI through chatbots, voice assistants, messaging apps, or any other type of platform where natural language interactions occur.
Conversational AI solution types
|Pre-care interaction||Solves for important pre-care interactions including symptom search, appointment scheduling, and health information.|
|Care process||Supports symptom checking, patient education, and communication between patients and care providers.|
|Post-care management||Promotes effective post-care management with follow-up appointment scheduling, reminders, and care plan adherence support.|
|Enduring relationship||Provides ongoing care plan education, monitors symptoms, manages care gaps, and delivers patient surveys.|
The case for conversational AI
Demand frequently outstrips available resources within the healthcare sector. A 2022 Beryl Institute poll found that 76 percent of Americans had not had a positive healthcare experience during the previous three months, and 60 percent of Americans had negative experiences. And according to a report from digital health company Cedar, 28 percent of patients had changed providers during the previous year due to poor digital health experiences.
Conversational AI offers opportunities to dramatically improve low satisfaction rates and reduce negative experiences through timely and personalized patient interactions. These real-time tailored conversations can assist patients throughout their care journeys, from pre-care to follow-ups and ongoing engagement, significantly improving the patient experience. In addition, automated tasks reduce workload on care teams and help health systems scale their services more effectively.
How conversational AI can drive value
|Improved patient acquisition||• Helps patients access information on demand
• Provides personalized recommendations and content to meet their needs
• Rapidly scales to accommodate new patients in a cost-effective manner
|Reduced cost to serve||• Automated interactions can decrease labor costs related to patient questions and messages
• Algorithms provide cost-effective continuous improvement
|Reduced churn||• Supports patients with personalized interactions on demand|
|Improved employee satisfaction||• Automates routine tasks and relieves care team burdens
• Delivers intelligent support for clinical interactions
|Increased per-patient revenue||• Continuously gathers information and analyzes patient preferences to refine interactions and recommendations|
Key attributes of conversational AI solutions
Health systems should prioritize these capabilities when they invest in conversational AI solutions:
Contextual understanding: The solution should understand and respond to patient inputs in a manner that takes the context into account, providing relevant and accurate information or advice.
Interoperability: Conversational AI solutions should seamlessly integrate with the EHR and other existing systems, databases, and digital channels.
Data security and privacy: Solutions must protect patient privacy and comply with HIPAA and all other applicable regulations.
Naturalistic interaction: The user experience should mimic human conversation and include an easy, intuitive interface.
Scalability: The solution should be capable of handling increasing volumes of interactions as use cases expand and adoption grows.
Adaptability: A good solution should keep pace with rapidly evolving AI technology and continuously improve over time.
Organizing for success with conversational AI
What health systems should consider as they assess their needs and investigate conversational AI solutions:
- Leadership buy-in. Leadership across the enterprise should understand the value of AI and the importance of investing in the right technology and driving a patient-focused culture.
- Cross-functional teams: Successful incorporation of conversational AI into healthcare operations often requires a cross-functional team of IT professionals, healthcare providers, and data scientists or AI specialists (whenever possible).
- Training and change management: As with any new technology, there will be a learning curve. Successful adoption depends on adequate training and support for staff to manage the change effectively.
- Integration strategy: Small projects or pilots can help health systems learn, adapt, and scale, delivering valuable insights to aid with robust implementation strategies.
- Patient engagement: At its heart, conversational AI is a tool to enhance the patient experience. Regularly gathering patient feedback and making necessary adjustments is key to ensure the technology meets their evolving needs.