AICybersecurityHealthLatest News

How AI Tools Like Conversational Intelligence Improve Healthcare Customer Journeys

By Dataversity – California

According to a recent report, a continuous loop of disruptions impacts 20% of customer interactions in healthcare, with nearly half of these disruptions delaying or preventing patient care. However, organizations using conversational intelligence to listen to and analyze the voice of the customer (VOC) are realizing benefits, citing a 25% increase in first-call resolution rates and a 10% decrease in customer churn.

Conversational intelligence – and listening-focused AI – have the potential to significantly improve customer interactions and the delivery of timely responses to common questions by helping leaders identify systemic issues and provide more seamless, frustration-free experiences.

Conversational Intelligence Holds the Key to Understanding Customer Frustrations

Let’s talk data for a minute. There are two types of data: structured and unstructured data. Structured data has a high degree of organization and a predefined data model or schema. It lives in relational databases with predefined fields that enforce data types. Structured data includes numbers, dates, and categorical values that are easy for computers to process.

Unstructured data lacks definitive organization and is generally stored in non-relational databases or file systems. It includes audio, video, social media posts, and text – and it’s difficult for computers to process and extract insights from. It’s also the type of data into which conversations fall.

But while it’s critically important, containing real-time insights and valuable customer feedback, unstructured data is often underutilized because:

  • It’s difficult to process.
  • The tools for managing and gaining insights from it are still evolving.
  • It’s harder to maintain Data Quality and avoid bias with its inherent ambiguity.
  • It often contains sensitive personal information requiring additional stringent security measures.

Healthcare organizations rely on both types of data, collecting and analyzing a tremendous amount of demographic, diagnostic, financial, and other sources of patient data. Every year, the average hospital generates approximately 50 petabytes of information – a volume more than double that of the Library of Congress, translating to 137 terabytes of data created daily.

This volume of data makes it impossible for human teams to review, analyze, and pull insights substantive enough to understand customers and predict health and business outcomes.

Forward-thinking organizations are adopting AI to help their systems do more with the data they collect and to address significant pain points. Common challenges stem from:

  • Patient confusion about processes or procedures
  • Communication issues among humans involved in an interaction
  • Technology breakdowns

What’s worse, customers can experience multiple disruptions in a single interaction. Here’s a common scenario:

Donna contacts her healthcare provider to ask about an insurance copay charge. First, she connects online via chat, but she struggles to articulate her concern and the agent isn’t able to answer her questions. Finally, she phonesthe company, but her call is plagued by a series of unfortunate disruptions; she struggles to connect to a live agent and has a lengthy wait time. She hangs up, thinking she’ll try again later, but when she does, she encounters another long wait. Then she runs into a malfunctioning automated menu.

When she finally reaches an agent, the person isn’t familiar with the specific insurer and transfers her to a different department. The call is accidentally disconnected and Donna has to call back a third time. One agent provides Donna with incorrect information, leading to further frustration and wasted time. Another agent gives conflicting information about the charge, leaving Donna confused and uncertain.

Throughout the call, agents display a lack of empathy and understanding about Donna’s concerns. They use technical jargon and unclear language, making it difficult for her to follow the process. After spending an hour on the phone and being transferred multiple times, Donna’s no closer to getting any answers. The last agent she speaks with tells her that she must call a different number.

The result? Donna hangs up feeling frustrated, helpless, and angry. Multiple disruptions and lack of a resolution leave her with a negative impression of the call center and her insurance company. Consequently, Donna leaves her doctor and goes to a new health system the following month.

This scenario highlights the potential for multiple disruptions to compound and create a frustrating loop of obstacles that:

  • Impact the customer service experience.
  • Emphasize the importance of investing in the right technology to analyze and pull insights from these conversations.

What AI Can Do

AI is a powerful tool that provides insight into customer satisfaction and helps agents better understand customer needs and respond with empathy and personalized solutions.

AI surfaces recurring themes, areas of frustration, opportunities for improvement, and other insights. Stories taken directly from customers’ own voices show key decision-makers at the executive level exactly what’s happening. Storytelling connects heads and hearts, inciting change because leaders can’t unhear what they’ve heard.

AI enables data-backed storytelling to capture and personalize challenges by combining quantitative and qualitative data to provide meaning, driving enterprise impact via:

  • Context: Listening at scale to understand the meaning behind and value of customer conversations, identify root causes, and strategically plan for future needs, growth, and outcomes.
  • Comprehension: Data discovery and synthesis derive from understanding the threats, frustrations, and opportunities customers face.
  • Connection: Integrating the voice of the customer with micro- and macro-business decisions with a relevant data source representative of the customer demographics leads to more meaningful, informed decision-making.

Best Practices for Implementing AI Successfully

Here’s how to implement AI effectively in your organization now – and in the future.

  • Review your existing tools to determine what your organization uses the most, who has expertise, and how your people collaborate, because AI can help improve process efficiencies.
  • Collect diverse, high-quality, industry-specific training data, because the AI’s only as good as the data it’s trained on. Aim for variety in speakers, accents, context, etc., to improve its robustness and accuracy.
  • Continuously improve through iterative training. As you get more user data, retrain models periodically to improve their performance over time. Measure speech recognition and intent classification accuracy using set tests and monitor metrics to check if and when retraining is needed.
  • Plan for security and privacy, anonymizing data, encrypting audio in transit and rest, and carefully controlling access to training data. Stay up-to-date on all regulations to ensure compliance.

Remember: AI can elevate the work of humans. Ensure your teams understand its value in parsing conversational data to pull insights to tell the stories guiding the organization’s decision-making.

How companies build, incorporate, and use AI matters because it’s leading the way and driving change within the healthcare industry. The future holds myriad opportunities to transform healthcare through the power of conversation, data storytelling, and listening at scale.

Today we live in a T-shaped world. While broad knowledge across the ecosystems is critical, deep insights and expertise of Subject Matter Experts help organizations leapfrog. At IndiaTechnologyNews, we cover much more than news, views and analysis, and we feature SMEs to help translate their knowledge to wider audiences. Reach me at

You may also like

More in AI