Unlocking Patient Insights with Conversational AI in Healthcare

Healthcare leaders should champion human-AI collaboration by having people work with AI to validate insights and develop engagement strategies. Organizations can — and should — implement conversational AI because it allows leaders to identify actionable areas of change and protects individuals’ privacy and identities. 

Yet while these organizations recognize the merit of sharing these conversations enterprise-wide, many worry that doing so will compromise data privacy and compliance. But developments in conversational artificial intelligence (AI) can safely bring the voice of the customer to the forefront. 

Listening for better business decisions

Unstructured data comprises at least 80% of data, and it’s growing at a breakneck speed of 55% to 65% YOY. This massive volume, which includes conversational data, makes it impossible for humans alone to extract meaningful insights. AI, however, enables businesses to listen at scale, quickly identify trends and make data-driven decisions to improve the customer experience (CX).

By leveraging AI to analyze customer feedback from calls, chats, emails and other sources, healthcare organizations can:

• Gain insights into successes and pain points in CX and the customer journey.
• Improve customer and employee satisfaction.
• Increase profitability.

Why? Because customer feedback provides real-time insights, allowing businesses to rapidly adjust and improve. Talking directly to customers helps organizations understand their lived experiences. 

Consider this scenario: A customer calls their insurance company with a question about their vision bill. However, the insurance company outsources its vision and dental payments to an external vendor. Calling the 800 number on the back of the card doesn’t guarantee a customer will connect to an agent equipped to answer their question. Sure enough, the agent puts the caller on hold to consult with a business colleague. After several back-and-forth conversations, the caller finally gets their answer. These unspoken customer friction points present an opportunity for organizational improvement. However, without reviewing the recordings, leaders can’t learn from their mistakes or implement better communication strategies.

Using AI to listen promotes empathy and understanding, which empowers leaders to make better business decisions for the organization and its customers.

Putting AI to work

Data-driven insights from AI lead to better customer experiences, retention and business key performance indicators (KPIs). AI augmentation enables more accurate, granular and faster analysis of customer conversations at scale to surface actionable insights. Training AI on call center scripts, support chats, social posts, surveys and other patient-specific narratives yields much better results than AI trained on more general datasets.

Simulation of various customer scenarios — and role-playing during agent training — enables interactive prototyping of situations, scripts and processes; facilitates executive decision-making by helping to surface and develop resolutions for areas of concern; and maximizes a positive CX.

When trained on industry-specific datasets consistently and used to truly capture customer voices, AI offers incredible benefits. In addition to scaling more cost-effectively than hiring more people to analyze conversations, AI:

• Processes vast amounts of conversational data faster than humans to capture real-time insights and automatically feeds insights it’s derived into downstream systems like CRMs.
• Applies consistent logic to analyze conversations neutrally versus humans, who have innate biases and variability.
• Extracts and correlates insights across dimensions, like intents, keywords, sentiment and specific topics.
• Detects nuanced patterns and relationships among words and phrases that humans may miss. 
• Leverages machine learning (ML) to analyze convos in different languages, analyzing audio, emotional, lexical, semantic and text features to generate a more holistic view of patient sentiment.

Protecting customer data 

While data and privacy concerns exist, you can take steps to ensure security controls. Start by using enough data to generate useful insights while implementing safeguards to mitigate privacy risks. Doing so requires ongoing governance and responsible data practices — and AI enables the collection and analysis of much more data than a human team.

Obtain proper consent, with callers explicitly agreeing to the call recording and analysis. Consent language should clearly explain how the organization will use and share the data. Prior to sharing results externally or internally, the organization should use techniques like hashing or tokenization to anonymize data, remove all personal identifiers from call transcripts and data sets, and set strict access controls that are compliant with HIPAA or PCI DSS protocols.

Maintain oversight with an executive privacy officer and cross-disciplinary team accountable for ensuring responsible data practices across the analysis pipeline, insight use and stakeholder reporting of findings. By instilling proper governance throughout the entire lifecycle of unstructured customer data, healthcare organizations build trust while unlocking powerful insights from AI-based conversational analytics. 

The next era of customer listening with AI in healthcare

A few years ago, no one was talking about conversational AI. But there’s been a dramatic shift in the industry, where contact and call centers are transforming into insight centers for enterprises and organizational leaders.

We have much to gain with our ability to listen more effectively at scale — to catch risks before they become a reality and provide a safety net for our systems of care so they don’t fail our customers. We don’t have to choose between customer centricity or business metrics and KPIs because we can have both.

Listening at scale allows us to sort through the totality of data, understanding what’s in our control versus what isn’t — and devoting our resources to fixing what we can. Listening at scale means we hear the problems and the good happening within our organizations. 

Humans are creative and imaginative and able to think outside the box, whereas AI excels at logical analysis and computation. When paired, they balance one another and can generate global and local insights to inform decisions and ideas.

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About Eric Prugh 

Eric Prugh is the Chief Product Officer at Authenticx and leads product strategy, design, and product marketing. Eric has spent more than 15+ years building and scaling software companies in go-to-market, product, and international functions. Prior to Authenticx, Eric was Co-founder and Chief Product Officer at PactSafe, a platform that powered over 1 billion online contracts for companies like Wayfair, DoorDash, Orangetheory Fitness, Dell, Upwork, and more. Eric also was a leader at ExactTarget, a marketing technology giant in Indianapolis that sold to Salesforce in 2013.