Empowered Patient Podcast Unlocking the Power of Conversational Intelligence for Healthcare Stakeholders with Amy Brown Authenticx
Amy Brown, CEO of Authenticx, uses conversational intelligence to analyze conversations between humans and between humans and chatbots to gain insights that can drive improvements in patient experience, operational efficiency, and faster resolution of problems in the healthcare environment. Using AI and trained professionals, the Authenticx solution helps pharmaceutical companies improve drug launches, personalize customer interactions and respond more effectively to patient needs. In clinical situations, the machine learning models help identify and address obstacles impeding more effective patient/provider/payer relationships.
Amy explains, "At Authenticx, we really target bidirectional conversations. We can take in all types of unstructured data that exist: notes, social, etc. That said, we find the most insightful and contextually rich types of data sources are the ones that represent a back-and-forth between customer and company. And in our world, we're working with very large healthcare companies, like pharmaceutical manufacturers, health insurance companies, health and hospital systems."
"They're typically having conversations with patients or caregivers, any type of healthcare consumer, they're also having conversations with other stakeholders in the healthcare system, like insurance companies talking to providers' offices, or doctors' offices talking to pharmaceutical manufacturers. We find that the research and study of that back-and-forth bidirectional communication, and doing that at scale, is incredibly insightful to the business."
"We ingest the call recordings or the chat interaction files into our platform, and we apply AI to listen for the things that matter most to healthcare companies. We've spent five years curating our own proprietary training data, where we have listeners, human listeners, who have listened to hundreds of thousands of conversations. We intentionally hired social workers, healthcare workers, nurses, people who have worked inside the healthcare system, because we found that interpreting the communication they're listening to is so crucially important to making high-quality AI."
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