Written by 
Amy Brown, a former healthcare executive, founded Authenticx in 2018 to help healthcare organizations unlock the potential of customer interaction data. With two decades of experience in the healthcare and insurance industries, she saw the missed opportunities in using customer conversations to drive business growth and improve profitability.
Authenticx addresses this gap by utilizing AI and natural language processing to analyze recorded interactions—such as calls, emails, and chats—providing healthcare organizations with actionable insights to make better business decisions.
What inspired you to transition from a career in healthcare operations and social work to founding Authenticx, a tech-driven AI company?
With my educational background in social work and my 20-year work experience in contact center operations, my desire to advocate for individuals in healthcare became both my passion and mission.
During my time working in insurance and healthcare sectors, I noticed organizations struggling to truly understand their customers’ needs through repetitive surveys and robocalls, which often led to low response rates and metrics that were not reliable.
And that’s where Authenticx came in. By leveraging AI to analyze recorded customer conversations, I realized healthcare would be extracting valuable insights directly from the voice of the customer, empowering the industry to truly connect with their customers to strategize, invest, and take action.
How did your personal experiences, especially observing the healthcare system firsthand through your father’s practice and your own work, shape your vision for Authenticx?
My experience started by understanding how my father served in healthcare through his role as a physician. He has always practiced listening and family-centered care, so, to me, he was the exception to the typical friction you often hear about. I remember him telling me that it is the patients’ words that can help lead you to the answers. And that stuck with me, and it led me to focus my career and aspirations to improving that system by listening; it is key.
When I entered healthcare firsthand though government work and contact center operations, I saw all the different entities that are entangled in the healthcare system trying to make it work. And while this developed my macro view of affecting change, they still weren’t diving into all the data available from the countless interactions shared with customers. I wanted to help shed light on that ignored conversation data and help organizations improve their customer experience while meeting their outcomes more efficiently and effectively.
As someone without a traditional tech background, what challenges did you face while founding an AI company, and how did you overcome them?
I have faced plenty of friction along the journey of founding an AI company. Navigating critics was fatiguing, both as a founder and as someone without the technical expertise in creating SaaS technology. However, I’ve learned surrounding myself with individuals who complement my knowledge gaps is a powerful way to work together to build a company.
After leaving the corporate world and starting Authenticx, how to best approach data aggregation and analysis were my focus. So, I found a partner, Michael Armstrong, who had an impressive background in tech ( and now is our Chief Technology Officer) and we began to build out what Authenticx is today.
Authenticx uses AI to analyze healthcare conversations. Could you walk us through how your AI models are specifically tailored for healthcare and what makes them unique?
Authenticx’s models are built by and for healthcare. We approach our models with experts from healthcare, social work, and tech involved every step of the way; it is human-in-the-loop AI. And we train our models using healthcare-specific data with outputs and insights reviewed by the people who understand bias risk, gaps in context, and miscommunication that can create friction from the market and the customer.
We label the data for sentiment, friction, compliance, adverse events, topics, and other metrics and pain points. These labels become the foundation of our AI machine learning and deep learning models. We continuously evaluate, test, and retrain the models for an iterative process to build reliable AI that meets security and governance guidelines.
What is the ‘Eddy Effect,’ and how does your AI platform help healthcare organizations address this issue?
The Eddy Effect™ is our proprietary Machine Learning model and metric, which identifies and measures customer friction in the customer experience journey. Like an eddy in a river, such as a large rock causing water to swirl, the Eddy Effect™ gives insights into what causes that frustrating loop for customers. It helps identify disruptions and obstacles that are a barrier (or the large rock) to creating a positive experience.
The results of the Eddy Effect™ AI model are illuminated within dashboards spotlighting various signals of friction found in conversation data. And it is on these dashboards that common metrics, such as call length, sentiment, accuracy, and estimated waste costs are monitored for customer friction. For instance, we had a client that lacked insight into quality and pain points from their third-party contact center. With Authenticx, they targeted friction points, themes and topics, and quality to reduce the presence of identified friction by 10%.
How does Authenticx ensure that its AI models provide insights that genuinely improve patient care and reduce friction in the healthcare system?
We prioritize conversational data analysis, which provides valuable insights into customer interactions and uncovers important issues and opportunities that may be overlooked by other data sources. Authenticx employs GenAI models to simplify complex and nuanced data and provide actionable recommendations specifically for healthcare. Our reporting tools offer a consumable view of performance metrics and trends. Our built-in workflows allow users to respond timely.
Most of all, we have a consistent review of our models and their outputs. From our Customer Advisory Board providing feedback on our product, and our in-house team of data scientists and analysts ensuring the reliability of the insights organizations receive, our human-in-the-loop approach helps to alleviate risks, biases, and incorrect information to improve AI accuracy.
What role does AI play in addressing operational inefficiencies, and how does it help healthcare organizations identify and resolve broken processes?
When you can listen to a call and cut through the noise to understand the context of the pain point, the more likely you are to identify significant issues that are the root cause of a broken process. When the root cause is found, organizations can strategize with a data-driven approach to invest in resources and effectively erase the guesswork for an efficient resolution.
AI is a tool that can be used to synthesize large amounts of data to identify, quantify and trend operational inefficiencies and broken processes at scale.
We had a customer leverage Authenticx to identify what was causing patient confusion in their prescription inquiry process, making up 20% of their calls. With insights into root causes of refill friction, they restructured their phone tree and revised agent prompts, resulting in their call intake being reduced by about 550 calls over two months, saving time and resources.
Can you share an example of how Authenticx’s AI has transformed a healthcare provider’s operations or patient outcomes?
Authenticx helped a regional hospital system identify the leading drivers of friction within its central scheduling process. Callers were getting stuck while seeking medical advice, there was a lack of visibility into specialty processes once the agent transferred the call, and repeated frustration of the inability to schedule an appointment quickly.
Authenticx AI activated a full-volume analysis of calls to identify the specific barriers and provide insights to coach agents, highlighting ways to improve their quality initiatives. Within two months, their team increased agent quality skills by 12%, used Authenticx insights to predict future friction points, and proactively addressed them.
How does Authenticx’s AI augment human decision-making, and what role do healthcare professionals play in refining the AI models?
We practice a human-in-the-loop approach to ensure ethical and reliable deployment and implementation of AI. Our platform mirrors that approach: An AI and human analysis that provides feedback about customer experience, operational performance, compliance, and more.
While our in-house team works at all levels of the platform, our GenAI models are trained with healthcare-specific data to produce insights such as context-rich summaries, topic aggregation, and automated coaching notes, and we’ve announced an integrated AI assistant that provides meaningful insights instantly.
How do you see AI transforming healthcare in the next five years, and what role will Authenticx play in that transformation?
The next five years of healthcare AI will be revolutionary. The impact that AI is having in the world is already significant, so having more data, insights, security, and governance established will lead to more precision and efficiency for predictive technologies, the employee and customer experience, and advanced ways to monitor care that ultimately improves the entire healthcare system.
Continuing to listen to improve, revise, and create models will help healthcare and patient care progress positively. This impact will come from industry-specificity in developing new AI tools and models – and we’re excited about it.
Thank you for the great interview, readers who wish to learn more should visit Authenticx.
