Amy Brown, a former healthcare executive, founded Authenticx in 2018 to assist healthcare organizations unlock the potential of customer interaction data. With 20 years of experience within 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 investigate recorded interactions—similar to calls, emails, and chats—providing healthcare organizations with actionable insights to make higher business decisions.
What inspired you to transition from a profession 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 touch center operations, my desire to advocate for people in healthcare became each my passion and mission.
During my time working in insurance and healthcare sectors, I noticed organizations struggling to really understand their customers’ needs through repetitive surveys and robocalls, which frequently led to low response rates and metrics that weren’t reliable.
And that is where Authenticx got here in. By leveraging AI to investigate recorded customer conversations, I noticed healthcare can be extracting helpful insights directly from the voice of the shopper, empowering the industry to really connect with their customers to strategize, invest, and take motion.
How did your personal experiences, especially observing the healthcare system firsthand through your father’s practice and your personal work, shape your vision for Authenticx?
My experience began by understanding how my father served in healthcare through his role as a physician. He has all the time practiced listening and family-centered care, so, to me, he was the exception to the standard friction you frequently hear about. I remember him telling me that it’s the patients’ words that may also help lead you to the answers. And that stuck with me, and it led me to focus my profession and aspirations to improving that system by listening; it is essential.
After I entered healthcare firsthand though government work and call center operations, I saw all the various entities which can be entangled within the healthcare system attempting to make it work. And while this developed my macro view of affecting change, they still weren’t diving into all the information available from the countless interactions shared with customers. I desired to help make clear that ignored conversation data and help organizations improve their customer experience while meeting their outcomes more efficiently and effectively.
As someone and not using a traditional tech background, what challenges did you face while founding an AI company, and the way did you overcome them?
I actually have faced loads of friction along the journey of founding an AI company. Navigating critics was fatiguing, each as a founder and as someone without the technical expertise in creating SaaS technology. Nonetheless, I’ve learned surrounding myself with individuals who complement my knowledge gaps is a strong solution to work together to construct an organization.
After leaving the company world and starting Authenticx, learn how to best approach data aggregation and evaluation were my focus. So, I discovered a partner, Michael Armstrong, who had a powerful background in tech ( and now’s our Chief Technology Officer) and we began to construct out what Authenticx is today.
Authenticx uses AI to investigate 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 best way; it’s human-in-the-loop AI. And we train our models using healthcare-specific data with outputs and insights reviewed by the individuals who understand bias risk, gaps in context, and miscommunication that may create friction from the market and the shopper.
We label the information for sentiment, friction, compliance, antagonistic events, topics, and other metrics and pain points. These labels change into the muse of our AI machine learning and deep learning models. We repeatedly evaluate, test, and retrain the models for an iterative process to construct reliable AI that meets security and governance guidelines.
What’s the ‘Eddy Effect,’ and the way 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 shopper experience journey. Like an eddy in a river, similar to a big rock causing water to swirl, the Eddy Effect™ gives insights into what causes that frustrating loop for patrons. It helps discover disruptions and obstacles which can be a barrier (or the big rock) to making a positive experience.
The outcomes of the Eddy Effect™ AI model are illuminated inside dashboards spotlighting various signals of friction present in conversation data. And it’s on these dashboards that common metrics, similar to call length, sentiment, accuracy, and estimated waste costs are monitored for customer friction. As an example, 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 cut back the presence of identified friction by 10%.
How does Authenticx make sure that its AI models provide insights that genuinely improve patient care and reduce friction within the healthcare system?
We prioritize conversational data evaluation, which provides helpful insights into customer interactions and uncovers vital issues and opportunities that could be missed by other data sources. Authenticx employs GenAI models to simplify complex and nuanced data and supply actionable recommendations specifically for healthcare. Our reporting tools offer a consumable view of performance metrics and trends. Our built-in workflows allow users to reply timely.
Most of all, we’ve 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 information scientists and analysts ensuring the reliability of the insights organizations receive, our human-in-the-loop approach helps to alleviate risks, biases, and misinformation to enhance AI accuracy.
What role does AI play in addressing operational inefficiencies, and the way does it help healthcare organizations discover and resolve broken processes?
When you possibly can take heed to a call and cut through the noise to know the context of the pain point, the more likely you might be to discover significant issues which can be the basis explanation for a broken process. When the basis cause is found, organizations can strategize with a data-driven approach to take a position in resources and effectively erase the guesswork for an efficient resolution.
AI is a tool that may be used to synthesize large amounts of information to discover, quantify and trend operational inefficiencies and broken processes at scale.
We had a customer leverage Authenticx to discover what was the reason for patient confusion of 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, leading to their call intake being reduced by about 550 calls over two months, saving time and resources.
Are you able to share an example of how Authenticx’s AI has transformed a healthcare provider’s operations or patient outcomes?
Authenticx helped a regional hospital system discover the leading drivers of friction inside its central scheduling process. Callers were getting stuck while looking for medical advice, there was an absence of visibility into specialty processes once the agent transferred the decision, and repeated frustration of the shortcoming to schedule an appointment quickly.
Authenticx AI activated a full-volume evaluation of calls to discover the precise barriers and supply insights to teach agents, highlighting ways to enhance their quality initiatives. Inside 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 make sure ethical and reliable deployment and implementation of AI. Our platform mirrors that approach: An AI and human evaluation that gives feedback about customer experience, operational performance, compliance, and more.
While our in-house team works in any respect levels of the platform, our GenAI models are trained with healthcare-specific data to supply insights similar to context-rich summaries, topic aggregation, and automatic coaching notes, and we’ve announced an integrated AI assistant that gives meaningful insights immediately.
How do you see AI transforming healthcare in the subsequent five years, and what role will Authenticx play in that transformation?
The following five years of healthcare AI might be revolutionary. The impact that AI is having on the earth is already significant, so having more data, insights, security, and governance established will result in more precision and efficiency for predictive technologies, the worker and customer experience, and advanced ways to observe care that ultimately improves your complete healthcare system.
Continuing to take heed to improve, revise, and create models will help healthcare and patient care progress positively. This impact will come from industry-specificity in developing latest AI tools and models – and we’re enthusiastic about it.
