Healthcare conversations are different. The stakes are higher, the system is complicated, and compliance is critical. In an era where healthcare organizations must compete on experience to retain customers, industry-agnostic speech analytics tools, AI, and machine learning models won’t cut it.
Machine learning is a form of AI in which computers are able to predict, learn and improve the AI’s own accuracy over time, by continuing to ingest training data. Machine learning models must be trained to provide value. And models are trained by the data that feeds them.
Many industries rely upon models that were trained using non-industry-specific data. These models are trained by large and often publicly available data sets that are not specific to a particular use case. While these public data sets might share the same language (i.e. English), the intent or meaning behind the words may be vastly different because of situational differences. Another frequent tactic used by companies who build AI models, is to hire offshore data-labelers to interpret unstructured data. If the offshore personnel do not sufficiently understand the language or cultural and situational context of the data, important nuances get lost, which can lead to:
- Inaccurate interpretations of meaning
- Distrust of the models to accurately predict reality
- User disengagement and lack of reliance on the AI to solve real problems
Healthcare organizations need a conversational intelligence solution that was built specifically for the industry because healthcare-specific AI:
- Extracts the most value from healthcare conversations because it was trained by healthcare-only conversations
- Has a deep understanding of the regulatory nature of healthcare
- Builds ML models that matter most to the industry, bringing speed and value to healthcare organizations
Ideally, healthcare technology companies utilize humans who have deep experience in the healthcare industry to label their training data. Qualified personnel will be able to accurately interpret the language, and more effectively interpret meaning and intent.
Healthcare-specific AI is a competitive advantage for healthcare organizations looking to improve customer experience, quality, and compliance. Here are a few examples of how leading enterprises are capitalizing on this technology:
- To understand why there was an influx in calls from HCPs
- To improve billing statements and reduce calls
- To understand drivers of long calls
- To increase patient adherence and product device utilization
- To improve accessibility to mental health resources for patients
See Authenticx in Action
Learn more about how Authenticx analyzes customer conversations to surface recurring trends in this two-minute video.
Authenticx was founded to analyze and activate customer interaction data at scale. Why? We wanted to reveal transformational opportunities in healthcare. We are on a mission to help humans understand humans. With a combined 100+ years of leadership experience in pharma, payer, and healthcare organizations, we know first-hand the challenges and opportunities that our clients face because we’ve been in your shoes.
Want to learn more? Contact us!