Our vision is simple. Be a true system of prescriptive insight for Healthcare Revenue Cycle Management, while working within existing data and systems of engagement, adding tremendous value through AI without causing disruption to the claims management and administrative reimbursement processes while doing it for 70% less cost than traditional approaches.
The vision is quite simple. Today’s healthcare revenue cycle departments are overwhelmed with manual processes, costly supporting technology, and overpriced consulting services that purport to deliver"best practice" guidance and analysis, while internal staff is still tasked daily, with making thousands of manual decisions, on time, and without error.
With this problem at hand, we set out to create a scalable and highly repeatable data analysis models, using machine learning algorithms to automatically analyze massive amounts of claim data (i.e., automate the human intelligence function) and provide prescriptive insights and actions for each claim, in seconds. We amplify the impact of our algorithmic approach by leveraging the power of a “community-driven” model using technology-enabled knowledge transfer, where best practice domain knowledge can be shared more efficiently across providers through self-learning algorithms. Just like current community benchmarking, our smart community learns from each other by applying our decision engines in a hub approach that leverages meta-outcome learnings of each provider to drive improved process variation, risk decision, claim recommendation by making the algorithms smarter over time. Better data, better benchmarking and a community of learnings.
Our engines do in seconds, what takes analysts and consultants hours to do.get a demo