Last month, Discovery CEO Jason Brown shared his thoughts on 2021 payment integrity market trends. Today we expand on one of those trends: new data analysis technologies. Read Jason’s blog here.
Innovations in data analysis technologies have become transformative in many industries across the globe. Artificial intelligence and machine learning enable organizations in utilities, financial services, healthcare, and other industries to analyze and understand their data in ways we never thought possible. In healthcare, these technologies create new efficiencies, unlocking opportunities to eliminate waste, reduce costs, and make healthcare more affordable for all.
Artificial intelligence and machine learning in healthcare
Experts predict that AI will play an increasingly prominent role in healthcare. In fact, research shows that artificial intelligence has the potential to improve patient outcomes by 30-40% while reducing treatment costs by as much as 50% in the next seven to ten years.
Advances in computing power, learning algorithms, and the availability of large data sets enable healthcare leaders to improve quality care and reduce costs. AI is expected to have a significant impact on both chronic disease management and administrative complexity.
As the current COVID pandemic rages on, AI can assist clinicians in telehealth to remotely monitor patients’ vital signs, enabling better care regardless of where they live.
Machine learning in premium restoration
A subset of artificial intelligence, machine learning holds great promise for premium restoration data analysis. Machine learning offers a method of data analysis that automates analytical model building. The technology identifies patterns in data and makes predictions based on that data.
For Medicare Advantage plans, predictive analytics can help correct inaccuracies in Centers for Medicare and Medicaid Services (CMS) eligibility data. By identifying patterns in the data, the technology can efficiently identify primacy changes in Medicare Secondary Payer scenarios. It can also more accurately identify members with End-Stage Renal Disease.
The balance of human intervention and machine learning
AI and machine learning have the potential to forever change the way we identify patterns in data, but human intervention is still vital. While the universe of everything the machine learning model knows is contained within the data, subject matter experts have a vast background of contextual information and the ability to deduce causes of observed patterns. Human maintenance is required for changing data structures, new data sets, changing business dynamics, and ongoing validation.
Machine learning really shines when the limitations of the human brain take effect. Machine learning has the ability to analyze data at scale, finding patterns in millions or billions of data points over hundreds of dimensions. The vast and ever-expanding data provides a vast knowledge bank that ML models can continually learn from, exploit, and iterate—all on a computer’s time frame (not a human’s). The patterns that the models find can be more encompassing than human-derived rules, and the data-driven decisions the models make can be quantitatively optimal, thereby eliminating bias, increasing precision, and improving coverage of possible cases.
Ultimately, this increase in true positives and the reduction in false positives drives value for our clients.
The best of both in premium restoration
Achieving the greatest success in premium restoration involves both the human brain and machine learning. Like an expert teacher providing hints to a student, subject matter experts can exploit their vast contextual background knowledge and can guide the machine to the most relevant data points and considerations to monitor during MSP and ESRD identification.
Machine learning can then extract patterns to make optimal MSP and ESRD decisions—and do so much more quickly and expertly than a human being can, simultaneously analyzing hundreds of considerations per potential case and quickly looking through millions of new data points. The machine then continues to learn from the growing dataset, more readily adapting to changes in the data and identifying new patterns on its own.
Machine learning at Discovery
Discovery Health Partners has made great inroads in the advancement of data analysis technologies like machine learning. We combine that with many decades of experience and subject matter expertise. Ultimately, we offer the best of both machine power and human power to help Medicare Advantage plans ensure the accuracy of premiums paid by CMS.