Machine Learning: The Future for Health Plans
By: Alex Meshkin
Health plans are struggling to become more efficient and competitive in the marketplace. Beyond needing to be financially viable; they need to provide the best healthcare options for their customers. The future success of health plans, and for healthcare, will be the adoption of intelligent systems where automation and machine learning intersect. In order for health plans to flourish in this ever-changing environment — I believe that they must put machine learning at the core of every aspect of their business.
At the recent Harvard Business School Healthcare Conference, Adam Koppel, Biogen’s vice president of corporate development and strategy, spoke about his company’s partnership with Verily (Google’s life sciences arm), in which they will use machine learning to understand how multiple sclerosis presents itself differently from patient to patient. Koppel shared his conclusions drawn from discussions and data sharing with Google.
Koppel said Google recognizes that “current payers [are] using actuarial math — it’s like the 19th century.” Koppel went on to say, “They [Google] want to take over CMS…and they will say that to you.”
While there may be some hyperbole here, I do believe Google sees the tremendous opportunity of applying its world-class machine learning expertise to solving the country’s most important and challenging problem — delivering high-quality, cost-effective healthcare.
What is machine learning and why will it make a difference?
Machine learning algorithms continuously learn and are capable of making data-driven decisions. These algorithms discover breakthroughs in inference and understanding of cause-and-effect allowing payers to personalize decision-making (e.g. formulary). Consider a population of patients and everything that is known about them – including demographics, medical history, all the factors needed to understand a member’s risk through predictive analytics. Add in machine learning capabilities that incorporate intervention models and you have a system that allows you to select the best care pathway for each person based on their own unique history and health status. This is the holy grail of care management and utilization management.
The likely end result of machine-learning-guided care decision-making? Billions of dollars in savings achieved by driving effective and efficient interventions that will have the best outcomes.
Do we really need to wait for tech giants like Google to fix healthcare?
Health insurance startups like Clover Health and Oscar Health see this same huge opportunity, and are designing new health plans around the use of data to improve the care of members and reduce the cost of care. Oscar Health, like other ACA Commercial Exchange (HIX) health plans, has struggled to stay financially viable. Its success will be dependent on the creation of intelligent platforms that better manage complex cases, proactively engage members to reduce costs and more accurately identify the acuity of member risk to drive increased risk adjustment revenue.
All major health plan operations will be transformed by machine learning. Whether modernizing utilization management to determine the best course of treatment, expanding analysis of gaps in care beyond quality measurements (e.g. HEDIS) or moving from retrospective risk adjustment to predictive identification of undiagnosed conditions (e.g. HCC Optimization), payers will be one of the largest and most important groups of consumers of machine learning in health care. As the traditional lines between payers and providers are blurred, ACOs and other risk-bearing provider organizations will be among the payers using these tools.
Incumbent payers adopting machine learning will have the advantage over newcomers using machine learning. Incumbent payers have data stores that span decades, and in some cases billions of historical care events — the lifeblood of machine learning. With the right partners and the right strategy, incumbent payers can leverage this experience, this data, in order to improve the lives of all Americans.
Payers are at a crossroads where machine learning intersects with the status quo — those who fail to adopt data-driven intelligent systems will see their fate follow the same path as media companies diminished by Google and Facebook.
But we at Flow Health are optimistic. We are partnering with one of the largest payers in the nation to change the future of healthcare. Their leadership is squarely focused on transforming their business. At Flow Health, we are building the Operating System for Value-Based Care that leverages machine learning to help health plans and providers individualize care plans, automate workflows across the continuum of care and improve care coordination. The power of the operating system, coupled with the depth of our partners’ data stores, will drive this change.
Thank you for taking the time to learn about Flow Health . Flow Health is presenting at HIMSS 2016 Venture+ forum. We invite you to see all of our #HIMSS16 posts. If you would like to know more about Flow Health and how we can help your organization succeed, please contact us today at email@example.com.