Opinion

Helping the Health Care Industry Embrace Machine Learning

Machine learning is no longer just a Silicon Valley abstraction — Congress has started paying attention to its potential as a disruptive technology, too.

This month, the House Ways and Means Health Subcommittee hosted a hearing on innovation in the Medicare Advantage space, and emerging technologies such as machine learning took the stage. This new technology not only has the power to transform lives, it’s creating opportunities previously unattainable in health care.

Machine learning, often referred to as artificial intelligence, has the incredible potential to achieve breakthroughs in disease treatments, predictive interventions and clinical decision support. But health care data is overwhelming. It’s often unstructured, unorganized and unholistic.

This has made it difficult to glean insight from using traditional methods, though the data itself has enormous potential. And that’s where machine learning comes in. In fact, a recent study conducted for the Health and Human Services Department acknowledged that machine learning is beginning to play a growing role in “transformative changes” in health — in clinical and nonclinical care settings.

Artificial intelligence is not a replacement for human intelligence — it’s differentiative, additive and complementary. That differentiation is why Congress should encourage the health care industry and the country to welcome machine learning with open arms and continue to support policies that encourage innovation.

It may surprise some how much machine learning is already integrated into our personal lives. Alphabet Inc.’s Google, for example, heavily uses machine learning to improve search results when organizing the web or to sort through our thousands of personal photos to help us find a particular friend or scene from the past more quickly. By sifting through vast amounts of data and constantly adapting its analysis, machine learning delivers humans more information faster.

Fortunately, many members of Congress already recognize the potential of machine learning in the health care field. They support thoughtful policies that encourage innovation and competition and incorporate technology into the best health care practices. Coupled with appropriate privacy and transparency safeguards, these policies can ultimately lead to better health outcomes and lower health care costs.

Now is the time to use these new technologies to our advantage. Technology holds the key to finally cracking many of the problems we’ve long faced in health care.

As it stands, research shows nearly a quarter of hospitalizations in the Medicare-Medicaid populations are potentially avoidable. Among the elderly population in particular, it’s estimated that 10 percent of these hospitalizations are caused by medication non-adherence, an issue with an identifiable intervention if detected.

In all cases, machine learning technologies can improve this status quo by identifying risk and pinpointing outreach and intervention. This technology gives health care practitioners not just more information, but the relevant information they need. The result is the difference between a heart attack and a normal evening at home.

We’ve seen machine learning help in many areas of health care already. For example, a computer can break down MRI images into machine-readable patterns and deliver new insight that helps doctors diagnose and treat issues previously undetected. And Americans agree machine learning in health care is exciting — in one PwC survey, 66 percent of consumers said they believe machine learning has the potential to cure cancer and diseases. This technology is leading to the early detection and treatment of tumors and other medical concerns that will ultimately save millions in health care costs.

During the recent hearing, I had the privilege of sharing Clover Health’s technological capabilities to apply machine learning to help improve outcomes. Clover Health is a health care and technology startup, and our mission is to use data analysis and preventive care to improve health outcomes for Medicare beneficiaries.

Machine learning enables us to analyze immense amounts of data so our nurse practitioners have the information they need to make powerful interventions that can maximize health outcomes. These interventions could come in the form of a timely text message reminding you that you haven’t seen your doctor in some time. More broadly, this technology can help health care providers, insurers and manufacturers focus on individualized care and apply it on a broad scale.

The hearing demonstrated Congress’ commitment to exploring policy changes that not only boost the ability for innovators to impact the market, but also promote competition and improve the consumer experience. Now is the time for leaders to act on that commitment by encouraging policies that allow health insurers and startups to develop and apply machine learning technology to transform the consumer experience and to make an even bigger impact on outcomes and cost.

 

Andrew Toy is the chief technology officer for Clover Health, a health insurance company using technology and data analytics to improve health outcomes and lower health-related costs for Medicare beneficiaries.

Morning Consult welcomes op-ed submissions on policy, politics and business strategy in our coverage areas. Updated submission guidelines can be found here.

Do NOT follow this link or you will be banned from the site!