Earlier this summer, the landmark decision by The Supreme Court overruling the Chevron deference generated ripple effects across industries. The decision significantly impacts the latitude and autonomy federal agencies have had to define, interpret, and enforce regulations. In healthcare, the milestone serves as an opportunity to take stock of what has been accomplished so far with big data and what prospects look like ahead in this new regulatory landscape.
From the outset, the new ruling ushers in added complexity to a healthcare AI regulatory framework that was already murky, particularly around how advanced technologies like AI can improve healthcare by analyzing enormously large sets of healthcare data (or so-called “big data”). The ensuing debate on AI regulation has largely missed the point of its potential to aid in transforming American healthcare for the better.
Spoiler alert: Big data won’t solve healthcare’s challenges. Here’s why: For decades, we have been collecting and analyzing massive amounts of healthcare data, hoping to improve health outcomes, reduce costs, and achieve health equity. In fact, this year marks the 15th anniversary of the HITECH Act, a $27 billion program of federal incentives to spur the adoption of electronic medical records, creating vast pools of health data with the potential to improve care. Alongside that, pharmaceutical companies, insurance claims, wearable devices, and other sources have also been contributing rich troves of big data.
Years later, what do we have to show for it? Not much in improved health outcomes. The United States remains the most expensive health system in the world. Despite the high spending, Americans experience some of the worst health outcomes overall among high-income nations. We have the lowest life expectancy at birth, the highest death rates for avoidable or treatable conditions, and the highest maternal and infant mortality. The U.S. also has the highest rate of people with multiple chronic conditions and an obesity rate nearly twice the average for other wealthy nations.
Where do we go from there? Real transformation and change are continually hamstrung by massive structural and systemic barriers such as our convoluted system for how care is paid for and reimbursed, hit-and-miss access to care, and more broadly, obstacles and friction in the interplay among providers, payers, and consumers. So far, technological advancements alone have failed to address the sought after “ecosystem transformation” that it will take to shift the bell-shaped curve of health outcomes to the right – to benefit all people.
What can big data offer to boost such efforts? Let’s apply AI to empower patients so they can make more evidence-based, informed choices while proactively aligning incentives. Medical decisions that are guided by reasoned data can lead to patient care that is more individualized, costs that are better optimized, and equitable outcomes that exceed global standards.
Emerging AI technologies offer an unprecedented opportunity to improve efficiency, reduce waste, and address inequalities. Having more diversity in data sources also represents untapped opportunity. Patients themselves can shed valuable insight on factors such as the long-term benefits and harms of surgery or the choices for medical therapies. Patient-reported outcomes matter and, with the aid of big data harnessing them, they can become a central component of decision-making. Plus, they can also tie directly to utilization and pricing.
Similarly, an informed choice approach with patients, rather than traditional informed consent, offers a way to integrate patients’ values and preferences into care. With generative AI tools, we can do a better job of providing clarity to patients on a given treatment’s benefits and harms.
These changes must come with tangible benefits for patients if we want to encourage greater participation — and trust — in data sharing. Imagine giving patients personalized insights about their health. This helps us shift from standardized care to care optimized to each patient. This opens avenues to reduce spending by avoiding persistently used procedures and treatments that might actually be unwarranted, ineffective, or simply unwanted by a patient who is better informed. Today, let’s celebrate the promise of emerging technology like generative AI to leverage big data for better healthcare. More importantly however, technologists and clinicians must continue to work as changemakers, pushing for a real re-envisioning of the healthcare ecosystem where big data’s potential can flourish
Photo: metamorworks, .
Dr. Peter Bonis is Chief Medical Officer of Wolters Kluwer Health and Adjunct Professor of Medicine at Tufts University School of Medicine.
Dr. Jim Weinstein is Head of Global Access and Equity at Microsoft. He was formerly CEO of Dartmouth Health, the Inaugural Director and Peggy Thompson Chair of the Dartmouth Institute, and Professor at Dartmouth and Clinical Professor Northwestern University (Kellogg School of Management)
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