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June 25, 2021

The Next Disruption in Healthcare: High-Validity Patient Data

According to Dr. Dan Riskin, the healthcare industry is disrupted in 50-year cycles, and the time has arrived for the next epic. His prediction? The proper utilization of high-validity patient health data.

Riskin, entrepreneur, Professor of Surgery and Biomedical Informatics Research at Stanford University, and CEO of Verantos, shared with host Bill Russell how to create a business model for tailored healthcare and how this leads to enabling laws or technologies.

The Cyclical Nature of Healthcare and the Anticipated Next Step

Since the birth of modern medicine, advancements disrupt the industry in 50-year segments, whenever a new advancement or technology spurs a season of excessive growth, according to Riskin. He outlined the last 150 years of these “epics” in healthcare:

  • 1870s: the Germ Theory led to the biggest drop in mortality in history.
  • 1920s: penicillin was discovered.
  • 1970s: the transition to evidence-based medicine.

    Dr. Dan Riskin This Week in Health IT

    Dr. Dan Riskin, CEO of Verantos and Professor of Surgery and Biomedical Informatics Research at Standford University

People are living longer in the 2020s compared to 100 years ago; yet in addition to widened options for treatments, the population experience several comorbidities. The stage is being set for the next disruption, Riskin explained.

“The question is, how are doctors choosing? What happens if they don’t have good information? We have challenges of sharing data, challenges of interoperability, challenges of accuracy. We can see where the industry needs to improve, but this is a long-term commitment that will take decades,” he said.

Data can be a critical part of this next stage, according to Riskin, who explained how medicine has begun to shift away from science back into a more “artistic” practice. However, providing doctors with high-quality data can re-align this practice.

What Data Can Do for the Future of Healthcare

In the last decade, the healthcare industry has become enabled by technologies like compute power, AI, and EHRs. However, tailored therapy, or real-world evidence, is the next disruption the industry desperately needs, according to Riskin.

Data must be collected to tailor care to patients in order for data-driven disruption to occur. However, there are challenges for data sharing, interoperability, and data accuracy. With focus, Riskin explained the aggregation of data will improve.

“We’ll see value in this decade between 2020 and 2030. but this is a long-term commitment of healthcare to move toward tailored therapy,” he said.

Healthcare culture must also shift to enable a real-world evidence approach. This shift presents both irrational and rational fears, according to Riskin. By relying on real-world evidence, there is a chance bad evidence can make it into the system. Patient information could be put at risk if inappropriately used as identifiers sold on the open market, he said.

“Now, an insurer can know about a patient and change their behavior based on that. Or a researcher, who shouldn’t have access to an individual’s information and name, will know this patient has cancer. This is a real fear that we won’t handle privacy and security well as an industry. And these fears have to be openly discussed and addressed,” he said.

Creating and Utilizing High-Validity Data to Increase Patient Health Outcomes

Only been recently have regulators, payers, and doctors expect the amount of information that comes with high-validity data.

“They’re starting to use it to change the standard of care there. The payers are deciding what they’re reimbursing based on it. The regulators are deciding what they’re approving. The doctors are deciding what they’re prescribing as these groups use real-world evidence to make these decisions that define the standard of care,” he said.

Verantos, where Riskin is CEO, uses high validity, real-world evidence. He identified that many patients and providers assume a higher quantity of data leads to more accurate results. However, Riskin explained this is not always the case.

“If you’re using low-quality data, doubling the amount of data, or taking it by 100 times, does not give you a different answer. It does not give you a better answer. The key is to use high-quality data, whether it’s a small amount or a large amount, and power it sufficiently for the assertion you’re trying to make,” he explained.

According to Riskin, most places utilize small amounts of real-world evidence in the healthcare industry. While some groups are putting out high validity real-world evidence, this is only the beginning. He hopes this will continue as the FDA presents real-world evidence guidance for 21st Century Cures.

Looking to the future, Riskin believes the healthcare industry is just at the beginning.

“The future should look better than it looks now, or we haven’t done our job well,” he said.

Understanding the Potential Bias of Health Data

Healthcare professionals should consider two potential biases in health data, according to Riskin.

When the average person hears “health care biases,” they will likely jump to healthcare inequities, Riskin explained. For example, results can be skewed in randomized trials because they involved more affluent participants, whereas low-income or marginalized groups are excluded. Riskin believes real-world evidence is an equalizer for information based upon a general population to analyze subgroups.

Real world evidence is more often used in routinely collected data, but what we’re saying here is RCTs randomized trials will never get us the subgroups and comparative effectiveness… They’re too expensive, and you can’t run studies on every person in the U.S.,” he explained. “On the other hand, routinely collected data has a huge amount of information within the different approved therapies, of which one is working better or worse for which subgroups.”

Another is bad data, leading to wrong information, which creates an adverse change in the standard of care. Trials that collect low-accuracy and low-validity evidence have challenged research studies and clinical assertions. This changes the overall standard of care.

The current technology landscape is comparable to an iceberg, according to Riskin. The first is the base, which is the traditional claims data that is queried in traditional ways. Further up is a smaller group making efforts to use unstructured data and natural language processing to pull out data to gain a richer view of the patient. The iceberg tip is the smallest group that brings together different data sets to figure out the intersection, later using source data to determine accuracy. They achieve high validity real-world evidence, which includes deep phenotyping and linkage.

How to Incentivize Healthcare Towards Better Patient Outcomes

To create a positive disruption, healthcare needs to make patients the customer, according to Riskin.

“In our industry, I think all of us know that vendors work with provider organizations or payers, and they defer to regulators. The money is flowing between these different stakeholders. But the patient doesn’t have the control of the dollars, decision-making, or the attention of the different stakeholders that they deserve,” he said.

He believes that the future of healthcare will look like real-world evidence and other aspects in the industry deferring to the patient’s expertise.

“The doctor may be the expert on biology, but the patient is the expert on [themselves],” he explained.

According to Riskin, he hopes to see the industry using high validity, real-world evidence to give custom-tailored treatment plans in the next ten years. Then, the industry can begin to use better genomics, sensors, and information to customize patient care.

“And 50 years from now, we’ll have another disruption. And hopefully, that’ll be even better than this one. But I got to tell you, we’re having a lot of fun on this one, and we are going to make healthcare better,” he said.

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