Precision Medicine concept
Is the value of "personalized medicine" over-promised? Why is the quality of health care declining for many people despite the pace of innovation? Do patients and doctors have conflicting priorities? What is the best path forward?
"How do we generate evidence for value, which is what everyone is asking for?"
Some of the country's leading medical experts recently debated these questions at the prestigious annual Personalized Medicine Conference, held at Harvard Medical School in Boston, and LeapsMag was there to bring you the inside scoop.
Personalized Medicine: Is It Living Up to the Hype?
The buzzworthy phrase "personalized medicine" has been touted for years as the way of the future—customizing care to patients based on their predicted responses to treatments given their individual genetic profiles or other analyses. Since the initial sequencing of the human genome around fifteen years ago, the field of genomics has exploded as the costs have dramatically come down – from $2.7 billion to $1000 or less today. Given cheap access to such crucial information, the medical field has been eager to embrace an ultramodern world in which preventing illnesses is status quo, and treatments can be tailored for maximum effectiveness. But whether that world has finally arrived remains debatable.
"I've been portrayed as an advocate for genomics, because I'm excited about it," said Robert C. Green, Director of the Genomes2People Research Program at Harvard Medical School, the Broad Institute, and Brigham and Women's Hospital. He qualified his advocacy by saying that he tries to remain 'equipoised' or balanced in his opinions about the future of personalized medicine, and expressed skepticism about some aspects of its rapid commercialization.
"I have strong feelings about some of the [precision medicine] products that are rushing out to market in both the physician-mediated space and the consumer space," Green said, and challenged the value and sustainability of these products, such as their clinical utility and ability to help produce favorable health outcomes. He asked what most patients and providers want to know, which is, "What are the medical, behavioral, and economic outcomes? How do we generate evidence for value, which is what everyone is asking for?" He later questioned whether the use of 'sexy' and expensive diagnostic technologies is necessarily better than doing things the old-fashioned way. For instance, it is much easier and cheaper to ask a patient directly about their family history of disease, instead of spending thousands of dollars to obtain the same information with pricey diagnostic tests.
"Our mantra is to try to do data-driven health...to catch disease when it occurs early."
Michael Snyder, Professor & Chair of the Department of Genetics and Director of the Center for Genomics and Personalized Medicine at Stanford University, called himself more of an 'enthusiast' about precision medicine products like wearable devices that can digitally track vital signs, including heart rate and blood oxygen levels. "I'm certainly not equipoised," he said, adding, "Our mantra is to try to do data-driven health. We are using this to try to understand health and catch disease when it occurs early."
Snyder then shared his personal account about how his own wearable device alerted him to seek treatment while he was traveling in Norway. "My blood oxygen was low and my heart rate was high, so that told me something was up," he shared. After seeing a doctor, he discovered he was suffering from Lyme disease. He then shared other similar success stories about some of the patients in his department. Using wearable health sensors, he said, could significantly reduce health care costs: "$245 billion is spent every year on diabetes, and if we reduce that by ten percent we just saved $24 billion."
From left, Robert Green, Michael Snyder, Sandro Galea, and Thomas Miller.
(Courtesy Rachele Hendricks-Sturrup)
A Core Reality: Unresolved Societal Issues
Sandro Galea, Dean and Professor at Boston University's School of Public Health, coined himself as a 'skeptic' but also an 'enormous fan' of new technologies. He said, "I want to make sure that you all [the audience] have the best possible treatment for me when I get sick," but added, "In our rush and enthusiasm to embrace personalized and precision medicine approaches, we have done that at the peril of forgetting a lot of core realities."
"There's no one to pay for health care but all of us."
Galea stressed the need to first address certain difficult societal issues because failing to do so will deter precision medicine cures in the future. "Unless we pay attention to domestic violence, housing, racism, poor access to care, and poverty… we are all going to lose," he said. Then he quoted recent statistics about the country's growing gap in both health and wealth, which could potentially erode patient and provider interest in personalized medicine.
Thomas Miller, the founder and partner of a venture capital firm dedicated to advancing precision medicine, agreed with Galea and said that "there's no one to pay for health care but all of us." He recalled witnessing 'abuse' of diagnostic technologies that he had previously invested in. "They were often used as mechanisms to provide unnecessary care rather than appropriate care," he said. "The trend over my 30-year professional career has been that of sensitivity over specificity."
In other words: doctors rely too heavily on diagnostic tools that are sensitive enough to detect signs of a disease, but not accurate enough to confirm the presence of a specific disease. "You will always find that you're sick from something," Miller said. He lamented the counter-productivity and waste brought on by such 'abuse' and added, "That's money that could be used to address some of the problems that you [Galea] just talked about."
Do Patients and Providers Have Conflicting Priorities?
Distrust in the modern health care system is not new in the United States. That fact that medical errors were the third leading cause of death in 2016 may have fueled this mistrust even more. And the level of mistrust appears correlated with race; a recent survey of 118 adults between 18 to 75 years old showed that black respondents were less likely to trust their doctors than the non-Hispanic white respondents. The black respondents were also more concerned about personal privacy and potentially harmful hospital experimentation.
"The vast majority of physicians in this country are incentivized to keep you sick."
As if this context weren't troubling enough, some of the panelists suggested that health care providers and patients have misaligned goals, which may be financially driven.
For instance, Galea stated that health care is currently 'curative' even though that money is better spent on prevention versus cures. "The vast majority of physicians in this country are incentivized to keep you sick," he declared. "They are paid by sick patient visits. Hospital CEOs are paid by the number of sick people they have in their beds." He highlighted this issue as a national priority and mentioned some case studies showing that the behaviors of hospital CEOs quickly change when payment is based on the number of patients in beds versus the number of patients being kept out of the beds. Green lauded Galea's comment as "good sense."
Green also cautioned the audience about potential financial conflicts of interest held by proponents of precision medicine technologies. "Many of the people who are promoting genomics and personalized medicine are people who have financial interests in that arena," he warned. He emphasized that those who are perhaps curbing the over-enthusiasm do not have financial interests at stake.
What is the Best Path Forward for Personalized Medicine?
As useful as personalized medicine may be for selecting the best course of treatment, there is also the flip side: It can allow doctors to predict who will not respond well—and this painful reality must be acknowledged.
Miller argued, "We have a duty to call out therapies that won't work, that will not heal, that need to be avoided, and that will ultimately lead to you saying to a patient, 'There is nothing for you that will work.'"
Although that may sound harsh, it captures the essence of this emerging paradigm, which is to maximize health by using tailored methods that are based on comparative effectiveness, evidence of outcomes, and patient preferences. After all, as Miller pointed out, it wouldn't do much good to prescribe someone a regimen with little reason to think it might help.
For the hype around personalized medicine to be fully realized, Green concluded, "We have to prove to people that [the value of it] is true."
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.