Marc S. Williams, MD, delivered the keynote address at the Center for Genomic Medicine’s 2019 symposium. Williams is the director of the Geisinger Genomic Medicine Institute in Danville, Pennsylvania. He traveled to HudsonAlpha’s campus to address researchers about Geisinger’s efforts to develop patient-centered precision health data that can be used to set standards of care.
The Geisinger Genomic Medicine Institute has incorporated genetic sequencing into routine medical practice, aiming to explore the impacts of including genetics as a part of preventative and ongoing care. Their research has collected consent from nearly 250,000 patients. More than 150,000 of those patients have already been sequenced.
Genomic medicine and precision medicine are often talked about together, but Williams emphasized that precision medicine extends beyond genomics. Williams points to a definition of precision medicine that operates from all of the available information, genetic or otherwise. The definition also speaks to patient desires, with the aim of maximizing the outcomes that patients most care about, while minimizing the outcomes that a patient fears most. Williams notes that many patients want different outcomes from their care, even when treating the same condition. Some are willing to undertake risk with even a minimal hope of success; others want a treatment regimen that will help them be comfortable. Ensuring you treat the patient with their desired outcomes as a focal point is critical to success in precision health.
Williams contrasts this definition of precision health with current healthcare practices. He says that a great deal of medical care relies on intuitive medicine, based primarily on the observation of symptoms. A truly precise healthcare system, Williams explains, would hone in on specific causes rather than broad results. He gives the example of Type 2 diabetes, noting that precise medicine would diagnose more specific causes and results than the broader definition of the condition includes.
The research at Geisinger aims to collect data for a number of challenging variables in genomic medicine. For example, measuring outcomes for preventative genomic medicine poses a challenge given the timeline of those outcomes. If an infant patient receives genetic sequencing results that show a predisposition to high cholesterol, it may be fifty years before you can collect meaningful data on the success or failure of the ensuing preventative care. The team at Geisinger attempts to identify patients who have intermediate outcomes, not a final result but a progress report of preventative care. For example, a polyp being removed from a patient who went for an early colonoscopy after being told they were genetically predisposed to colon cancer. Extended timelines make patient satisfaction and cost outcomes hard to measure as well. Researchers aim to find whether patients are happy with their sequencing and care, if for example, they never develop a condition when they were told they were predisposed. That data is most complete when it encompasses all of the sequencing-driven preventative care they will receive over the course of their life. Cost outcomes also present challenging timelines, as a totality of preventative care costs would need to compared to typical outcome costs. All of these measurements develop over decades.
Williams says that the ultimate ambition is to improve health outcomes and reduce costs. They have assembled a significant amount of data to move them toward that goal.
Written by David Kumbroch at HudsonAlpha; firstname.lastname@example.org