Defining terms, analyzing outcomes: Precision health in a learning health system
On November 29, at an event Precision Health cosponsored with LHS Collaboratory, Marc Williams, MD, addressed the timely topic of “Patient-Centered Precision Health in a Learning Health Care System,” using Geisinger’s population-based genomic medicine initiative as the example. Williams, who is the director of Geisinger’s Genomic Medicine Institute, demonstrated how the principles of collaboration and continual review and improvement that are the hallmarks of a learning health system lend themselves to effectively implementing a precision health program. “Health care delivery is increasingly defined by precision health and learning health care systems,” Williams told the dozens of participants at the Palmer Commons event.
Williams started by differentiating among the terms “genomic medicine,” “precision medicine,” and “precision health,” which are often used interchangeably. Currently, he said, precision medicine is “intuitive medicine,” based on “empirical trial and error,” but aspires to “precisely define” a condition at the root level. “Precision health,” on the other hand, encompasses “precision in prevention,” employing “a population perspective.” In a May 2018 article in Health Affairs, Williams wrote that he and his colleagues prefer the term precision health, “as it encompasses both wellness and disease.”
Precision health is frequently equated with genomic medicine, and although Geisinger’s precision health experience has a strong genomic medicine component, Williams stressed that the two terms are not synonymous. “…[Ge]nomic data must be combined with data from other sources (for example, clinical, environmental, and social) to inform precision care,” Williams wrote in his Health Affairs piece. In his presentation, he added, “Family history is still relevant,” even though it’s “not as sexy” as parsing the genome.
An important factor to the success of a precision medicine program is the system in which it is being implemented. “Health care systems as traditionally configured are not designed or equipped to deliver precision health to patients,” Williams wrote in Health Affairs. Enter the learning health care system, which “generates and applies best evidence for collaborative healthcare.” With its focus on communication across disciplines and the central role the patient plays in the process, a learning health care system provides an appropriate and fertile environment to implement precision health practices.
Once a precision health program is implemented, its overall impact must be measured. Williams and his colleagues defined a set of six outcomes to measure the program’s benefit to individuals, providers, processes, costs, and the system overall. These analyses reveal what is effective, as well as where care gaps exist.
Williams discussed areas for improvement and further study during a Q&A session following the presentation, such as how to keep the patient actively engaged throughout the healthcare process, how to incorporate genomics data into population health, and how to include patient-reported outcomes in the continual improvement loop of learning healthcare systems.
Also key to the success of implementing precision health practices on a larger scale is data standardization and accessibility. “Genomics data is persistent,” Williams said, meaning it does not change over time. How, then, he asked, can we make this data “travel with the patient,” when the simple task of transferring records is difficult in traditional health systems? To apply precision health practices on a national level, we need “standardization across data elements,” as well as “safe harbors” to aggregate data, he said. Keeping datasets separate and sequestered benefits patient privacy and security, but how much do these practices benefit health? Williams suggested that researchers also ask the question, “What are the harms of keeping data sequestered?”