Precision Medicine: Separating Fact From Fiction

There’s much evidence to suggest that precision medicine can and will improve patient care and affect everyday medical practice. But precision medicine has its critics, who are convinced that proponents exaggerate what this medical model can accomplish, and question whether it will cost too much too implement, and how long it will take to see benefits for the average patient in community practice—if ever.

During the last 12 months, John Halamka, MD, CIO at Beth Israel Deaconess Medical Center, and I embarked on a research project to look at the benefits and risks of precision medicine and to separate fact from fiction. The project, which culminated in the publication of “Realizing the Promise of Precision Medicine” (Elsevier/Academic Press), and will be featured at HIMSS2018, found that data analytics, properly vetted mobile apps, and patient engagement are all key ingredients in the realization for precision medicine. But our research also revealed several significant obstacles and skepticism.

Among those concerns: Precision medicine is far too expensive to take hold in the US healthcare system; there is no reason to invest $200 million in an initiative or launch specialized departments in academic medical centers devoted to the topic. Skeptics are basically saying: “We already practice personalized medicine with every patient we see.” Others say such projects will overwhelm clinicians with mountains of data that will be impossible to turn into actionable insights, especially in community practices where doctors only have 15 minutes to see each patient.

How Much Will Precision Medicine Cost?

An analysis in the Journal of Economic Perspectives that reviewed the cost and benefits of 58 oncology drugs approved by the FDA from 1995 to 2013 concluded: “The scientific knowledge embodied by new drugs is impressive, but progress in basic science has not always been accompanied by proportionate improvements in patient outcomes. Gains in survival time associated with recently approved anticancer drugs are typically measured in months, not years.” The same analysis also found that each additional month of life offered by these precision medicine agents is getting more and more expensive over time. Insurance companies and patients paid $54,100 for a year of life in 1995, but in 2005, it cost $139,000. In 2013, the same year of life cost $207,000. The return on investment has been meager, with survival improving by only a few months.

“We Already Practice Personalized Medicine”

Many skeptical clinicians say they have been providing individually tailored care for years. If a patient with hypercholesterolemia comes into the office, they are not indiscriminately given a statin to lower their LDL levels. They are asked about contraindications, including the presence of liver disease. Similarly, patients with type 2 diabetes are not automatically put on metformin, a sulfonylurea, or insulin. Most physicians follow a detailed algorithm from the American Diabetes Association that walks them through a stepwise approach that includes diet, exercise, and a series of specific drugs.

Although some thought leaders might call that precision medicine, it’s more accurate to refer to it as trial and error medicine. Personalized medicine with a capital P is much more sophisticated and takes into account the root causes of each disorder—when it can be deciphered—and the interplay among interacting risk factors. One of the goals of the Precision Medicine Initiative, which has been renamed the All of Us Research Program, is to collect data on genomics, dietary habits, physical activity level, exposure to infectious agents, social determinants of health, climate, data from mobile health apps, and more. Once these interlocking contributing causes of health and disease can be correlated with specific disorders, it’s likely the program will reveal new preventive and treatment options.

It will take years to bear fruit, but several innovative thinkers have already conducted data analytics projects that can help personalize care for subgroups of patients.

Precision Medicine Rests on Better Data Analytics

In 2002, a landmark study was published that found type 2 diabetes could be prevented in many persons at risk for the disease by either giving them metformin, a common hypoglycemic agent, or enrolling them in an intensive lifestyle modification program. The investigators divided 3000 patients into 3 groups, with about 1000 receiving metformin, the second group lifestyle modification, and the third served as controls.

Metformin reduced the incidence of diabetes by 31% when compared to controls, and the lifestyle program reduced it by 58%. But the researchers couldn’t predict which patients receiving the experimental protocols would respond to the treatment and which would ultimately develop diabetes.

In 2015, Jeremy Sussman, MD, from the University of Michigan, and his colleagues reanalyzed the raw data from the Diabetes Prevention Program (DDP) using more sophisticated statistical tools and factoring in several risk factors that the original researchers did not consider. Sussman et al did a deeper dive into risk factors that might contribute to diabetes. While the original DDP examined overweight, fasting blood glucose, and glucose tolerance test results, Sussman analyzed 17 factors and narrowed it down to 7 that contributed to the likelihood of an individual developing the disease.

Their reanalysis found that the “average reported benefit for metformin was distributed very unevenly across the study population, with the quarter of patients at the highest risk for developing diabetes receiving a dramatic benefit (21.5% absolute reduction in diabetes over three years of treatment), but the remainder of the study population receiving modest or no benefit.” The message is clear: Using advanced data analytics techniques can help us get closer to that holy grail of designing preventive and treatment plans to meet each individual’s needs.

Bio

Paul Cerrato has more than 30 years of experience working in healthcare as a clinician, educator, and medical editor. He has written extensively on clinical medicine, electronic health records, protected health information security, practice management, and clinical decision support. He has served as Editor of Information Week Healthcare, Executive Editor of Contemporary OB/GYN, Senior Editor of RN Magazine, and contributing writer/editor for the Yale University School of Medicine, the American Academy of Pediatrics, Information Week, Medscape, Healthcare Finance News, IMedicalapps.com, and Medpage Today. The Healthcare Information and Management Systems Society (HIMSS) has listed Mr. Cerrato as one of the most influential columnists in healthcare IT.