One-Size-Fits-All Medicine

One size does not fit all. The real question is: how do we predict who is going to respond? And how do we individualize treatments so they can begin ultimately be tailored to the person.

Thomas Insel, MD, Director , National Institutes of Mental Health

March 18, 2012 – Somehow, we are told by government experts, that by using data analytics, clinical algorithms, and statistics of average outcomes on mass populations, we will not be able to homogenize, standardize , and legitimize medicine, convert it into a rational science, improve outcomes, and manage health care.

Perhaps so. But that is not how individual doctors and individual patients think. They think of their practices, their bodies, their genes, and their personal problems and diseases as unique.

Everything human does not lend itself or cannot be reduced to protocol and to Google-like algorithms.

As Jerome Goodman, MD, professor of medicine at Harvard, observed in How Doctors Think (Houghton Mifflin, 2007),

“Clinical algorithms can be useful for run-of-the-mill diagnosis and treatment – distinguishing strep throat from viral pharyngitis, for example. But they quickly fall apart when a doctors needs to think outside their boxes, when symptoms are vague, or multiple and confusing or when test results are inexact. In such cases – the kinds of cases where we most need a discerning doctor – algorithms discourage physicians from thinking independently and creatively. Instead of expanding a doctor’s thinking, they constrain it.”

Blind belief in homogenization, standardization, and a commitment to data as a management tool , may be desirable for society as a whole, but it us not always relevabt individual differences or desires. As a typical American might say: vive the differences.

Tweet: Americans believe in individualism and freedom in health care, not in one-size-fits-alldecision making.