Healthcare: The Glacial Pace of Applying Innovation

Innovation has had a tendency to move at a glacial pace, and world history is littered with scientific discoveries that took a long time to reach us and have an impact on our lives.

So many fields such as advanced math and complex numbers discovered in the 16th century were originally described as “imaginary,” as if to emphasize their impracticality hundreds of years before they were used in calculations involving alternating current (AC) and impedance.

In physics, we have seen incredible insights, from basic observations of Sir Isaac Newton in his Philosophiæ Naturalis Principia Mathematica to Albert Einstein’s astounding revelations in his theories of general and special relativity.

In the field of biology, Darwin’s theories contained in On the Origin of Species were deemed heretical at the time, and yet now they are considered to be the foundation of evolutionary biology.

Medicine proves to be no different. We have seen repeated instances of rejection and challenges to new technologies and insights. When René Laennec came up with the original stethoscope, his newly invented instrument was famously referred to in the Times of London as such:

“It will never come into general use notwithstanding its value,” the newspaper wrote. “It is extremely doubtful because its beneficial application requires much time and gives a good bit of trouble to both the patient and the practitioner; and because its hue and character are foreign and opposed to all our habits and associations. It is just not going to get used.”

New Treatments Applied Slowly
Today, it takes on average 17 years for innovation to reach general application in healthcare, as written in an article published in the Journal of the Royal Society of Medicine. “The answer is 17 years, (but) what is the question?” the article read. “Understanding time lags in translational research.”

The article’s authors reviewed multiple papers to ascertain the time delay in the application of medical insights into clinical practice. This table from the 2000 Year Book of Medical Informatics: Balas Boren Managing Clinical Knowledge for HC Improvement, illustrates the aforementioned time delay:

Clinical Procedure

Landmark Trial

Current Rate Use (2000)

Flu Vaccination



Thrombolytic therapy



Pneumococcal vaccination



Diabetic eye exam



Beta blockers after MI






Cholesterol screening



Fecal occult blood test



Diabetic foot care



To be clear, I am not advocating the application of unproven ideas and theories, but rather taking advances that have been proven with studies and expanding access to everyone.

Patient Engagement

We have seen multiple instances of patients who have refused to accept the current state of affairs in their conditions and treatment.

Dave deBronkart (aka “e-Patient Dave”) was an early advocate and trailblazer. In January 2007 he received a diagnosis of Stage 4, Grade 4 renal carcinoma, and his prognosis was not good (that’s an understatement). Had he accepted the prognosis and the standard treatment, he would not be here today. He did not, and together with his care team he pushed the boundaries of the disease and our understanding of it, joining a clinical trial for a new therapy that was successful. Ten years later, he is thankfully still here, and he continues to advocate for and push the boundaries of patient engagement and participation.

Not all therapies work in the same manner, and not all patients are good candidates for new therapies, but it’s a fair assessment that most of us would want a similar lifesaving cure for a catastrophic disease. Teasing out what works and what doesn’t remains an ongoing challenge in science. Science and discovery are littered with many blind alleys, failures, and course corrections, but it is these failures that contribute to our continued progress.

The new age of “all the data” is going to change the way we innovate and discover. As Chris Anderson of WIRED recently asked, “what can science learn from Google?” His response was outlined in a post titled The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, as noted below:

“At the petabyte scale, information is not a matter of simple three- and four-dimensional taxonomy and order, but of dimensionally agnostic statistics,” Anderson wrote. “It (the petabyte scale) calls for an entirely different approach, one that requires us to lose the tether of data as something that can be visualized in its totality. It forces us to view data mathematically first and establish a context for it later.”

“Learning to use a computer of this scale may be challenging,” Anderson continued. “But the opportunity is great: The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.”

Applying Knowledge Today

So now we are facing a future in which information and discoveries are arriving at an increasing rate. For proof, look no further than the Exponential Medicine site, part of Singularity University. You could also attend the great Exponential Medicine Conference that takes place each year in San Diego, Calif. to get an idea of the tsunami of innovation coming our way.

So how do we capitalize on this increased knowledge so that the best information is applied each and every time we look for insights and treatments in medicine?

Incremental Improvements to Adoption of Innovation

For the incremental approach, turn these insights into small actionable pieces that can be applied at each of the intersection points. For example:

  • Make the information available in its entirety to everyone involved in the care – this includes not just the clinicians but also the patients and their family and friends (with the approval of the owner of the data – the patient);
  • Abolish selective reporting: Make the research data widely available, and importantly, publish all the data, not just the data that matches the desired outcome or result;
  • Be open to change and alternatives – recognize that the resistance to change is inherent in all of us, but acceptance can be the first step in change; and,
  • Find common ground and practice guidelines that make it possible to reach an agreement and limit the variation in care that occurs in treatment that comes with your location and treating entity.

Do you have any other suggestions? What small change have you seen that makes a difference to speed up the appliance of science in healthcare? What one thing could we do that would have a big impact in this area? Let me know.

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