Hurry up and Implement
Innovation has had a tendency to move at glacial pace and history is littered with scientific discoveries that took a long time to reach our world and have an impact on our lives.
So many areas in our lives – math and complex numbers discovered in the 16th Century that were originally described as “imaginary” numbers as if to emphasize their impracticality and it was hundreds of years before they were used in earnest in calculations with alternating current and impedance. In physics, we have seen incredible insights from the basic observational details of Newton in the Philosophiæ Naturalis Principia Mathematica to Einstein’s astounding revelations in the Theory of Special Relativity and the Theory of General Relativity. The insights from these continue to provide breakthroughs in our understanding of the world
In Biology, Darwin’s theories contained in his book On the Origin of Species were deemed heretical at the time and yet now are considered to be the foundation of evolutionary biology.
Medicine proves to be no different and we have seen repeated instances of rejection and challenge to new technologies and insights. When René Laennec came up with the original stethoscope it was famously referred to in the Times of London:
“it will never come into general use notwithstanding its value. 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
It takes on average 17 years for an innovation to reach general application in healthcare – in this paper in the Journal of the Royal Society of Medicine 17 Years is the time for Translational Research. The authors reviewed multiple papers to ascertain the time delay in the application of medical insights into clinical practice. This table from “2000 Year Book of Medical Informatics Balas Boren Managing Clinical Knowledge for HC Improvement”
|Landmark Trial||Current Rate Use (2000)|
|Diabetic eye exam||1981||38.4%|
|Beta blockers after MI||1982||61.9%|
|Fecal occult blood test||1986||17%|
|Diabetic foot care||1983||20%|
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.
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 ePatient 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 and joined a clinical trial for a new therapy that was successful. 10 years on he is thankfully here and continues to advocate and push the boundaries of patient engagement and participation.
Not all therapies apply and not all patients are good candidates for new therapies but it’s a fair assessment that most of us would want a similar life-saving therapy for a catastrophic disease. Teasing out what works and what does not 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 from Wired asked, “What can Science Learn from Google as he suggested, The end of Theory: The Data Deluge makes the Scientific Method Obsolete“.
At the petabyte scale, information is not a matter of simple three- and four-dimensional taxonomy and order but of dimensionally agnostic statistics. It 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. 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 where information and discoveries are arriving at an increasing rate – look no further than the Exponential Medicine site (Part of Singularity University) and attend the great Exponential Medicine Conference that takes place each year in San Diego to get an idea of the Tsunami of innovation coming our way. So how do we capitalize on this increase knowledge acquisition 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, it’s turning these insights into small actionable pieces that can be applied at each of the intersection points
- It’s making 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 the resistance to change is inherent in all of, acceptance can be the first step in chang
- Find common ground and practice guidelines where possible to reach agreement and limit the variation in care that occurs in treatment that comes with your location and treating entity
Do you have any better 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?
This was originally posted on ICD10 Monitor and you can listen to the podcast here
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