Measure Differently to Think Differently

Credit: Innovation by Boegh, licensed by Creative Commons

There are many forms of innovations.  Sometimes medical innovation is nanotechnology, molecular imaging, high-precision targeted therapy, or 3D-printed prosthetic, which are advancements whose adaptation rate are limited by the rate of research.  This is a good thing.

And then, there exists technology that has become commonplace in every other industry but is still considered “innovation” in medicine due to their glacial adaptation rates in hospitals and clinics.  Case in point: When was the last time you saw a pager that doesn’t belong to a healthcare provider?

The use of tablet devices in a hospital as recently as 2014 was also considered cutting-edge research, publishable in academic journals despite tablets’ otherwise wide availability.

The answer may lie in our love for scorecards.

Balanced Scorecards are management tools designed to measure performance against benchmarks.  Modern doctors love a scorecard, even if they call it something else. You might know it as a laboratory value or a radiology report.  As physicians we love objective data so much that we are taught early on to explicitly ignore them lest we may be lost in the forest for its trees.  e.g. “Treat the patient, not the lab.”  “A radiologist with a ruler is a radiologist in trouble.”

Doctors are not the only ones that love scorecards.  Managers of large organizations, like those in hospitals, also love scorecards as a measurement tool.  While it is a great tool to get an organization on track for key performance metrics, over time it can contribute to inertia against change. As such, they can unwittingly lean against acceptance of innovation or adaptation of new technology

While scorecards are excellent in determining the presence of aberrant performance, they do not measure cause, only the effect.  In mature large organizations, performance over time  evolves to optimize on its corresponding scorecard items.  Therefore, at least part of the problem with resistance to adaptation of new ideas or technology arises not so much from how it decreases performance but from how change appears suboptimal because it is different.  Change – good or bad – steers an organization away from its current course as measure by established metrics.

Like evolution, organizational expectations and metrics – conveyed through scorecards – are backward rather than forward-looking.  Innovations are like genetic mutations in an organization.  Even if they are adaptive to a future selection pressure, they will face eradication if they do not yield immediate benefits or at least remain neutral in the current climate.  After all, lungs are a great idea for amphibians, reptiles, and mammals, but they would have been a terrible idea for the fish.

So why are we still using pagers, and why does the bestselling premiere electronic medical record system in America run on 1960s technology? In a world where “search” had become the preferred mode of navigating very large datasets – think “the Internet” – decades ago, why is parsing through patient record for specific information, such as when a medication was first prescribed, still largely a manual task?

In part, it is because organizationally, a healthcare system is held accountable – more so than many other industries – to a strict set of scorecard metrics which are tied to reimbursements.  Therefore, once a system “settles in” to its optimal state, the eagerness for organizational-scale adaptation of new innovations tends to coincide with changes in reimbursement patterns.  For instance, the largest drive for adaptation of electronic medical records came with reimbursement incentives for “meaningful use” of EMR.  One of the main drivers for computer-assisted detection in mammography is additional reimbursement associated with its use.

Bookstores were measured by increases in total store revenues and by same-store revenues (and their income statement variants), but online sales was not on the scorecard when first started.  Even as late as 2010, bookstores like Borders preferred to outsource their online presence rather than to consider it as a core competency.  Blockbuster made most of its revenues on late fees, so Netflix, which ran on a “mail the DVD back whenever you want without late fees” business model, was clearly doomed to fail by Blockbuster’s scorecard. Both Borders and Blockbuster learned their lessons too late and fell from industry leaders to business school footnotes.

The difference between improving the quality of care in healthcare and biological evolution is that we don’t have to stay reactive to the changing selection pressures.  Uniquely among animals, we do have the ability to make an educated guess on what the future holds.  We should make use of that incredible gift.

Howard Chen on GithubHoward Chen on LinkedinHoward Chen on Wordpress
Howard Chen
Vice Chair for Artificial Intelligence at Cleveland Clinic Diagnostics Institute
Howard is passionate about making diagnostic tests more accurate, expedient, and affordable through disciplined implementation of advanced technology. He previously served as Chief Informatics Officer for Imaging, where he led teams deploying and unifying radiology applications and AI in a multi-state, multi-hospital environment. Blog opinions are his own and in no way reflect those of the employer.

Leave a Reply