By Julie Yoo, Chief Product Officer at Kyruus
Once upon a time, a world-renowned surgeon, Dr. Michael DeBakey, was summoned by the President when the Shah of Iran, a figure of political and strategic importance, fell ill with an enlarged spleen due to cancer. Dr. DeBakey was whisked away to Egypt to meet the Shah, made a swift diagnosis, and recommended an immediate operation to remove the spleen. The surgery lasted 80 minutes; the spleen, which had grown to 10 times its normal size, was removed, and the Shah made a positive recovery in the days following the surgery – that is, until he took a turn for the worse, and ultimately died from surgical complications a few weeks later. [1]
Sounds like a routine surgery gone awry, yes? But consider this: Dr. DeBakey was a cardiovascular surgeon – in other words, a surgeon whose area of specialization was in the operation of the heart and blood vessels, not the spleen. He was most well-known for his open heart bypass surgery techniques, and the vast majority of his peer-reviewed articles relate to cardiology-related operating techniques. High profile or not, why was a cardiovascular surgeon selected to perform an abdominal surgery?
If one uses volume as a surrogate for clinical experience, one will find a robust body of literature that supports high degrees of correlations between high levels of clinical experience and positive medical outcomes. A small subset of examples includes:
- Surgeons who have performed higher volumes of endarterectomy have lower mortality rates in their patients than those who have lower levels of experience [2];
- Patients medically treated for heart failure by high-volume physicians have lower mortality rates than patients treated by low-volume physicians [3]; and
- Surgeons performing greater than 81 lumbar spinal stenosis procedures in a four-year period were statistically significantly more likely to not have post-operative complications in their patients than those performing fewer than 15 [4]
Malcolm Gladwell has written about this topic (the “10,000 hours rule”), it’s been studied in numerous industries ranging from aviation to manufacturing, and even your own mother might have used this framework to teach you how to play the piano (“practice makes perfect”). As a cardiovascular surgeon, Dr. DeBakey had significantly less clinical experience doing spleen surgeries than the vast majority of general surgeons. And yet, when given the choice of an entire planet’s worth of physicians, the Shah of Iran ended up being routed to him instead of another, more clinically experienced individual.
This type of event may occur more often than you think. Every second, about 30 patients are seen by a physician with significantly less clinical experience in their diagnosis than a colleague who is within 10 miles of that physician [5]. We may be able to reduce the risk of complications, poor outcomes, repeated surgeries, or death for millions of patients per year by having those who determine their course of care use a data-driven approach to physician selection. Cost metrics tend to correlate with clinical experience as well – for a single tertiary care hospital, reallocation of surgery patients to a more experienced colleague even within the same hospital could not only improve outcomes but also save the hospital over $10M per year in costs [5].
The ability to view comprehensive and accurate information on the clinical experience of a physician, along with their practice location, cost profile, and availability, within the context of an actionable workflow, is paramount to solving this multidimensional optimization problem of getting patients to the right doctor – after all, your ability to find the right doctor is only as good as your ability to get a timely appointment with her.
The most provocative thing of all is that the data required to get this right already exists, whether in the records and information systems maintained by every clinic in the country, or in publicly available data sources. The shortcomings of these data are clear – sometimes we can only obtain facility-level data versus individual data. In other cases, the data may only represent a subset of an individual’s practice, and generally the data tend to be very messy and difficult to acquire and accurately merge and normalize. But the general trends of more data liquidity and transparency are enabling the operationalization of this information in the healthcare delivery setting at an unprecedented scale.
We at Kyruus have developed methods to integrate these data silos and embed the resulting information asset into the Patient Access and Referral Management workflows of hospitals, health systems, and accountable care organizations. Our ProviderMatch system addresses the problems of referral misdirection, supply and demand optimization, and utilization management by enabling referring providers and care coordinators to ensure that you and I as patients are seen by the right doctor, in a timely manner that respects financial, geographical, and operational constraints.
The use of data to more precisely match patients to doctors raises the quality of every patient encounter, and helps us avoid the fate of the up to 30% of specialist referrals that today are being sent to the wrong type of provider. Think Kayak.com for referral management, with a basis in the concept of clinical experience.
The moral imperative to use clinical experience as a major driver in the patient-physician matching process has never been so clear. If we could turn back the hands of time with visibility into clinical experience, could we have better triaged the Shah of Iran and altered his clinical outcome? We seek to ensure that history does not continue to repeat itself.
References:
[1] Makary, Marty. Unaccountable: What Hospitals Won’t Tell You and How Transparency Can Revolutionize Health Care.
[2] J Vasc Surg. 2008 Aug;48(2):343-50; discussion 50
[3] Circ Heart Fail. 2013 Sep 1;6(5):890-7
[4] Neurosurgery. 2012 Jun;70(6):1346-53; discussion 1353-4
[5] Kyruus data analysis
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