In the business setting, benchmarking methods are applied across nearly
every industry.
These methods are seldom applied in the healthcare setting because of
the reality that individual doctors, groups of doctors, and hospitals
see different patient groups with different risk profiles.
In other words, some physicians do indeed see sicker patients.
That said, some see healthier groups of patients,
as well.
Enter machine learning.
By leveraging optimal classification trees, Alexandria Health has
created a method to first determine the risk level of a patient
population, to then assess it against a determined global standard, and
then identify where a given physician, group of physicians, or hospital
lies on this performance grid to help identify areas of best practice
and areas of distinction.
Thus, the method not only fairly measure the performance but also
identifies specific strengths and weaknesses in performance compared to
peers.
Explore our congenital heart surgery application of this technique and
learn how we partnered with the European Congenital Heart Surgeons
Association to benchmark 295,000 procedures across 180 hospitals in
Europe.