Industry executives and experts share their predictions for 2020. Read them in this 12th annual VMblog.com series exclusive.
By
Sanjeev Agrawal, President, LeanTaaS
Data Science-Driven Metrics Will Start Being Adopted Due to Meaningless Averages
Operations
teams seem to run on "averages," especially in the healthcare arena. Committees
and leadership look at all kinds of averages to make decisions - average LOS,
average case length, average turns per infusion chair, average block
utilization, and average wait times, to name a few. The simplest way to
illustrate the problem with averages is as follows: "If I put my head in the
freezer and my feet in the oven, my average body temperature might be 98.6
degrees Fahrenheit, but I will soon be dead."
Some
practical examples:
- Take the way global
averaging works for estimating case lengths for spinal surgeries that every operating
room has to decide on. Global averaging of a 2-level spine fusion and a 5-level
procedure to come up with the "average length of a spine case" is like putting
my head in the freezer and feet in the oven to come up with a meaningless
answer. There is a much more useful and sophisticated way of looking at
specific case types and the notes for the procedure and coming up with data science
models that are far more useful to estimate lengths of the two very different
types of spine cases.
- How clinics anticipate and plan for the volume of patient arrivals
requires much better planning than "40 patients per day." The data now exists
for us to understand and staff within each 15-minute window of each day the mix
of the various appointment types in the incoming arrivals; the time spent by a
specific provider with a patient for each type of appointment that they
encounter; and the probability of an add-on, cancellation, late arrival or
no-show. Only then can clinic provider templates be optimized to lower patient
wait times and have a smoother day for staff, physicians and patients.
These
are two of hundreds of examples - the morning huddle for the expected number of
discharges in inpatient units, the length of an infusion appointment, the
expected wait in the emergency room - there are now tons of historical data
that can be used to come up with far more meaningful and accurate predictions.
Given
the need for better utilization of resources in enterprises in general - but
particularly hospitals - operations teams will have to start moving to use more
science with the data to determine and review metrics.
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About the Author
Sanjeev Agrawal
is president of LeanTaaS, a Silicon Valley-based innovator of predictive analytics solutions to
healthcare's biggest operational challenges. He works closely with dozens of
leading healthcare institutions including Stanford Health Care, UCHealth, NewYork-Presbyterian,
MD Anderson and more. Sanjeev was Google's first head of product marketing.
Since then, he has had leadership roles at three successful startups: CEO of
Aloqa, a mobile push platform (acquired by Motorola); VP product and marketing at
Tellme Networks (acquired by Microsoft); and as the founding CEO of Collegefeed
(acquired by AfterCollege). Sanjeev graduated Phi Beta Kappa with an EECS
degree from MIT and along the way spent time at McKinsey & Co. and Cisco
Systems. He is an avid squash player and has been named by Becker's Hospital
Review as one of the top entrepreneurs innovating in healthcare. For
more information on LeanTaaS, please visit https://leantaas.com/ and
follow the company on Twitter @LeanTaaS, LinkedIn at www.linkedin.com/company/leantaas and Facebook
at www.facebook.com/LeanTaaS.