In this article, Jim Hoffman, COO of BESLER Consulting, discusses current uses of predictive analytics in healthcare. It was featured in the September 2014 edition of Managing Health Today, a publication of the Hudson Valley Chapter of HFMA.

1. September 2014
Page 1
MANAGING HEALTH TODAY
Serious News & Ideas for Healthcare Executives
September 2014
Volume 19 Issue 2
Westmed Medical Group
Issue highlights:
7 Predictive Analytics in Healthcare
8 Making the Case for Business Intelligence
14-15 SHIN-NY Set to Launch Next Year
pgs. 10-11
featured practice:
The Data
Issue
Don’t miss our
Photo Gallery
of the 2014
Renegades Game!

2. September 2014
Page 7
featured article
Predictive Analytics in Healthcare
Jim Hoffman, COO, Besler Consulting
Predictive analytics has been used for many years in other in-dustries,
but the concept has only recently come into favor in
healthcare.
What does “predictive analytics” mean? At a high level, pre-dictive
analytics involves feeding a software tool a large vol-ume
of historical data to determine patterns of factors that
relate to a particular outcome. The software calculates a set
of rules that can be applied to current data in order to make
a prediction. The outcomes can be anything from “will this
patient be readmitted?” to “will this claim be denied?” to “how
many nurses should I schedule for next Tuesday?”
You interact with predictive analytics every time you visit
a supermarket. The coupons that print out at checkout are
based on analyzing your current order and your past orders
(if you use a membership card), and determining what else
you’re likely to buy. If you buy hot dogs and ketchup, you
might receive a coupon for hot dog buns. This is predictive
analytics at work. On Amazon.com, when you see the section
under an item with the heading “Customers Who Bought This
Item Also Bought,” this list of products is also generated via
predictive analytics.
In healthcare, predictive analytics is being used in a number
of areas:
• Readmissions: Identifying patients likely to be read-mitted
is becoming a popular use of predictive analytics in
healthcare. With increasing Medicare penalties for excess
readmissions, hospitals are incentivized to reduce them. Pre-dictive
analytics can allow limited resources for discharge
planning and post-discharge follow-up to be dedicated to the
patients most likely to be readmitted.
Some payers are using predictive analytics to identify ben-eficiaries
likely to be readmitted. The payer then proactively
communicates with the patient to be sure prescriptions are
filled and primary care appointments are made, in an effort to
reduce expenses related to readmissions.
• Observation: Medical necessity audits by various gov-ernment
and private payers have led hospitals to very care-fully
consider the use of inpatient admission vs. observa-tion.
Overusing observation status can result in reduced
patient revenue, and underutilizing observation status can
lead to medical necessity denials. Efforts are underway to
use predictive analytics to help to determine, in conjunction
with caregiver expertise, the most likely patient classification
based on the demographics, vital signs and medical history
of a patient.
• Denials: By examining thousands of previously billed
claims and their subsequent adjudication, predictive analytics
has been used to identify the claims most likely to be denied
before the bill is dropped. By routing these high risk claims
to a review workflow, denials can be prevented and cash flow
improved.
• Propensity to Pay: Predictive analytics models are also
being used to determine a patient’s ability to pay his or her
out of pocket expenses prior to an elective procedure. Demo-graphics
and credit scores are combined to create a risk score,
and this is used make a decision about whether to extend
credit or to require payment prior to provision of services.
Not every problem is a good candidate for a predictive analytics
solution. If a definitive answer can be derived from the data at
hand, then predictive analytics is a poor fit. For example, one
could use predictive analytics to assign a Medicare DRG to a
claim before it’s billed. Predictive analytics could determine
which DRG the claim “looks like.” However, a DRG grouper
can definitively determine the appropriate DRG, and is what
should be used.
When utilizing predictive analytics, there is often a tradeoff
between ease of implementation and effectiveness. If a pre-dictive
analytics model to determine risk of readmission is
built solely on claims data, which is easily obtainable, then the
model is relatively simple to create and implement - simply
run UB-04 data through the predictive model and generate a
risk score at the time of billing. However, many of the more
accurate readmission predictive models require EMR data and
lab test results, as well as socioeconomic data unrelated to bill-ing.
Interfacing with multiple hospital IT systems and modi-fying
admission processes may be required to gain the benefits
of a more accurate prediction.
Healthcare is beginning to catch up to many other industries
when it comes to the use of predictive analytics. Providers
should monitor the expanding implementation of the tech-nology
to determine when the price point and accuracy can
provide a solid return on investment based on their particular
needs.
Jim Hoffman brings twenty-five years of
technology and operations experience to
his position as COO of BESLER Consulting.
Most recently, he was President and General
Manager of Accuro Revenue Management
for MedAssets. Prior to the acquisition of
Accuro Healthcare by MedAssets, he served as President and
Chief Operating Officer of the Accuro Revenue Management
business unit, and Chief Operating Officer of Innovative Health
Solutions, acquired by Accuro from Besler Consulting in 2005.
Jim is a graduate of the University of Virginia.