Prospective Analytics: The Next Thing in Healthcare Analytics

Healthcare Analytics
Advances to the
Prospective Stage for
Outcomes Improvement
– Anne-Marie Bickmore

© 2014 Health Catalyst
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
A new term that is quickly gaining
traction is prospective analytics.
And for good reason.
This new type of analytics offers
an unprecedented opportunity to
use data to affect decisions,
actions, and outcomes at the
point of care.
It takes advantage of retrospective and predictive analytics
to support clinical decision making in a more expansive
way, by looking at the potential results of all options.

You should understand that
without a proven track record in
retrospective and predictive
analytics, jumping right into
prospective analytics is like
walking onstage to perform a
Beethoven symphony with a
world-class orchestra at
Carnegie Hall with no prior
musical experience.

To get to this new level, organizations
must first establish a foundation by
gaining experience with other types
of analytics.
Just as becoming an accomplished
musician – for all but the very rare
savant – requires learning to read
music, working with an expert
instructor, and practicing for many,
many hours.

Currently, there are three types of
analytics that organizations should
use with the petabytes of clinical,
financial, and operational data
they currently generate to elevate
quality, improve outcomes, and
lower costs.
It’s important to not only become
proficient in all three, but to do it in
the proper order. Let’s look at each
of them in depth.

Retrospective Analytics
Healthcare organizations need to
perform this as the foundational
layer for all other analytics.
This must be mastered first. It’s
the equivalent of learning to read
music.
Retrospective analytics provides
a look at what has already
happened, helping a healthcare
organization understand why
those events happened.

Clinicians can use retrospective
data to view past actions – such
as whether a panel of patients
with sepsis received medication
A or medication B – and what the
outcomes were with each.
They can also confirm that the
sample size used in the analysis
was statistically valid.

Retrospective analysis can be
extremely effective at helping the
organization standardize care and
remove variations.
It’s main limitations are:
1. Leads to conclusions that are
restricted to choices already made.
2. Takes a long time for those
conclusions to become policy at
the point of care, usually after
many rounds of discussion.

Predictive Analytics
Predictive analytics takes a higher
level, forward-looking view.
It takes the conclusions from
retrospective analytics and gives
the organization the ability to
speculate on options.
For example, let’s take a heart
failure readmission scenario.

Predictive analytics help identify
heart failure patients with a high
risk for returning to the hospital.
Once we identify those patients,
we alert a case manager and
interventions can focus on
decreasing the likelihood of
patients returning as readmissions.
In our music analogy, predictive
analytics is the equivalent to
practicing scales and techniques
that improve the musician’s skills.

With predictive analytics, the outcomes
aren’t known or guaranteed.
The organization is simply looking at
the likelihood of an event to occur if it
follows a particular course.
It then relies on other processes to
determine what action to take.
While predictive analytics can
generate new possibilities, it is
still not a decision-making tool.

Next Level Analytics
Prospective analytics takes the
knowledge gained through
retrospective and predictive analytics.
Then they drill down to show bedside
clinicians (or administrators) all
available options for changing the
current state, as well as the
associated consequences.
This is why organizations are so
excited about this new methodology.

For a good clinical example consider the
two basic types of appendectomies:
• Simple appendectomy removes an
inflamed appendix
• Complex appendectomy removes a
ruptured appendix
Because the symptoms are the same
for both types, the clinical team doesn’t
know which type they’re addressing
until they begin to operate.
The standard is to code all append-
ectomies as simple upon admission.

The challenge is that the surgical
procedure and the post-op care for a
simple appendectomy are very different
than for a complex one.
Different medications are required and
the length of stay is longer for complex
appendectomies.
If the procedure isn’t re-coded, the
quality measures will be based on the
wrong set of standards.

Prospective analytics collates data as it
is entered into the electronic health
record (EHR) and alerts the clinician to
the possibility that this procedure initially
coded as a simple appendectomy may
actually be a complex appendectomy.
Also provided are the best practice care
options for the complex appendectomy
patient vs. the simple appendectomy.
It’s a form of decision support.

Prospective and predictive analytics
has many possible applications on
the operational side, such as:
• Predicting patient loads in ER based
on previous events.
• What services are likely to be needed.
• How to allocate radiology resources
based on predicted load.
It is still an educated guess but it
allows organizations to plan for
future events.

Retrospective analytics are good at
identifying problems.
Predictive analytics are good at
anticipating problems.
The prospective approach delivers
its value by validating the gut
instinct of clinicians and healthcare
administrators, with real-time,
evidence-based solutions to
problems, i.e., empirical data.

Sharing Knowledge
One advantage of prospective
analytics is that it can integrate the
multiple variables associated with
each patient and disease process,
and identify likely outcomes based
on existing data and past analysis.
Consider the heart failure example.
The system could secure a follow-
up appointment in a timeframe
most suited for the patient.

Blending Prospective Analytics
into Workflows
Healthcare is filled with great
initiatives that never come to
fruition because they don’t fit within
the workflows of healthcare
professionals.
It’s important to proactively bring
the results of prospective analytics
to clinicians and others as part of
their normal course of work, rather
than forcing them to seek it out.

Blending Prospective Analytics
into Workflows
Consider that the typical primary
care physician has roughly 12 to 15
minutes to spend with a patient.
He/she doesn’t want to spend that
time looking up information.
The most updated information
presented at the point of care should
include all possible outcomes.
This is what drives clinical quality
improvement and ensures
consistency at the lowest
reasonable cost.

Driving Adoption
Prospective analytics offers
tremendous possibilities. Keep in
mind that not everyone in the
organization is forward-thinking.
If the organization starts with
retrospective analytics to support
the conclusions drawn by
prospective analytics, it will be
much easier to sell to the skeptics.
Soon you’ll have them making
beautiful, and cost-effective,
music together.

More about this topic
Three Approaches to Predictive Analytics in Healthcare
David Crockett – Senior Director of Research and Predictive Analytics
3 Reasons Why Comparative Analytics, Predictive Analytics, and NLP Won’t Solve
Healthcare’s Problems - Dale Sanders – Executive Vice President, Software
The Practical Use of the Healthcare Analytics Adoption Model
Jarod Crapo – Vice President
Cut Through the Confusion: The 5 Types of Healthcare Analytics Solutions
Paul Horstmeier – Senior Vice President
How Clinical Analytics Will Improve the Cost and Quality of Healthcare Delivery
Dan Burton – CEO, Health Catalyst
Link to original article for a more in-depth discussion.
Healthcare Analytics Advances to the Prospective Stage for Outcomes
Improvement

For more information:

Anne-Marie Bickmore joined Health Catalyst in December 2012. Prior to coming to
Catalyst, she worked for Lantana Consulting as the lead Project Manager (2011-
2012), Director of Informatics at Swedish American Hospital Rockford, IL (2010-
2011), and Intermountain Healthcare serving in multiple leadership roles both clinical
and IT (1999-2011). Anne Marie has dual Bachelor’s degrees in Psychology and
Nursing from the University of Utah.
Other Clinical Quality Improvement Resources
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Prospective Analytics: The Next Thing in Healthcare Analytics

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