Laura Craft, Research Director, Healthcare Industry Research, Gartner

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© 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Next Generation Analytics: The
Backbone of the High Performing
Health System
Health Services Executives BI Event
December 3, 2015
Laura Craft
Research Director
Healthcare Industry
Research
Industry Research – At the intersection of business and IT

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Quality & Compliance Reporting
Cost Analysis
Financial & Operational
Reporting
Market Share Analysis
Contact Forecast and Modeling
Disease Registries
Risk Stratification
Patient/Physician Attribution
Gaps in Care
Outcomes Analysis
Precision Medicine
Quantified Self
Cognitive /Adaptive Learning
Clinical Safety Surveillance
Clinical Deterioration
Detection
Revenue Cycle Analytics
Clinical Effectiveness Research
Genomic Sequencing and
Analysis
Real-Time
Temporal
Analytics
Workforce Analysis
Capacity Management
To boldly go where no analytics program
has gone before

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1. Understanding Next Generation Analytics
2. Next Generation Analytics in Action
3. Recommendations

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© 2014 Gartner, Inc. and/or its affiliates. All rights reserved.
Next Generation Analytics

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 How would you characterize your organizations
use of BI?
1. Primarily reliant on retrospective, historical data for
reporting and analysis – dashboards, drill down, cubes
and pivot tables, etc.
2. Ample use across the organization of data mining and
discovery tools to do root cause analysis, detect patterns
and trends – diagnostic analysis.
3. Uses predictive models to prevent re-admissions, detect
likely hood of sepsis, identify clinical deterioration, etc.
4. Applies adaptive intelligence to decision making,
recommend course of treatment, identify diagnosis etc.
Quick audience poll..

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 More Digital Data
Analytics Capabilities are Evolving
Prescriptive
Analytics
Predictive
Analytics
Diagnostic
AnalyticsWhat
happened?
What will
happen?
How can we
make it
happen?
Why did it
happen?
Descriptive
Analytics
VALUE
DIFFICULTY
Foresight
Insight
Hindsight
Inertia
Imperative
 Advanced Analytic
Tools
 More Complex
Questions
 More Urgency
Around Changing
our Delivery Models
 The ambition for
precision medicine

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Future
Analytic Capabilities are an Increasing Part
of Healthcare’s Decision Making Processes
Future
Past
Create
Awaren
ess a
Decisio
n Must
be
Made
Underst
and the
Scope
and
Context
of the
Decision
Identify
Likely
Outco
mes
Identify
the
Best
Course
of
Action
Report
on the
results
of the
action
Descripti
ve
PredictivePrescripti
ve
Diagnos
tic

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 Query
 Reporting
 Adhoc Analysis
 Cubes
 Pivot Tables
 Drill Through
 Relational
 Algorithms
 Machine Learning
 Adaptive Intelligence
 Cognitive Computing
 Topological Data Analysis
 Temporal Analytics
 Geospatial and Location
Analysis
There is a new set of terminology to
become familiar with
The Common and Familiar The New and Unknown

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© 2014 Gartner, Inc. and/or its affiliates. All rights reserved.
Next Generation Analytics In Action

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On Becoming an Algorithmic Business
“Algorithms are where the real value lies,” said Peter Sondergaard, Senior Vice
President, Research, in the opening keynote at Gartner Symposium/ITxpo in
Orlando. “Algorithms define action,”
It’s not going to be a discrete event—that all of a sudden we go from not using big
data in medicine to using big data in medicine. I view it as more of a continuum, more
of an evolution. As we begin building these models, aggregating big data, we’re going
to be testing and applying the models on individuals, assessing the outcomes, refining
the models, and so on. Questions will become easier to answer. The modeling
becomes more informed as we start pulling in all of this information. We are at the
very beginning stages of this revolution, but I think it’s going to go very fast,
because there’s great maturity in the information sciences beyond medicine.
Dr. Eric Schadt, the founding director of the Icahn Institute for Genomics and Multiscale Biology at New York’s Mount Sinai
Health System

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Algorithms in Action: A Routine Patient
Episode
Catalyst PersonBusinessThing
Based on
symptoms, kiosk
suggests
physician consult,
options for care.
Person doesn't
feel right.
Kiosk
dispenses
common RX,
prints
treatment
instructions.
Private medical
kiosk captures
medical, diagnostic
information.
On-demand
physician consult
opened.
Person
agrees.
Physician asks for
visual inspection and
additional tests
available in kiosk.
Personal and home
monitors confirms high
temperature lower activity
level past few days.
Suggests care locations
nearby.
Kiosk queues
patient based on
symptoms for
virtual visit.
Physician confirms
diagnosis,
prescribes
treatment.
Kiosk orders
replenishment
of test kits
and RX,
disinfects
itself.
• Data is being
streamed daily
• Biometrics are
monitored
• Algorithms are
looking for subtle
changes, early
warning indicators
• Additional data is
pulled into
understand location
and local minute
clinics
• Advise is rendered
• Data is captured
• Intelligent logic
processes data
and based on a
presumptive
diagnosis
suggests
options for care
and which
physicians are
best for a
consult
• Data is risk
stratified for
queuing
patients

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Algorithms in Action: A Pediatric
Emergency Averted
Pediatric Asthma Cohort.
At risk patient population
identified.
The population is
assigned to the
care of Dr. Best. Dr. Best provides
all patients with a
wearable device
that collects
biometrics.
As Riley is playing with her
friends her biometrics are being
collected.
A care
coordinator
is assigned.
Alerts are
sent to Riley’s
device, her
care
coordinator
and her
Mother
1. A risk
stratificatio
n algorithm
has been
used to
identify the
target
population
2. A patient
physician
attribution
algorithm has
been used to
make sure
each patient
has an
accountable
physician in
the right
geographic
location.
3. An
algorithm
has been
running in
the
backgroun
d looking
for subtle
changes in
biometric
readings
and against
personalize
d ranges
for Riley.
An
abnormal
pattern is
detected.
The care coordinator
takes several steps:
1. Calls Riley’s
Mother;
2. Immediately
accesses Riley file
and gets the
current biometric
data;
3. Assesses the
urgency
4. Sets up a virutal
with Dr. Best
4. Levels of acuity
and risk are
calculated
Dr. Best consults
with Riley and her
Mother and
determines Riley
should increase her
inhaler dose for the
next 48 hours.
5. Dr. Best was prompted to increase
Riley’s dosage based on a correlation
of risk and environmental factors.
Pollen levels are projected to
continue to be high, Riley was not
responding to the normal level of
dosage, Riley has a history of ER visit
with uncontrolled asthma during
periods of high pollen.

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New Approaches Simplifying Access to
Complex Logic
“A market for algorithms will emerge.
More accurately, markets for snippets of software that do a specific function.
Imagine a marketplace where tens of millions of algorithms are available”
Peter Sondergaard, Symposium 2015
For the first time,
Medal will make
available its
extensive catalog of
medical algorithms
through the
Apervita Market as
conveniently curated
bundles.
Cleveland Clinic
announced they will
publish their extensive
portfolio of algorithms
in the Apervita Market,
empowering health
enterprises
everywhere to improve
patient outcomes.
Apervita, Inc. today
announced the
University of Michigan
has joined the
Apervita community
to commercialize the
university’s portfolio
of medical algorithms,
assessments and
protocols.
Mayo Clinic will now
make its analytics
available on
Apervita. Mayo’s
portfolio of
algorithms covers a
large number of
specialties, such as
cardiovascular,
pulmonology, and
oncology.

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New Capabilities That Do Cool Things:
Topological Analysis
Asthma Severity
High Low
To need to discern
the subtypes of
asthma and
pinpoint predictors
of severe asthma
from millions of
patient attributes
Problem
Identified six patient
sub-populations –
that had previously
gone unnoticed
Analysis Discovery
Opportunity to
identify the
biomarkers that are
predictors of the
disease and devise
targeted therapies
Opportunity
Applied topological
analysis across a
vast amount of data
points to detect
patterns in the
asthmatic
population.

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New Capabilities That Do Cool Things:
Topological Analysis
High
Problem
• Variation in length of
stay in Total Knee
Replacement
Surgery
Analysis
• Applied topological
analysis across a
vast amount of data
points to look for
clusters of patients.
Discovery
• Identified a cluster of
patient under the
care of there
surgeons. Each who
used the same pre-
surgical anti-
inflammatory
protocol.
Opportunity
• Design a common
protocol/care
pathway
Low length of stay
Low to moderate length of
stay
Long length of stay

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 Opportunity
– Discriminate between two commonly misdiagnosed
diseases
 Data and Analytics
– Echocardiograms consisting of 10,000 attributes from 90
metrics in six different locations of the heart—all produced
by a single one-second heartbeat
– Associative memory engine from Saffron Technology,
combining NoSQL, semantic graph, machine learning and
cognitive distance algorithms based on Kolmogorov
complexity
 Results
– Ability to discern cardiomyophathy from pericarditis—both
which cause heart failure but are complex to diagnose
and require vastly different treatments
– Reduced misdiagnoses from 27% to 10%
The Art of the Possible: Big Data is at the
Heart of Improved Diagnoses

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New Capabilities That Do Cool Things:
Document Mining
Source: http://www-03.ibm.com/press/us/en/pressrelease/44697.wss
Watson assists major Baylor College of Medicine
researchers in generating better tumor
suppression hypotheses
500 known
human kinases
and 10s of
thousands of
possible proteins
they can target
According to Dr. Olivier Lichtarge, professor at Baylor College of
Medicine.,“…there are over 70,000 papers published on this
protein. Even if I’m reading five papers a day, it could take me
nearly 38 years to completely understand all of the research
already available today on this protein. ”
Watson was used to mine the medical literature up to
2003 when only half of the 33 phosphorylating protein
kinases had been discovered. Of the nine found nearly
a decade later, Watson accurately predicted seven.
5000 new
articles/year
on P53 kinases
Time intensive
process to
identify new
relationships
“Our…hope is to
systematically extract
knowledge directly
from the totality of the
public medical
literature. For this we
need technological
advances to read text,
extract facts from
every sentence and to
integrate this
information into a
network that
describes the
relationship between
all of the objects and
entities discussed in
the literature,” –Dr.
Lichtarge, Cullen
Foundation Endowed
Chair at Baylor.
ChallengesSuccessValue
16

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Recommendations

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 Craft an informatics and analytics architecture that will
support great data movement and exchange, expert
mining and discovery tools, virtualization and big data.
Think Logical Data Warehouse.
Recommendations
 Assess the skills that will be needed and create a plan
to fill the gaps.
 Make sure your current analytics strategy leaves ample
room for innovation. Innovation will be a the heart of
becoming an algorithmic business
 Get excited!!! There is endless opportunity to advance
treatment, optimize performance, detect disease
deterioration, etc.

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Wrap-Up
Questions?