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1. Using Analytics to Drive
Effectiveness in Medicaid
Copyright ©2012 Elsevier, Inc. All rights reserved. Details provided in this document are
for information purposes only and, unless specifically agreed to the contrary by Elsevier
in writing, are not part of any order or contract.
New Medicaid
Enrollees by 2016
Most spending is for the
elderly and disabled
High-Cost Medicaid Patients are
more likely to have dual medical
and behavioral health diagnoses
24 Million
$$$
$347 Billion

2. Without advanced
analytics, both state
Medicaid agencies
and Medicaid
health plans will
operate blind.
2
In the past two decades, Medicaid spending has
grown by 450%. By 2020, Medicaid will cover nearly
100 million Americans, or more than 30% of the U.S.
population.
Medicaid expansion in the states will seriously
exacerbate the influx of large numbers of new
patients with existing, undertreated, undiagnosed
and poorly managed conditions, as well as limited
provider capacity, making efficient identification and
stratification even more important.
Without advanced analytics, both state Medicaid
agencies and Medicaid health plans will operate
blind. So, too, will physicians and other caregivers.
As the U.S. health care system transitions from a
pay-for-service model to pay-for-value through new
arrangements including bundled payments and the
Accountable Care Organization (ACO), health care
organizations require advanced analytics solutions to
track, analyze and report on quality and cost across
every care setting.
Introduction
This eBook is designed to help Medicaid health care professionals be more effective in managing Medicaid beneficiaries and
the providers who treat them. To do that, a practical perspective is explored on how analytics are being used to drive change
in health care (generally) and, in turn, are driving organizational effectiveness in Medicaid Plans and Agencies, in light of the
rapidly evolving health care landscape. It begins by providing a common understanding of health care analytics and ends with
direction on how to evaluate your own organization and discover which opportunities will further its effectiveness in treating
the Medicaid population.
If you have not built the power of these high-value analytic assets into your clinical and financial programs, it is likely that
your organization is not maximizing the insight that is available to drive effectiveness for your Medicaid population.
How Organizations Are Using Analytics to Drive Effectiveness
in Medicaid Quality of Care

3. With the increased
interdependence
and focus on sharing
of risk fostered by
health care reform,
organizations that
have strong data
assets and make use
of them effectively
to drive change
will have a very
distinct competitive
advantage.
3
Health care services are becoming more patient-centered and are rallying around themes of accountability, value
and collaboration.
Providers and Medicaid Health Plans are actively committing themselves to one another in an environment of
cooperation and shared success. Together, they are changing their clinical and business processes to achieve
results from positive patient outcomes and efficiency.
It is important going forward that these organizations have a command of their analytic assets. But more
importantly, what is not readily seen by outsiders is that they are embedding their analytic assets into these
partnership programs rather than using them in an isolated manner. These activities demonstrate that shared
analytic assets are increasing shared organizational success. See Figure 1 below:
Analytics have been an active part
of the health care industry since
the 1990s when they were largely
financially driven. Providers, health
plans and government agencies
have always accessed their analytic
data assets to promote sound
organizational processes and goals.
How the analytics have been applied,
and the amount of value that has
been derived from their use has been
up to each organization. Success or
failure in driving value from analytic
investments has also been up to the
individual organization but varies
widely across the nation.
However, with the increased inter-
dependence and focus on sharing of
risk fostered by health care reform,
organizations that have strong
data assets and make use of them
effectively to drive change will have a
very distinct competitive advantage.
A health care organization that drives success through predictive
analytics is a much more desirable health care partner to the
government that underwrites Medicaid programs.
Medicaid
Managed Care Plans,
Programs & Analytics
Provider Programs
& Analytics
Government
Agencies & Analytics
Program C
Program C Program C Program C
Program D
Program D Program D Program D
Program B
Program B Program B Program B
Program A
Program A Program A Program A
Providers
Patient
Payers Government
Agencies

4. A key factor to
remember is that
the term “analytics”
does not just refer to
data but what you
do with the data and
how you change
business processes
to leverage it.
4
Defining the Term “Analytics”
The term “analytics” is commonly used in health care,
but there does not appear to be a single definition of
what it truly means within health care organizations.
And while we health care analytics experts use the
term and are passionate about the topic as we do
business, it is necessary to take pause and create
clarity. At one end of the spectrum, some use the term
generically when referring to reporting on data sets.
At the other end of the spectrum, some use the term
to refer to complex statistical predictions and data
mining activities. Both can be correct.
For the purpose of this text, and at the 50,000-foot level,
health care analytics is a science (methodology) as well
as a process.
The analytics process/methodology involves accessing
sets of raw data (often created from business and clinical
transactions related to patient/provider encounters), and
studying them to draw conclusions and gain insights that
were unknown.
These conclusions and insights are not inherently a part
of the raw data. In other words, the transactional process
that created the raw data was not specifically designed to
support garnering conclusions or insights.
A key factor to remember is that the term “analytics”
does not just refer to data but what you do with the data
and how you change business processes to leverage it.
In health care, analytics
frequently involve placing sets
of financial and clinical rules
into algorithms that are then
built into analytic technologies.
These technologies are able to
process an organization’s raw
business data to create new,
value-added or enhanced data
elements which are then made
available for business or clinical
use. Consider the following
example of how an organization
transforms large sets of raw,
transactional data into high-
value Predictive Analytics:
A Technical Perspective
1 Highly detailed health care data (such as pharmacy, lab, clinical and
claims information) are fed into a predictive modeling processing “engine.”
The engine conducts a line-by-line review of the large volume of
detailed data.
The statistical, clinical and financial models of the engine work to study
the data, aggregate and organize it.
The engine then creates new “analytic” elements (e.g., Patient Risk
Score, Underlying Risk Drivers, Clinical Measures, Forecasted Patient
Cost, etc.) that are more useful to clinicians and business professionals
than the highly detailed data could be in their raw state.
These new, value-added data elements are then able to be placed
adjacent to the raw data in the database and are made available for
clinical and business use.
2
3
4
5
Organizations that make effective use of analytics are at an advantage because they have enhanced insights.
However, an organization that has these high-value analytic assets does not achieve effectiveness just by having
the analytic data. Organizations that enhance their raw data assets are more likely to be sophisticated in how
they use analytics to guide human decisions or to drive fully-automated decisions. An organization that is highly
competitive is one that transforms the analytics assets into business strategy and operational processes. This
leads to a Practical Application perspective of analytics.

5. The key to gaining
more effectiveness
is having these
advanced analytics
populated in a way
where they can
be accessed and
reported by clinicians
and business
professionals in the
form of aggregate,
query/drill-down,
and ad-hoc and
standard reports.
5
In health care, the individuals that create analytics are rarely the actual end users of the analytics. If a health care
organization creates the analytics internally, it is frequently done by a technology department. It is much more common,
however, for health care providers and payers to purchase vendor solutions that will provide pre-packaged analytics. In both
instances, business users of analytic information tend to be removed from the technical nature that was discussed earlier in
the Technical Perspective.
Vendor analytic solutions are typically business intelligence systems that allow end users to analyze and report on the
organization’s data as well as the value-added analytic data outputs. The user is less concerned about the technical nature
of the analytics and algorithms and is more concerned with how to apply the information to business strategy or operations.
For these individuals, the practical perspective of analytics is that they are value-added data elements that are made
available within analysis and reporting systems, as shown below in Figure 2:
Figure 2 illustrates a practical perspective on how predictive modeling is experienced by end users of analytic
solutions. Note again that Predictive Modeling is shown as having a high degree of competitive advantage.
Access and reporting are where analytics become linked to business process.
A Practical Application Perspective
Source: Image Adapted from Book “Competing on Analytics: The New Science of Winning”
by Thomas H. Davenport and Jeanne G. Harris, page 7.

6. Predictive analytics
provide a critical
advantage
by enabling
organizations to
have insights that
help them to make
the most informed
decisions possible,
given what can be
“known” through
today’s data.
6
How To Use Predictive Analytics To Gain More Effectiveness
Predictive analytics are playing a critical role in helping both providers and health plans to ramp up their effectiveness, and
become fortified to survive while the U.S. health care system transforms. This is particularly true for provider organizations
that are treating Medicaid patients, taking on additional risk or participating in shared savings/incentive programs with the
government, in addition to producing higher quality patient outcomes. Predictive analytics provide an edge to organizations
by offering critical insights that help them to make the most informed decisions possible, given what can be “known”
through today’s data. These organizations are very interested to know the:
1 Data assets must be
made ready for use.
Value must be able
to be extracted from
the data (Predictive
Analytics).
Medicaid organizations
must be able to use
analytic data to
discover opportunities
unique to their
population.
Medicaid organizations
must then go further
to define, deliver and
measure programs
that deliver results
based upon use of
the analytic data.
2 3 4
Types of services that will be required in the upcoming year
Individuals who are not in compliance with recommended treatment protocols
Individuals who have increased in risk over time
Individuals who will have higher levels of engagement
Individuals who are most likely to respond well to medical management outreach
Projected financial risk for individuals as well as sub-populations (i.e., chronic care patients)
Impact of risk on organizational performance against value-based contract expectations
Recall from earlier discussions that the key to gaining
effectiveness through Predictive Analytics is having these
advanced analytics populated into analysis and reporting
systems that the organization can use to support strategic,
operational and clinical decisions. The most effective
Medicaid organizations are highly successful in making
use of analytic assets to gain a competitive edge in today’s
health care market. While each of these organizations
may have subtle differences in what they want to
accomplish, they share commonalities in the data they use
and how they apply it to drive success. They are using it
strategically to define their vision, to define their place in
the market and to drive revenue growth by increasing their
performance against clinical and administrative targets.
More specifically, the most effective health care organizations
use analytics to manage risk while improving clinical
quality, reducing the cost of care and increasing efficiency.
Medicaid organizations that are positioning themselves to
lead during this time of change are not only making use
of their own administrative data assets, but they are also
making use of data assets that can be obtained through
new business relationships that focus on working together
to provide quality, value-based care (e.g., Accountable
Care) to patients. However, this is not a simple task.
Organizations must be able to take the data and make
a specific plan on how it will be applied to clinical and
business activities in order to achieve results.
What Makes the Most Effective Health Care Organizations Different?

7. Having an advanced
ability to measure
helps an organization
to understand its
current state of
performance and set
future goals.
7
Measure, Manage and Repeat
In health care, analytic data elements are particularly good
at measuring something in a way that your organization’s
raw data would not be able to accomplish. And they allow
these measures to be applied to a wide array of health care
topics. For example, Predictive Analytics enable a health
care provider organization to measure the clinical and
financial risk of their overall patient population and also
further measure the risk for sub-populations such as the
Medicaid patients for a specific clinic, a specific provider or
a specific disease like Diabetes.
Having an advanced ability to measure clinical and financial
risk helps an organization to understand its current state
of performance and set future goals. More importantly,
an organization that has an advanced ability to measure
also has the ability to discover specific areas where
Is there a standard method or
road map for how top Medicaid
organizations use analytics?
How will analytic assets impact
our business, both from a process
and financial perspective?
What clinical or administrative
strategies use analytics?
What do analytics have to do
with the detailed steps of my
operational activities?
How will analytics strengthen
my relationships with clinical
and business partners?
The Most Effective Organizations Use Analytics to Lever Change
Many industry articles discuss the power of health care analytics and work to describe the basics (e.g., Health care Analytics
101). They accomplish a heightened awareness that having a strong analytics platform is no longer a nice-to-have asset, but
that analytics are now an operational necessity. However, the challenge with many Analytics 101 articles is that they are not
tactical – they stop short of educating executives and analysts on how to directly apply the analytics to improve business
operations and achieve positive results.
All too often these articles do not provide executives with the information necessary to conduct an assessment of their
organization’s use of analytics, nor do they provide the information necessary to understand where competitive edge
opportunities may exist. In order to leverage analytics in a competitive way in health care, some common questions may be
asked to start moving in that direction.
performance is lower than what is expected or what may
be acceptable. For example, a Medicaid organization that
uses Predictive Analytics has the ability to focus on the
Asthmatic population and determine which patients
have higher clinical and financial risk. The most effective
Predictive Analytics will be able to break this risk down
and show the underlying risk drivers (which may not be
asthma) as well as forecast future issues for the population
such as inpatient admissions, emergency room events, etc.
Analytics are valuable because they measure and bring
information to those who are caring for Medicaid patients
– information that was not otherwise readily apparent.
Once this intelligence is exposed, it can be discussed, and
the organization can determine what actions, if any, need
to be taken to manage performance and to re-measure and
monitor over time.

8. Effective Medicaid
organizations have
learned how to
use analytics to
influence outcomes
of beneficiaries, while
also realizing savings
and reducing risk.
8
The Illusion of Control in Health Management
After the transition to using analytics in measuring performance is achieved, analytics go on to provide a more
significant value in helping organizations conduct the “manage” portion of “measure, manage and repeat.”
What does the provider organization that has a diabetic patient population with high financial and clinical risk do
about managing that risk? What does the Medicaid organization that has a large asthmatic population with a high
financial and clinical risk do about managing that risk?
A significant challenge is that Medicaid organizations cannot control health outcomes – they can only influence
them. Health issues can be treated once they have occurred, avoided through prevention or managed through
lifestyle, etc. But at the end of the day, an organization cannot literally control an individual’s or population’s
health outcome. Yet organizations must deal with repercussions (often financial) of health outcomes. In light of
this challenge, effective Medicaid organizations have learned how to use analytics to influence outcomes of
beneficiaries, while also realizing savings and reducing risk. These Medicaid organizations understand the art of
influence and use the advanced insights that analytics provide to drive the “levers” that are available to them –
the activities that they can do to incent, steer or guide change to occur.
Examples of Levers that
Medicaid Managed Care Plans
Use to Influence Health Care
Quality and Cost
HEALTH AND WELLNESS
Medicaid Managed Care Plans can offer health/wellness
programs, disease management and health risk
assessment programs to populations that may most
benefit from participation or may be at higher levels of
financial or clinical risk, and who are motivated to change.
NETWORK DESIGNS AND
NETWORK-FOCUSED PRODUCTS
Medicaid Managed Care Plans can offer pre-defined
provider networks and can work to actively improve
the depth, breadth and quality of provider networks.
Medicaid Managed Care Plans can also increase levels of
clinical and financial accountability with providers related
to the care they deliver.
Additionally:
• Predict, address, and track needs and costs
of massive new eligible population.
• Build capacity to manage costs of large
number of dual diagnosis patients.
• Anticipate, plan for pent-up demand.
• Focus limited resources on highest cost,
highest risk patients with greatest opportunities
for quality improvement and cost savings.
• Optimize value-based payments and
appropriate risk adjustment.
• Optimize performance on quality measures.
• Operate effective complex case management
programs.
• Implement and manage provider payment
reform in network.
• Better compete for contracts for dual eligible
population and Exchange population.

9. How do you curb
high utilization of
Emergency Room
visits for non-
emergent care?
Clinical and business
activity “levers” are
more powerful when
they are based upon
analytic insights.
9
These clinical and business activity “levers” are more powerful when they are based upon analytic insights. Advanced insight
guides strategy work by highlighting unseen opportunities while also illuminating underlying patterns that may need to
be addressed. Advanced insight guides operational work by helping to hit the target more accurately, address challenges
associated with underlying drivers and help to sophisticate an organization’s ability to prioritize.
Examples of Levers that PROVIDER
ORGANIZATIONS Use to Influence
Health Care Quality and Cost
RISK ARRANGEMENTS WITH MEDICAID
MANAGED CARE PLANS AND STATES
Providers can collaborate with both Medicaid Managed
Care Plans and States through new risk-and-reward
reimbursement arrangements that center on themes
of Accountable Care and Value-based Care, as well as
collaborate in partnership programs that work to increase
clinical integration, quality and efficiency.
PATIENT OUTCOMES
Providers can improve the measurement and management
of patient outcomes according to standards of care
defined by the medical industry. Moreover, provider
organizations can evaluate practice pattern variances
in light of outcomes and work to bring consistency to
system-wide care delivery.
HEALTH AND WELLNESS
Providers can collaborate with health/wellness programs,
disease management and health risk assessment
programs for patients. Using these programs to identify
opportunities for intervention or stronger population
management is key to influencing outcomes.
CARE DELIVERY PROCESS
Providers can improve the processes used to deliver
care to gain efficiency, The level of quality and patient
satisfaction and engagement. Since the Medicaid
population has unique challenges in engagement,
analytics provide steps toward a “road map” for caring
for Medicaid beneficiaries and understanding their risk
drivers as well as their level of motivation.
COST MANAGEMENT
Provider Practices can gain internal efficiencies by
analyzing cost and utilization patterns and understanding
established benchmarks, including how they measure
against them. Using these insights to change care delivery
is key to becoming more competitive.
Examples of Levers that State Medicaid
Agencies Use to Influence Health Care
Quality and Cost
MANAGING POPULATIONS – HEALTH
AND WELLNESS
Predicting, addressing and tracking needs and costs of
massive new eligible Medicaid populations, while focusing
limited resources on highest cost, highest risk patients
with greatest opportunities for quality improvement and
cost savings, and who are motivated to change. Patient-
centered medical home programs, case management
programs round out the use of analytics to be a more
effective State Medicaid agency.
NETWORK DESIGNS AND
NETWORK-FOCUSED PRODUCTS
Integrated health plans for dual eligibles – implemen-
tation, risk adjustment, performance management
are all ways that designs for network management is
addressed. Additionally, managing and maximizing
shared shavings with Medicare is another way that
network management is more effective with analytics.

10. 10
There is a traditional advantage in making strong use of
analytics to drive your organization’s internal performance.
But in the rapidly evolving health care market, it has
become clear that a Medicaid organization’s success is
increasingly dependent on the ability to become a more
desirable clinical and business partner by sharing the
power of analytic assets and using them in key areas to
drive improvements and lower costs.
Health care reform is fostering new levels of cooperation
and is increasing the shared financial risk between
health care providers, Medicaid plans and States.
Organizations that own analytic data assets and go the
distance to actively share these assets through cooperative
programs will achieve higher levels of effectiveness than
those that do not use information as strategically.
As your organization creates a strategy for success in
this environment, keep in mind that analytic assets
are escalating in value. Market leading health care
organizations have analytics represented on their strategic
road maps – directly (and most importantly) as key drivers
of operational programs. They have them, and are building
them into how they do business.
Key Considerations
We invite you to actively investigate your own health care
organization by considering the following method:
1 2
3 4
Look to see if the data that guides your organization’s
strategies is maximized – meaning that it relies heavily
on the power of advanced health care analytics
(including predictive analytics) rather than relying
solely on aggregations of raw operational data.
See if your operational programs (Patient Health
Improvement, Cost/Risk Management, Efficiency,
Quality, Performance Management, etc.) are built
using advanced analytics that are available to opti-
mize each program for success.
Spend some time scratching beneath the surface
of your operational business programs and find out
exactly which analytic data elements are being
utilized. If analytics are not driving your programs,
it is a red flag that your organization is not using the
insight that is available to drive your competitive
edge.
Compare the analytic assets used in your opera-
tional programs to industry best practices as well
as what market leaders use to drive their programs
(examples noted earlier). If your programs are not
built with the depth and breadth of market-leading
analytics, it is an indicator that your organization has
opportunities to further its competitive edge.
The appendix that follows is designed to support your investigation by sharing our industry expertise. In these pages,
we highlight the types of programs that you may wish to evaluate – programs that are likely operating across various
departments within your organization. More importantly, this appendix will point out the types of analytics that market-
leading Medicaid organizations are using within their programs along with a description of what each analytic provides.
From our unique vantage point, we can see best practices and market-changing activities, and our goal is to share our knowl-
edge so that your organization can benefit from what we see.
Leading Medicaid organizations have a command of the levers that are available to them, and they have a command of
their analytic assets. They achieve success by building advanced analytics into their clinical and business programs, which
operate to lever the desired outcome. Analytics help define a program’s strategy and are also used within operational steps
of programs. While the industry uses a wide array of programs, they generally fall into five categories. Within each of these
five categories, there are a number of key analytic assets that are being used to drive their success.
Analytics help define
a program’s strategy
and are used to
achieve success in
Medicaid clinical and
business (financial)
programs.

11. 11
Primary Focus
The primary focus of these programs is to improve the health of patients/beneficiaries. With these programs the organization
is saying, “I want to improve the health of my patient/beneficiary population because healthy people have a higher quality of
life and cost Medicaid plans (and States) less.”
#1
PATIENT
HEALTH
IMPROVEMENT
PROGRAMS EPISODES OF CARE
Provides as complete a picture as possible of a disease or medical
condition for a single patient/beneficiary or a population and includes
inpatient, outpatient, professional and pharmacy services. Episodes of
Care are frequently used to analyze the cost patterns, service patterns
or provider practice patterns underneath specific diseases or medical
conditions, and are excellent at supporting the analysis needed for
patient health improvement programs.
RISK DRIVERS
Describes the underlying risk factor(s) that drove the patient/
beneficiary’s risk forecast. Health improvement programs can use Risk
Driver information to provide additional insights to consider when
planning resources to include in health improvement programs. The
risk drivers also provide insights to what is driving the risk, which may
be different than “primary” diagnosis.
FORECASTED RISK INDEX
Provides an index number for a patient/beneficiary that
describes the relative risk the patient/beneficiary has (for utilizing
services and incurring cost) when compared to the rest of the
Medicaid population. Risk indices are frequently used to stratify
patients/beneficiaries in patient health improvement programs. This
takes into account risks associated with dual diagnoses – medical
diagnoses combined with mental health diagnoses (e.g., depression,
schizophrenia, substance abuse, etc.)
DISEASE MANAGEMENT
Targets specific diseases or medical conditions, typically chronic diseases such
as Diabetes, Asthma, Hypertension, COPD, Heart Failure, etc.
PREVENTIVE CARE
Focuses on making sure people are getting the routine medical care necessary to
proactively manage/monitor health, which leads to the avoidance of degrading health
and resulting costs.
WELLNESS
Ensures maintenance of healthy lifestyles, exercise and eating habits
Examples of
Patient Health
Improvement
Programs
Examples of Analytics Used in Patient Health Improvement Programs
APPENDIX: How to Apply
Predictive Analytics to Clinical
and Business Processes

12. 12
FORECASTED EMERGENCY ROOM (ER) VISITS
Forecasts the number of ER visits a patient/beneficiary will have in the next
12 months. This forecast information provides insight into which patients/
beneficiaries may have need for program resources that work to eliminate
or reduce the need for ER services. In a Medicaid population, this is an
especially useful insight since some Medicaid beneficiaries tend to use the
ER for primary care services. Targeting not only ER “high flyers” but also
those predicted to use the ER more frequently will result in a more effectively
managed Medicaid population.
FORECASTED INPATIENT DAYS
Forecasts the number of days that a patient/beneficiary will have in an
inpatient facility in the next 12 months. This forecast information provides
insight into which patients/beneficiaries may have need for program
resources that work to eliminate an inpatient admission, reduce the length
of stay or reduce the intensity of inpatient services.
APPENDIX: How to Apply
Predictive Analytics to Clinical
and Business Processes
#1 cont’d.
PATIENT HEALTH
IMPROVEMENT
PROGRAMS
RISK CATEGORY
Provides an easy-to-use categorization of the level of risk for patients/beneficiaries (e.g., 1 = Well, 2 = Low, 3
= Medium, 4 = High Risk and 5 = Catastrophic). Risk categories help to prioritize lists of patients/beneficiaries
within a patient health improvement program and can also be used to highlight sub-populations that may have
differing needs for program resources.

13. 13
GAPS IN CARE
Flags patients/beneficiaries who are non-compliant with evidence-based guidelines of care – guidelines
that have been created by the medical community that describe quality of care requirements for specific
diseases, medical conditions or preventive age/gender demographics. Gaps in Care are excellent for use
in patient health improvement programs because they provide specific information about the treatment
protocols the individual has not received. By closing these gaps, the beneficiaries have a better chance to
maintain optimal health and avoid health decline, and their resulting costs.
ACUTE IMPACT SCORE
Provides a score (0-100) that forecasts the patient/
beneficiary’s propensity for acute utilization to be
impactable. Patient health improvement programs can use
this analytic to channel resources to individuals that have a
higher potential for being medically managed to reduce their
utilization of inpatient and ER services and the related cost.
MOVER MEMBERS
Indicates the patient/beneficiary has increased or
decreased in risk since the last time that risk was measured.
Patient health improvement programs can use this to
channel program resources to individuals increasing in
risk, and see where program resources may intercede and
provide support. Patient health programs can identify
individuals who are decreasing in risk, which may assist
in modifying the type or intensity of resources for those
individuals.
CHRONIC IMPACT SCORE
Provides a score (0-100) that forecasts the patient/beneficiary’s propensity for chronic Gaps in Care to be
impactable. Patient health improvement programs can use this analytic to channel resources to individuals
that have a higher potential for increasing their compliance with recommended treatment protocols.
MOTIVATION RANK
Provides a score (0-100) that forecasts the patient/
beneficiary’s level of motivation to engage with physicians
and self-manage. Patient health improvement programs
can use this analytic to channel resources to
individuals that have a higher likelihood to engage with the
care delivery system and comply with treatment programs.
APPENDIX: How to Apply
Predictive Analytics to Clinical
and Business Processes
#1 cont’d.
PATIENT HEALTH
IMPROVEMENT
PROGRAMS

14. 14
Primary Focus
The primary focus of these programs is to manage the overall cost and the financial risk associated with providing health care
servicestopatients/beneficiaries.Withtheseprograms,theorganizationissaying“Iwanttomanagefinancialimpactthatproviding
these services has on my organization’s budget as well as manage the financial risk associated with my organization’s survival.”
#2
COST/RISK
MANAGEMENT
PROGRAMS
NETWORK UTILIZATION/LEAKAGE
Channels patients to specific health care providers within a desired network
ER UTILIZATION
Ensures that the ER is used only for emergency conditions, delivering care efficiently
within the ER; motivating the beneficiaries to use PCPs, internal medicine and
primary clinics instead of ERs for primary care.
THE IMPACT OF CODING
Focuses on the impact of coding on reimbursement and organization performance
CASE MANAGEMENT
Programs that focus on managing complex and/or costly medical cases through
nurse Case/Care Managers, Nurse Practitioners and affiliated care providers. These
programs focus on co-morbid beneficiaries, including dual diagnosis beneficiaries
and their unique needs for medical and mental health care.
UTILIZATION MANAGEMENT
Programs that focus on specific utilization patterns of concern, such as high-cost
diagnostic imaging services, knee replacement services, gastric bypass services, etc
Examples
of Cost/Risk
Management
Programs
Examples of Analytics Used in Cost/Risk Management Programs
EPISODES OF CARE
Provides as complete a picture as possible of a disease
or medical condition for a single patient/beneficiary or a
population and includes inpatient, outpatient, professional
and pharmacy services. Can be used to illustrate cost,
service and provider practice patterns and are an excellent
tool for analyzing potential savings opportunities.
RISK DRIVERS
Describes the underlying factor(s) that drove the patient/
beneficiary’s risk forecast. This may or may not be the
same as primary diagnosis. Cost/risk management
programs study underlying risk drivers to understand a
specific population and compare risk drivers for sub-
populations of interest.
FORECASTED RISK INDEX
An index number for a patient/beneficiary that describes
relative risk (for utilizing services and incurring cost)
compared to the population. Indices for groups can be
shown at an aggregate level.
FORECASTED DRUG (RX) COST
Forecasts a patient/beneficiary’s cost for pharmacy
services for the next 12 months based on previous 12
months’ data. Cost/risk management programs can use this
to inform program activities that target pharmacy costs or
utilization of pharmacy services for specific populations.
MOVERS
Indicates the patient/beneficiary has increased or
decreased in risk since the last time that risk was
measured. Cost/risk management programs can use this
analytic to quickly identify shifts in risk for the Medicaid
plan (or State).
FORECASTED MEDICAL COST
Forecasts a patient/beneficiary’s cost for medical services
for the next 12 months, based on the previous 12 months’
data. Cost/risk management programs can use this to
inform program activities that target medical cost or
utilization of medical services for specific populations.
APPENDIX: How to Apply
Predictive Analytics to Clinical
and Business Processes

15. 15
Primary Focus
The primary focus of these programs is to identify and fix inefficiencies associated with delivering care. With these programs
the organization is saying “I want to drive more efficiency into the care that is provided so that my patients/beneficiaries
receive the right services, at the right time, in the optimal environment and with the most efficient use of financial resources.”
#3
EFFICIENT
HEALTH CARE
RESOURCE
CONSUMPTION
PROGRAMS
PROVIDER PERFORMANCE
Analyzes provider practice patterns to discover opportunities to increase quality, to
become more cost effective or to manage performance compared to value-based
contract targets.
PATIENT-CENTERED CARE/MEDICAL HOME
Focuses on medical management and rewarding quality patient-centered care,
ensuring that dual diagnoses and disabled populations get the specific care that
they need, while also controlling costs effectively.
ACCESS TO CARE
Ensures that patients/beneficiaries have adequate access to care based upon
geography, service type, diseases/medical conditions, etc. For the disabled Med-
icaid population, this may mean designing services that are tailored to assist this
population in receiving appropriate and timely care, and preventative services.
NETWORK COVERAGE
Ensures that provider networks contain a mix of provider types and service offerings
that fulfill the needs of the patient/beneficiary population.
Examples
of Efficient
Health Care
Resource
Consumption
Programs
EPISODES OF CARE
Provides as complete a picture as possible of a medical condition for
a single patient/beneficiary or a population and includes inpatient,
outpatient, professional or pharmacy services. Frequently, episodes
are used to analyze service or provider practice patterns for specific
medical conditions. They are often used to identify variation from
expected norms and support further drilling to discover potential
opportunities to gain efficiency.
FORECASTED RISK INDEX
An index number for a patient/beneficiary that describes relative risk
(for utilizing services and incurring cost) compared to the population.
A higher risk index suggests that the patient/beneficiary is forecasted
to have a higher utilization of future services, and may benefit from
program intervention.
ACUTE IMPACT SCORE
A score (0-100) that forecasts the patient/beneficiary’s propensity for
acute utilization to be impactable. Efficiency programs can use this
analytic to identify patients/beneficiaries with a higher potential for
being medically managed to reduce their utilization of inpatient and ER
services.
MOTIVATION RANK
A score (0-100) that forecasts the patient/
beneficiary’s level of motivation to engage with
physicians and self-manage. Efficiency programs
can use this analytic to identify patients/
beneficiaries that have a higher likelihood to
engage with the care delivery system and comply
with efficiencies that are built into treatment
programs for their disease or medical condition.
Examples of Analytics Used in Efficient Health Care Resource Consumption Programs
APPENDIX: How to Apply
Predictive Analytics to Clinical
and Business Processes

16. 16
Primary Focus
To identify and improve known quality issues that are high profile targets within the health care system. With these programs
the organization is saying “I want to ensure that I’m doing all that I can to address these important topics for my beneficiaries.”
Examples include reducing re-admissions, increasing patient-centeredness, medication management programs, etc.
#4
QUALITY
MANAGEMENT
PROGRAMS
RE-ADMISSIONS
In the Medicaid population, readmissions are often 50-150% higher in the dual
diagnoses population. Focuses on patients/beneficiaries who are readmitted to an
inpatient facility after having a previous discharge (for the same or any condition).
By intervening with these beneficiaries, there is documented proof that compliance
to medications and avoidance of readmission is improved.
DISEASE MANAGEMENT
Focuses on specific diseases or medical conditions, typically chronic diseases such
as Diabetes, Asthma, Hypertension, COPD, Heart Failure, etc. Additionally, these
programs can also target Mental Health intervention programs, particularly for
co-morbid Medical/Mental Health dual diagnoses beneficiaries.
PATIENT-CENTERED CARE/MEDICAL HOME
Focuses on programs that focus on medical management and rewarding quality
patient-centered care, increasing collaboration among the providers who care for
Medicaid beneficiaries.
MEDICATION MANAGEMENT
Ensuresthatpatients/beneficiariesarecomplyingwiththeirmedicationregimenand
manages the complications of drug interactions if they are on more than one drug.
Additionally targets those beneficiaries who are identified through the analytics
to have abusive patterns of drug use (e.g., obtaining multiple prescriptions of
narcotics from more than one physician, obtaining narcotics from multiple
pharmacies, obtaining narcotic prescriptions that conflict with one another, etc.)
Examples
of Quality
Management
Programs
GAPS IN CARE
Flags patients/beneficiaries who are non-compliant with evidence-based guidelines of care – guidelines created by the
medical community that describe the quality of care requirements for specific diseases, medical conditions or preventive
age/gender demographics. Gaps in Care are excellent for use in quality improvement programs because they provide
specific information about the treatment protocols the individual has not received. Closing these gaps improves quality.
Looking at providers who have high populations with gaps in care (performance quality), as well as specific beneficiaries
who have gaps in care (quality of health), are ways to close the quality gap and improve care as well as cost.
Examples of Analytics Used in Quality Management Programs
APPENDIX: How to Apply
Predictive Analytics to Clinical
and Business Processes
FORECASTED RISK
Ensures that PCMHs and/or Care Managers can be very effective in managing beneficiaries and enabling appropriate
stratification for interventions, referrals to Disease Management programs, etc.

17. 17
#4 cont’d.
QUALITY
MANAGEMENT
PROGRAMS
CHRONIC IMPACT SCORE
Analytics that provide a score (0-100) which forecasts the patient/beneficiary’s propensity for chronic Gaps in Care to be
impactable. Quality management programs can use this to channel resources to individuals who have higher potential
for increasing compliance with recommended treatment protocols, resulting in healthier Medicaid populations.
EPISODES OF CARE
Provides as complete a picture as possible of a disease or medical condition for a single patient/beneficiary or a population
and includes inpatient, outpatient, professional and pharmacy services. Episodes of Care are frequently used to analyze
service patterns or provider practice patterns underneath specific diseases or medical conditions and are excellent at
supporting the analysis needed for quality improvement programs.
MOTIVATION RANK
A score (0-100) that forecasts the patient/beneficiary’s level of motivation to engage with physicians and self-manage.
Quality management programs can use this analytic to channel resources to individuals with a higher likelihood to engage
with the care delivery system and comply with treatment programs. Conversely, it enables stratification of those beneficia-
ries who need the maximum amount of “high touch” to achieve effectiveness.
ACUTE IMPACT SCORE
A score (0-100) that forecasts the patient/beneficiary’s propensity for acute utilization to be impactable. Quality
management programs can use this to channel resources to individuals with higher potential to proactively manage
beneficiaries, resulting in fewer hospitalizations and ER visits, thus reducing costs.
APPENDIX: How to Apply
Predictive Analytics to Clinical
and Business Processes

18. Primary Focus
The primary focus of these programs is to demonstrate an organization’s performance and improvement. With these programs,
the organization is saying “I want to measure performance, identify opportunities for improvement, implement operational
programs to address the opportunities and then demonstrate improvement.”
#5
PERFORMANCE
MANAGEMENT
PROGRAMS
ORGANIZATIONAL PERFORMANCE
Focuses on improving an organization’s performance measures of quality, cost,
patient/beneficiary satisfaction, engagement, etc.
PROVIDER PERFORMANCE
Analyzes the practice patterns of individual physicians or facilities to discover
opportunities to increase quality, to become more cost effective or to manage
performance compared to value-based targets
Examples of
Performance
Management
Programs
Examples of Analytics Used in Performance Management Programs
GAPS IN CARE
Flagspatients/beneficiariesthatarenon-compliantwithevidence-basedguidelines
of care – guidelines created by the medical community that describe the quality
of care requirements for specific diseases, medical conditions or preventive age/
gender demographics. Gaps in Care are an artifact of the quality measurement
process, which is specifically designed to measure performance against care
standards.
FORECASTED RISK INDEX
An index number for a patient/beneficiary that describes the relative risk the
patient/beneficiary has (for utilizing services and incurring cost) when compared
to the population. The risk burden of a Medicaid population is an important
consideration in performance improvement programs, and it is a standard
component built into the equations that calculate performance.
EPISODES OF CARE
Provides as complete a picture as possible of a disease or medical condition for
a single patient/beneficiary or a population and includes inpatient, outpatient,
professional and pharmacy services. Episodes of Care are frequently used to
discover performance improvement opportunities related to cost of services,
efficiency in service utilization or potential quality concerns.
APPENDIX: How to Apply
Predictive Analytics to Clinical
and Business Processes
18

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