why most care management programs fail to deliver result
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Why Most Care Management
Programs Fail to Deliver Results
By Kirit Pandit
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It is now fairly common knowledge that Care Management (CM)
programs have had mixed success in reducing the Per Member
Per Month (PMPM)cost for a population.
There are many publications that site case studies and compile
savings and ROInumbersfor care management programs across
the country in the last 5 years.
These research publications conclude that most CM programs
that are successful are those that arehighly integrated, high
touch programs.
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Are the CMs going after the right cohort of population?
However, these studies mostly ignore the other important
question.Our recent studies have indicated that most CM programs are
not picking the right candidates for appropriate care
management programs.
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VitreosHealth (formerly PSCI) did a recent study with a Medical
Home population of about 11,000.
We used EMR data for calculating clinical State-of-Health (SOH)risk scores and claims data for calculating utilization (PMPM)
costs.
PMPMcost included both acute, ambulatory, post rehab, andskilled nursing facility.
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Fig 1 illustrates the framework we used to analyze the At-Risk
population. We segment the population on the basis of clinical risk score
and PMPM cost. The clinical risk score is a composite of the individual
disease risk scores and is calculated from EMR (clinical) data that
includes vitals and lab results.
Figure-1
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The top right quadrant (Critical)is the cohort of high cost, high
clinical risk score patients. These patients are clinically risky
based on the current state-of-health and are also high utilizers
today and account for about 50% of the total population spend.
The lower right quadrant represents the cohort (High Utilizers)
that are high utilizers today even though they are relatively at
lower clinical risk based on their State-of-Healthanalysis using
EMR data.
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Typically, they are emergency room (ER) and medication
abusers and are either hypochondriacs, and/or may have socio-
economic and access-to-care problems.
Both these segments are typically identified through claims
analysis in most population and disease management programs
and become high risk candidatesfor care management
programs.
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However, there is a far more important category of patients
which is the upper left (Hidden Opportunity).
This cohort comprises of members that are clinically at higherrisk today based on EMR data analysis, but have historically not
been high utilizers, hence are not identified by claims based risk
scores that are biased towards historical utilization costs.
In most cases, they account for only 10% of the total spend and
have very low PMPM costs, so most of these members are
ignored by CM programs.
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Figure-2
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However, through repeated ACO case studies, we have found
that within 12-18 months, 15 - 20% of the Hidden Opportunity
members transition to the Criticalcategory if they are ignored
by care management programs.
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This is illustrated in Fig 2. Once they move to the right, they
account for anywhere from 40-50% of the spend of the Critical
categorythe following year.
This means anywhere from a quarter to half the spend
associated with the Criticalcategory comes from these new
patients that did not exist at the beginning of the year in the
Criticalcategory.
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Yet, the Hidden Opportunity category is largely ignored. Why?
One reason is that most care management programs are driven
by claims data analysis which cannot identify this Hidden
Opportunitypopulation.
However, predictive clinical risk scores that use both the harvest
EMR data along with claims data can easily identify this hidden
opportunitycohort.
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In addition to using EMR data, these 10-15% of the hidden
opportunitycohort that are the future liabilities can be
identified through a multidimensional risk model which
combines this clinical risk with other risk factors such as
compliance risk, socio-economic risk, access-to-care risk andmental well-being risk.
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VitreosHealth has been able to identify this population
consistently in retrospective analysis.
Once these are identified, published studies have proven that a
high-touch, integrated CM program can successfully reduce the
PMPM by 20-25% and potentially avoid the movement of this
cohort to the catastrophic Criticalsegment.
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Figure-3
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Fig 3 shows that an ideal Care Management program is one
which- Prevents the 10-20% of the hidden opportunity category
from becoming critical. Ensure the high clinical risk patients
P1 do not move to the right criticalcategory.
Makes sure the criticalpopulations health and acuity remains
in check and reduce their utilization through effective case
management and care coordination and move this population to
the left.
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Identifies the causal factors for the High Utilizers(socio-
economic, access-to-care, mental well-being) to design tailor-
made care management programs address their unique mental
and social well-being needs.
Identify future high-risk patients early in the disease cycle (pre-
diabetic, obesity, hypertension, anxiety, etc.) from the current
Relatively Healthycohort and continue to keep them healthy
through fitness, wellness programs and disease counseling.
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It is important to note that traditional claims based analysis can
only provide a partial picture, since they lack clinical records such
as vitals, lab results, family history, etc. which can be used in
disease models to predict more accurate and segment the
population more precisely.
A combination of clinical, claims and demographic dataand a
multi-dimensional risk modelcan segment the population more
accurately and provide the correct candidates to put into a CMprogram.