why most care management programs fail to deliver result

8/12/2019 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.

why most care management programs fail to deliver result

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