Why Most Care Management Programs fails 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 ROI numbers for
  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 are
  highly 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.
• PMPM cost included both acute, ambulatory, post
  rehab, and skilled nursing facility.

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Figure-1
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.
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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-Health analysis
  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
  candidates for 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 higher risk 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 Critical category 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 category the
  following year.
• This means anywhere from a quarter to half the
  spend associated with the Critical category
  comes from these new patients that did not exist
  at the beginning of the year in the Critical
  category.

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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 Opportunity population.
• However, predictive clinical risk scores that use
  both the harvest EMR data along with claims data
  can easily identify this hidden opportunity
  cohort.

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• In addition to using EMR data, these 10-15 of
  the hidden opportunity cohort 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 and mental 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 Critical segment.

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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 critical category.
• Makes sure the critical populations 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 Healthy cohort 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
  data and a multi-dimensional risk model can
  segment the population more accurately and
  provide the correct candidates to put into a CM
  program.