Two variables are required in the value-based healthcare equation if it is to add up to a profitable contract. One variable, optimizing the care for the patient population, is commonly included and is a focus for most healthcare systems involved in managing population health. However, a second variable, getting the right dollars in order to care for that population, is often overlooked. And yet this variable is easier to attain. It’s a matter of appropriately assessing the risk of the population by addressing inaccurate diagnoses coding. Here, we offer four methods for solving this variable: identifying high-risk gaps over time, persistent diagnosis tracking, identifying code adequacy, and identifying likely diagnoses.

1. The Formula for Optimizing the Value-
Based Healthcare Equation ̶ Brant Avondet

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Optimizing the Value-Based Healthcare
Adapt or die. As far back as Darwin,
the principle of evolution has been
applied to organizations.
With changes in the business
environment, those who adapt to
change will thrive and survive while
others die off.
With the move toward value-based
care, organizations need to formulate a
new equation in order to provide the
best care and remain viable.

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Optimizing the Value-Based Healthcare
To impact care and outcomes of the population in this new environment,
the entire equation needs to be optimized.
The Value Based Care Equation
The Right Dollars to
Care for the
Population
Understand the conditions and
severity of the conditions of the
population
Ensure correct coding of the
conditions and severity of the
conditions of the population
Ensure the payments for the
conditions are adequate and
appropriate to care for the
population
Optimize the care
for the Patient
Population
Identify top priorities(biggest
bang for the buck) for improve-
ment resources to address
Design and implement
improvement workgroups to
decrease cost and improve
patient populations
Profit/loss of the
Value Based
Care Contract
Understand the conditions
and severity of the population
Ensure correct coding of the
conditions and severity of the
conditions of the population
Ensure the payments for the
conditions are adequate and
appropriate to care for the
population
+ =

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Receiving the Right Dollars Is Key
If value-based care is an apple tree, the
low hanging fruit is in getting the right
dollars to care for the population.
When the 2014 results for the Medicare
Shared Savings Plan Accountable Care
Organizations (MSSP ACOs) were
released, some facilities determined where
things could have gone better – where the
low hanging fruit was.
The easy pickings were in getting the right
dollars in the door to care for the
population in the first place

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Receiving the Right Dollars Is Key
Each organization identified a large
number of patients that were not
appropriately risk adjusted going
into the year.
As such, the organization did not
receive the dollars to care for more
complex patients than anticipated.
One organization estimated that 90
percent of the low hanging fruit for
its ACO was found by identifying
the risk of the beneficiaries.
Value Based Care Equation
- Current opportunities -
The Right Dollars
to Care for the
Population
Optimize the care
for the Patient
Population

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Assessing the Risk of the Population
To appropriately assess the risk of the
population, we need a new paradigm
where we focus on documenting
diagnoses instead of procedures.
In a very simplified form, value-based
care contracts determine the dollars
per beneficiary by looking at
demographics and underlying
conditions of the beneficiaries.

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Assessing the Risk of the Population
CMS determines the dollars for MSSP
ACOs by the demographics of the covered
population and its Hierarchical Condition
Category (HCC) scoring system.
The HCC score is the proxy for the
underlying conditions of each
beneficiary.
If the HCC scores for individual
beneficiaries are inaccurate, then
the dollars provided to care for them
do not reflect the care they need.

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Assessing the Risk of the Population
Fee-for-service reimbursement is based
almost exclusively on procedures and care
delivered, which is why it is crucial to
ensure that these interventions are
accurately documented.
Though DRGs and other bundled payments
have moved us toward value-based care,
value-based contracts go further, allowing
care organizations to take on a larger part
or all of the risk for the beneficiary.

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Assessing the Risk of the Population
Because the health status of the population
is crucial to determining the payment to the
care organization, the focus shifts from
accuracy of documentation for all
procedures and interventions, to accuracy
of documentation of all diagnoses.
Missing diagnoses result in inaccurate
pricing of how much care a beneficiary
would optimally require during the life of
the contract.

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Four Common Methods for Addressing
Inaccurate Diagnoses Coding
1. High-Risk Gaps over Time
Most value-based care contracts review
past diagnoses going back 1 ̶ 2 years
when calculating the risk score used to
adjust the dollars-per-beneficiary amount.
If the clinician does not document a
diagnosis, then over time that diagnosis
may fall off of the adjustment calculation.
If the condition is chronic and not
regularly documented, the risk score for
the patient may be artificially low.

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Four Common Methods for Addressing
Inaccurate Diagnoses Coding
1. High-Risk Gaps over Time
To quickly identify beneficiaries with
missing diagnoses, compare historical
HCC scores to the current HCC score.
If the past score is high, but the current
HCC score is low, then that patient may
need to be evaluated so persistent
diagnoses can be documented.
Tools that enable comparison of past
diagnoses with current diagnoses ensure
the current picture is accurate and
simplify this evaluation.

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Four Common Methods for Addressing
Inaccurate Diagnoses Coding
1. High-Risk Gaps over Time
High Risk Gap Identifier
• Identify my patients who had high risk scores last year, but currently do not have high
scores. This will enable me to set up appointments with them if they have not yet been
seen in the current year, to keep them healthy and document risks that are still present.
• Prioritize patients by the greatest difference between last year’s score and this year’s
score. This may be done based on location, provider, condition, etc.
High Risk Gap
Identifier creates
worksheet
Provider team or
care coordinator
works the list to
schedule patients
Patients seen
Care provided
Risks Documented
HCC more accurate
Dollars into ACO more accurate
Care delivered to riskiest patients

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Four Common Methods for Addressing
Inaccurate Diagnoses Coding
2. Persistent Diagnosis Tracking
A more granular use case involves persistent
diagnoses that have fallen off or have not been
evaluated and cared for in the current year.
For example, an amputee is most likely still
missing the limb. A worklist of beneficiaries
not evaluated in the last year with historical
diagnoses of persistent conditions ensures
they are seen and treated.
The right risk is then used when determining
the dollars received to care for beneficiaries
and their persistent conditions.

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Four Common Methods for Addressing
Inaccurate Diagnoses Coding
2. Persistent Diagnosis Tracking
Persistent Diagnosis Tracking
• Identify my patients who have had an amputation or ostomy or other persistent
complication that has not been documented this year.
• Create a prioritized list of who I need to see or make sure that when I see them I
document the amputation or ostomy so that it is noted in the record/bill.
Persistent Diagnosis
Tracking creates lists
of patients who have
not had their
persistent diagnosis
documented this year
Provider team or
care coordinator
works the list to
schedule patients
Patients seen
Care provided
Diagnosis noted
HCC more accurate
Dollars into ACO more accurate
Care coordination is more
effective with accurate lists

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Four Common Methods for Addressing
Inaccurate Diagnoses Coding
3. Code Adequacy Identification
Most care providers will pick the shortest or
easiest when choosing diagnosis codes.
One organization discovered physicians often
chose the diagnosis code for uncomplicated
diabetes because it was the first choice in the
EHR, when they should have been choosing
the code for diabetes with complications.
This coding error related to a six figure payment
shortfall. After training and system changes, the
diagnoses distribution was a more accurate
reflection of population risk.

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Four Common Methods for Addressing
Inaccurate Diagnoses Coding
3. Code Adequacy Identification
Code Adequacy Identification
• Identify my providers that use a generic code for a condition where a more specific
code is likely more accurate.
• Provide a view of common diagnosis and provider frequency of using generic
code vs. other similar providers.
Code adequacy
identifier shows which
providers use generic
code more frequently
Provider leaders
determine of
physicians are not
using the appropriate
code (i.e. 250.0 for
diabetes when renal
failure is present)
Interventions to
make it easier to
code appropriately
for conditions that
are most frequently
not coded correctly
HCC more accurate
Dollars into ACO more accurate
Patient risks more accurately
identified for care management

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Four Common Methods for Addressing
Inaccurate Diagnoses Coding
4. Identifying Likely Diagnoses
A large percentage of patients being treated do
not have the proper diagnosis documented.
One way to identify these individuals is to
leverage rules-based engines that evaluate
clinical data to identify probable diagnoses.
The classic example is identifying beneficiaries
using Betaseron without a diagnosis of MS; or
identifying regular use of albuterol inhalers
and/or Singular without a diagnosis of asthma.

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Four Common Methods for Addressing
Inaccurate Diagnoses Coding
4. Identifying Likely Diagnoses
Another example of a practical use of these
tools is an ACO who identified roughly 10% of
its diabetic population did not have a diabetes
diagnosis recorded.
When the ACO leadership reached out to the
primary care teams with this information they
discovered either the clinicians didn’t know the
diagnosis wasn’t recorded or the patient was
diabetic and hadn’t been diagnosed or treated.
Within one month, many of the patients were
seen and had a diagnosis recorded.

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Four Common Methods for Addressing
Inaccurate Diagnoses Coding
4. Identifying Likely Diagnoses
Likely Diagnosis Identifier
• Identify my patients who have a condition but have not been coded for that condition.
• Create simple to complex rules to provide lists of patients with a likely diagnosis sorted
by probability and show why they have this diagnosis.
Likely diagnosis
Identifier creates lists
of patients with a high
likelihood of a
diagnosis.
Provider team of
care coordinator
works the list to
schedule patients
Patients seen
Care provided
Diagnosis noted
HCC more accurate
Dollars into ACO more accurate
Care coordination is more
effective with accurate lists

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Right Dollars to Care for the Right Conditions
Not many of the provider organizations
negotiating value-based contracts focus
on dollars specific to a diagnosis or
population, but eventually, this too will
become more important to the equation
of profitable contracts.
As treatment protocols evolve, optimal
care costs will change. Organizations
that take time to evaluate the true cost of
providing care for various diagnoses will
ultimately be more prepared for this shift.

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An ACO Case in Point
Partners Healthcare, the largest
integrated healthcare delivery system
and ACO in New England, understood
the need to have access to information
about the full scope of services provided
to its patients, including cost and
outcomes of care.
Under the Bundled Payments for Care Improvement Initiative Model 2,
an episode of care includes all of the services a patient receives for a
certain health event, beginning with a qualifying inpatient admission and
ending 30, 60 or 90 days after discharge. Partners needed a way to
reliably capture all of this data to identify promising opportunities to
improve care delivery and outcomes, while reducing cost and waste.

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An ACO Case in Point
Using Health Catalyst’s Late-Binding™
Data Warehouse Platform, Key Process
Analysis tool, and Bundled Payments
analytics tool, Partners has been able to
identify cost-driving clinical areas and
then evaluate the cost and variation
associated with care delivery for patients.
Partners has developed on-demand
access to 48 standardized episodes of
care, with costs and coefficient of
variation while enabling patient care and
service performance improvement
initiatives based on their analytic insights.

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Adding It All Up
Evolving toward value-based care
requires a shift in thinking.
In addition to optimizing the care for the
patient population, accurately defining
the risk of the population is an effective
and easy step toward securing the
needed resources for patient care.
Leveraging tools that identify high-risk
gaps, persistent diagnoses, adequacy of
codes, and likely diagnoses, ultimately,
leads to better patient outcomes and
viable value-based care organizations.

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For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

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More about this topic
Link to original article for a more in-depth discussion.
The Formula for Optimizing the Value-Based Healthcare Equation
How to Prepare for Value-based Purchasing in 4 Steps
Bobbi Brown, Vice President of Financial Engagement
Population Health Analytics: Improving Care One Patient at a Time
Tom Burton, Co-founder and Executive Vice President
The Key to ACO and Value-based Purchasing Success: Lowering Cost Structure
Dan Burton, Chief Executive Officer
Surviving Value-Based Purchasing in Healthcare: Connecting Your Clinical and Financial Data for the
Best ROI Bobbi Brown, Vice President of Financial Engagement
A Guide to Successful Outcomes using Population Health Analytics (White Paper)
Tom Burton, Co-founder and Executive Vice President

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Brant Avondet joined Health Catalyst in November 2014 as an Engagement Lead.
Prior to joining, Brant founded Searchlight Enterprises, a malpractice risk prediction and
online physician ratings research and company. Brant spent eight years in general
management and strategy roles at Cerner Corporation (NASDAQ: CERN) and three
years as an analyst at Monitor Equity Insights. Mr. Avondet holds an MBA with
distinction from Harvard University, a BA in economics and BS in Finance, magna cum laude, from
Brigham Young University.
Other Clinical Quality Improvement Resources
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