1. Succeeding in Healthcare Risk Adjustment
A Guide for Healthcare Providers and Accountable Care Organizations

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The State of Healthcare Risk Adjustment
The United States healthcare system is in the midst of a seismic shift from a fee-for-service
reimbursement model to a value-based one. In a historic announcement on January 26, 2015, U.S. Health
and Human Services (HHS) set a goal of tying 30 percent of traditional, fee-for-service Medicare payments
to quality or value through alternative payment models, such as Accountable Care Organizations (ACOs) or
bundled payment arrangements, by the end of 2016. This included tying 50 percent of payments to these
models by the end of 2018. This is the first time in the history of the Medicare program that HHS had set
explicit goals for alternative payment models (APMs) and value-based payments.
Barely a year after announcing that ambitious plan, HHS announced that it had already reached its goal
in the first quarter of 2016, a full nine months ahead of schedule. It also announced 121 new ACOs, and
higher provider participation rates than expected in other APMs.
These are remarkable outcomes.
It’s thanks in large part to Affordable Care Act-enabled initiatives such as the Medicare Shared Savings
Program (MSSP) and the Center for Medicare and Medicaid Innovation, which allowed for the testing of
new cost- and quality-conscious APMs.
There are similar goals and strategies being aggressively adopted by payers as well. UnitedHealthcare,
for example, cited that their total payments to physicians and hospitals tied to value-based arrangements
have nearly tripled in the last three years to $36 billion (full disclosure: UnitedHealthcare is a longtime
customer of ours). Those payments are expected to increase to $65 billion by the end of 2018.
This is a fundamental shift for healthcare providers that requires them to master two core competencies
that are largely new for most:
1. Risk-Based Contracting — Provider organizations must become experts in risk adjustment and
structuring risk contracts with payers and other providers within their network. Setting accurate
payment benchmarks and optimal reimbursement levels required to cover the cost of care is
important to successfully participate in these programs.
2. Population Health Management — This includes the set of activities and processes required to
deliver the highest quality of care at a reasonable cost. It entails stepping beyond an acute, episodic
care delivery model to a more coordinated, continuous care management approach that emphasizes
proactive outreach and preventative care.
“Improving the quality and affordability of care for all Americans has always been a pillar
of the Affordable Care Act, alongside expanding access to health care,” U.S. Secretary
of Health and Human Services Sylvia Burwell said in a statement. “The law gives us the
tools to put patients at the center of their care, improve quality and help make care more
affordable over the long term.”

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While there has been a significant focus on population health management with an array of solutions
and consulting services that help organizations address this, the criticality of optimizing reimbursement
levels for risk-based contracts is often overlooked. This is evident by the latest ACO performance data
published by the U.S. Centers for Medicare & Medicaid Services (CMS), where more than 96 percent of
participating ACOs met the quality benchmarks. However, only 27.6 percent of these organizations were
able to set accurate expenditure benchmarks that allowed them to generate shared savings above their
minimum savings rate. Most ACOs failed to realize that improving quality doesn’t necessarily translate
into increased shared savings. This is why excelling in risk adjustment—and understanding the levers that
determine their shared savings rate—is so important.
Although there are differing techniques used to determine reimbursement relative to risk, quality and
performance levers, one of the most prevalent is risk adjustment. Risk adjustment is currently being used
to set benchmark payments for Medicare Advantage, ACOs, Commercial Exchanges and many others. For
organizations that are currently taking on risk or looking to do so for these populations, it is critical for
them to understand and accurately manage risk adjustment.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2013
95% 96%
24.7%
27.6%
2014
Received Shared
Savings Payment
Successfully
Reported Quality
ACO Performance

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Why is Risk Adjustment so Challenging?
At its core, risk adjustment requires a fundamental change in how clinicians and coders approach
documentation and coding. Typically, they focus on coding the acute or chronic conditions related
to the patient’s reason for the visit.
For risk adjustment purposes, however, clinicians and coders are required to review, document and code
all chronic conditions and statuses for the patient regardless of whether they are related to the reason for
the visit or the chief complaint. This is a significant departure from coding practices in most organizations
and has been a huge stumbling block for risk-bearing organizations.
A recent study published in the Canadian Medical Association Journal showed that 38.1 percent of
diagnosed diabetic patients in the analyzed population did not actually have a diabetic diagnosis coded
in their medical record. They were only identified by the presence of diabetic medications and lab values.
As you might imagine, there is a significant downside risk for providers who fail to identify and code their
diabetic patients, which impacts reimbursement, because these organizations are still responsible for
covering the costs associated to treating and managing these patients.
100%
Coronary artery
disease
Congestive heart
failure
Cardiovascular
disease
DiabetesChronic
obstructive
pulmonary
disease
Year1 Year2 Year3 Year1 Year2 Year3 Year1 Year2 Year3 Year1 Year2 Year3 Year1 Year2 Year3
17%
11% 10% 11% 8% 7%
18% 16% 16% 12%
100% 100% 100% 100%
Physicians are not providing a complete and accurate listing of ICD-9/10 codes for
members, particularly those with chronic diseases. All of the conditions listed are chronic,
yet only 17 percent of members with, for instance, coronary artery disease are coded
with this condition in the second year and only 11 percent are coded in the third year.
Coding for Chronic Conditions: The Dismal Record
Source: Reden Anders

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Moreover, patients with chronic conditions coded in a previous year must be seen and coded again in
the present year, which means that provider organizations must be proactive around scheduling patients
in for a visit—which, for many organizations, is another departure from established practice.
For providers who are currently focused on improving clinical documentation and coding best practices,
this is typically an extremely time-consuming and labor-intensive process. In some cases, it involves
hiring a number of additional risk adjustment coders or chart auditors who perform retrospective chart
reviews to identify missed codes. This is inefficient and has significant overhead costs associated to it,
as identified patients need to be brought back in order to assess, manage and document these conditions.
To alleviate this, some organizations are looking to put the onus on clinicians to better identify potential
or previously diagnosed and coded conditions at the point of care. Given how little time clinicians get
with their patients, and the diminishing amount of prep time they have in advance of patient visits, such
strategies are little more than set-ups for failure. Who has the time these days to sift through numerous
charts and the patient’s historical documentation to identify these conditions? While this sort of set-up
can have benefits, typically there is a large miss rate as conditions and associated codes get overlooked,
especially if they are buried deep within a patient’s history.
For organizations that wish to proactively schedule patients in to ensure previously coded chronic
conditions can be reevaluated and properly coded for the current calendar year, understanding exactly
which patients fall into the cohort can also be a significant challenge.
Scenario 1: What Was Coded
Condition
ICD-10
Code
HCC Risk
Score
Diabetes Mellitus with
diabetic nephropathy
E11.21 0.368
Peripheral Vascular Disease,
unspecified
I73.9 0.299
Chronic Obstructive Pulmonary
Disease, unspecified
J44.9 0.346
RAF Score: 1.013
Total Payment: $10,130
Scenario 2: What Should Have Been Coded
Condition
ICD-10
Code
HCC Risk
Score
Diabetes Mellitus with
diabetic nephropathy
E11.21 0.368
Peripheral Vascular Disease,
unspecified
I73.9 0.299
Chronic Obstructive Pulmonary
Disease, unspecified
J44.9 0.346
Sick Sinus Syndrome I49.5 0.295
Chronic Viral Hepatitis C B18.2 0.251
BMI 40.0-44.9, adult Z68.41 0.365
RAF Score: 1.924
Total Payment: $19,240
The Cold, Hard Impacts of Inaccurate Coding
Source: Data based on a Talix customer’s experience

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Six Steps to Excel at Healthcare
Risk Adjustment
If high accuracy coding to support risk adjustment is critical to organizations, and existing
processes are either retrospective, labor-intensive, error-prone or challenging to adopt,
then what can providers do to effectively address this?
In order to excel at risk-based contracts, providers must leverage data analytics and focus
on six key capabilities:
1. Prospective Risk Assessment — The motto here is “do it right the first time”
rather than relying on chart auditors to do laborious chart sweeps retrospectively
to identify codes that were either missed or miscoded the first time. This is extremely
labor-intensive, inefficient and costly as it entails bringing patients back in to see
the doctor. In a lot of cases, patients do not come in for their visit, resulting in these
conditions not being reviewed and associated codes not being captured. Prospective
risk assessment is the process by which providers leverage analytics applications
to identify high-risk conditions before the patient’s visit, optimize the patient’s care
plan accordingly, and then accurately code the visit. Prospective risk assessment
therefore has a dual benefit of accurate coding as well as improved care delivery.
2. Point-of-Care Integration — In order for clinicians to be able to use these analytic
insights at the point of care, it is important that these be seamlessly integrated into
their existing workflow (i.e., their electronic health record). To drive high adoption
rates, there should be minimal clicks to access this information with sufficient
supporting evidence that helps clinicians determine whether it is still an active
condition for the patient and what the appropriate course of action would be.
3. Self-Sufficiency — Currently, providers rely far too heavily on payers for
prospective risk assessment. While claims data is beneficial, there are also limitations
with it, because (a) it is retrospective, as it is claims-based; and (b) it is incomplete.
If conditions were not coded in the first place, then this would not be found in claims.
As providers increasingly take on more risk, it will be important for them to control
their own destiny. By leveraging powerful new patient data analytics applications,
the treasure chest of clinical data within provider institutions and across their
clinically integrated networks can be used to mitigate such limitations, and better
identify potential high-risk conditions that their patients may have.

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4. Comprehensive Patient Record Analysis — In addition to structured data found
within the electronic health record (EHR) or enterprise data warehouse (EDW), there
are a number of conditions often diagnosed and documented in unstructured data
such as specialist notes, radiology reports, consult notes, etc. With unstructured
data comprising up to 80 percent of all healthcare data, it is important for providers
to comprehensively mine this valuable data asset.
5. High-Accuracy Condition Detection — To alleviate the manual, time-consuming
process of sifting through chart after chart, providers should leverage patient data
analytics apps that automate this process. When identifying and selecting such a
solution, accuracy—both in terms of precision and recall—is vital so as to limit false
positives and misses.
6. Continuous Improvement — Given the criticality of this, as it directly impacts
reimbursement and care delivery, it is important that providers continually look for
areas of improvement, whether these are the top diagnosis codes that seem to be
missed more than others or documentation gaps and best practices that reduce
ambiguity and capture condition specificity.
Mining Unstructured Patient Data : The Power of Analytics
Insulin, Long term (current) use of Insulin
Condition/Finding
Condition/Finding
Condition/Finding
Diagnostic Procedure
Labs
Medications
The patient came in today with ongoing issues with
diabetic control. We have been fairly aggressively
adjusting her insulin. The patient has been on
insulin for a long time. Despite frequent increases in
her insulin regimen, she continues to have somewhat
high blood glucose, most notably in the evening.
The patient underwent an ultrasound-guided core
needle biopsy with clip placement of the 2.3cm mass
in the upper outer quadrant of the right breast.
Metastatic carcinoma was seen in 3/4 sentinel
lymph nodes.
Therefore, the patient was found to have T2N2MX,
stage IIIA breast cancer.
Diabetes
Stage IIIA Breast Cancer
Ultrasound-guided core needle biopsy:
clip placement of 2.3cm mass in the upper
outer quadrant of the right breast
ICD-9: 250.02
ICD-10: E11.65
HCC: 19
ICD-9: V58.67
ICD-10: Z79.4
HCC: 19
ICD-9: 174.4
ICD-10: C50.411
HCC: 12
High blood glucose
Metastatic Carcinoma

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Embracing Risk Adjustment
Opportunities (and Challenges)
Providers are moving into risk-sharing arrangements in ever-greater numbers. The trend is clear
and the benefits are many. These include:
• Improved patient care and outcomes;
• Increased practice efficiency;
• Lower overall cost of care delivery; and
• Better top- and bottom-line financial performance.
At the same time, providers are challenged with changing patient engagement, practice management,
and workflow processes to achieve these outcomes. In other industries, such challenges have been
overcome through a combination of highly effective software applications and shifts in operational
cultures. While in the past, healthcare has generally been slower than other industries to adopt
innovative technologies to drive efficiencies and improved operational outcomes, that’s changing
now that the costs and benefits are so pressing.
The degree to which providers effectively leverage all available patient data in concert with proven data
analytics applications will make all the difference between success and failure. Together with strategic
changes in practice processes and procedures, providers can profitably manage risk-adjusted patient
populations all while delivering improved care at lower overall costs.
About Talix
Talix provides healthcare risk management solutions to help providers, payers and accountable care
organizations address the challenges of value-based healthcare and risk-based contracts. Its SaaS
applications leverage patient data analytics to turn structured and unstructured health data into
actionable insights that drive improved risk adjustment, better patient outcomes and reduced costs.
For more information, please visit www.talix.com.