The traditional approach to risk adjustment uses claims-based data to calculate risk.   It’s easy to understand the appeal: health plans already have claims at hand, and those claims contain information in a consistent, structured format.  Unfortunately, this claims-based approach to calculating clinical risk is deficient – and it can put an organization at financial risk.

Claims may understate the actual health risk of a member – meaning that risk-adjusted premium payments for that member may be inaccurately low by $100-200 PMPY retrospectively and $1,000-2,000 PMPY prospectively.  Conversely, claims may overstate the actual health risk when insufficient clinical evidence exists – opening the door to potential audits and financial penalties.  In either direction, documentation gaps in claims-based data can have harmful financial consequences.

What makes claims-based approaches to risk deficient?

Claims data show all the services received by a member, providing a broad view of that member’s health needs.  But claims-based information has four major shortcomings.

Claims Data: Partial

Risk-adjusted premium payments are calculated using risk algorithms that take into account the documented risk of a patient.  Traditionally, documentation is collected by a health plan (and CMS) using the claims submitted by providers.  But the diagnoses submitted by providers on claims do not provide a complete picture of a patient’s medical risk – claims were purpose-built for billing, not detailed clinical communication.

Even when claims data are augmented with outside laboratory data feeds, records are still incomplete.   Where possible, health plans augment the information from claims by conducting chart reviews, sending certified coders or nurses out to practices to pull and review charts.    However, this can be disruptive to practices and inefficient.

Claims Data: Slow

Claims data are also slow; claims can lag behind by a month – or 3 to 6 months.  Once a patient is seen, a practice must generate and submit the claim to the health plan; once the claim is reported to the health plan it must be processed and paid.  This process can be disrupted and delayed in a variety of ways, all of which mean that claims-based documentation is not immediately available for use in risk documentation capture – or more importantly, interventions to improve patient health.

Claims Data: Misaligned

It is one thing to know a patient has elevated risk; it is another to intervene with appropriate care and services.  In the traditional model, health plans analyze claims data and generate long lists of patients who might need to be seen or where documentation might be missing.  The plans send these lists to providers on a periodic basis, but providers often struggle to operationalize the information.  The lists may wind up being discarded because they lack two key elements: aligned attribution and scheduling detail.

It is common for plans and practices to be misaligned on member attribution, or for a member to regularly see a provider who is neither their plan-assigned PCP nor their practice-assigned PCP in the EHR. When this misalignment occurs, it makes it difficult for a practice to use a list of target patients generated by the plan.

Claims Data: Irrelevant

Even if the plan and practice are aligned on attribution, the lack of scheduling detail is another barrier to operationalizing reports from the plan.   Without looking up records individually in the EHR, the practice has no idea whether a given patient has an upcoming appointment that would offer the chance to address the care gap.  Opportunities that are buried in the list go unaddressed, missed because the data was not relevant at the point of care.

THE FIX: ENHANCING CLAIMS WITH EHR DATA

When claims are enhanced with clinical data from the EHR, a much more accurate and useful picture of risk emerges.

Claims and EHR Data: Complete

EHRs are a much richer source of clinical information than claims – and extracting data directly from the EHR (Arcadia’s preferred approach) yields even richer data than a standard extract like a CCD.

Claims v CCD v Arcadia

EHR data can be used to identify gaps in risk document that would result in inaccurate premium payments – or situations where the clinical documentation does not support the coding on the claim.  And with an aggregated data asset containing both claims and EHR data, a health plan can automatically chart audit all members simultaneously to identify potential documentation gaps.  By targeting the most likely opportunities for documentation improvement, the hit rate for chart reviews can be improved to 50% or more.

Claims and EHR Data: Timely

EHR data can be refreshed in near-real time, reflecting the actual pace of practice operations.   If a practice invests in changing a workflow to close care gaps or improve risk documentation, its performance can be monitored in near-real time to ensure the change is successfully adopted.

Claims and EHR Data: Targeted

Using EHR data enables care gaps and documentation gaps to be accurately mapped to the provider best able to address those gaps – overcoming misaligned attribution.

Claims and EHR Data: Relevant

Scheduling opportunities can be used to support care gap and risk documentation gap closure at the point of care, by helping care teams identify the opportunities at upcoming visits.   The rich clinical detail in the EHR can power decision-support tools, helping clinicians ask the right questions about a patient’s care and document the patient’s risk appropriately.

When the traditional claims-based approach to risk adjustment is enhanced with EHR data, health plans can capitalize on opportunities to improve risk documentation (and the accuracy of risk-adjusted premiums) – while also keeping an eye out for inadvertent overcoding that could trigger an audit.

Alyssa Drew

Alyssa Drew is the Strategic Marketing Director at Arcadia, where she helps healthcare systems understand and unlock the value of their data to enable their success in value-based care.   Her background bridges both strategy and technology.   In over five years at Arcadia, she has managed complex analytics and transformation projects for Arcadia clients across the country and served as Arcadia’s Business Practice Leader.  Before joining Arcadia, she held management roles in enterprise analysis, strategic planning, and financial analysis for a $1B organization.

Alyssa has an undergraduate degree in Visual and Environmental Studies from Harvard University.  She has tremendous enthusiasm for the incredible work her Arcadia colleagues do on a daily basis, and is excited to host the Arcadia Healthcare Datathon annually.

August 19, 2016

The Multi-Million Dollar Value of Clinically Enhanced Risk

Organizations that rely only on claims-based information to manage risk are at a disadvantage.  Enhancing claims-based information with rich, high-quality clinical detail from the EHR improves accuracy of risk calculations and associated risk-adjusted premium payments.  An organization with 50,000 or more Medicare Advantage beneficiaries may be leaving $29M on the table by only using claims-based data. 

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