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Encounter Management & Risk Adjustment

Encounter Management & Risk Adjustment

March 23, 2021

by ACS Solutions

Enabling a Unified Encounter Management & Risk Adjustment Data Strategy in Healthcare

More than ever before, managing risk-adjusted payments requires better data. Payers who don't frequently report adequately or whose encounter data submissions fail in the Centers for Medicare & Medicaid Services (CMS) systems risk getting penalized for inadequate data submissions. From the patient care perspective, flawed or inadequate data submitted to CMS could mean that a plan won't identify members with complex and/or chronic conditions that must be carefully managed for optimal health outcomes. CMS has defined risk adjustment in a way that they are able to predict the future healthcare expenditures of individuals based on data such as diagnoses and demographics.

A majority of Medicaid beneficiaries are enrolled in managed care, which makes encounter data the primary source of information not only about their health and service use but also for details on spending by managed care plans. This data is therefore essential for measuring and monitoring the quality of managed care plans and for evaluating plan compliance with contract requirements.

Parameters of Encounter Data - ACS Solutions

Figure 1: Parameters of a high-quality Encounter Data

At a high level, encounter data creation and submission should be evaluated on four parameters:

  • Complete – meaning the data provides a record of all services rendered to managed care enrollees, and all data in the plan's data set have been successfully transferred into the state's data system.
  • Accurate – meaning the data that managed care plans capture the actual services rendered; when they were rendered (the service date); to whom they were rendered (the enrollee); by whom they were rendered (the provider); and, if a payment was rendered in connection to the service, if so, how much was paid. Plans should also successfully map this information between themselves and the state to ensure that the data stored in the state's system match the data stored in the plan's system.
  • Consistent – meaning the data elements in individual encounter records line up with each other (for example, the procedure codes for the services provided are consistent with the diagnosis codes reported because the latter are linked to conditions for which such services are typically rendered), and all plans submit data and files using the same forms, formats, and definitions.
  • Timely – meaning plans submit all data by the state-specified deadlines so that the state can use the data for program administration and management and can submit data to CMS on time.

Encounter Data Flowchart with Key Processing and Validation Steps

Health plans operating in Managed Medicaid, Medicare Advantage, Dual-eligible, and Marketplace LoBs are constantly challenged by disparate encounter systems and manual processes. These challenges lead to incomplete & inconsistent health data, delays in data submission, and incorrect EDPS (Encounter Data Processing System) and RAPS (Risk Adjustment Payment System) submissions. Overcoming these challenges requires a singular encounter management platform that spans all managed care lines of business and efforts to ensure complete revenue integrity.

The flow chart below shows provider/managed care plan data exchange under fee-for-service (FFS) payment arrangements between Medicaid managed care plans and providers. When managed care plans execute capitated contracts with providers or use value-based or incentive-based payments, the data flow looks similar to the flow shown here. Still, providers submit encounter records instead of claims, and payment may not be connected to the submission of individual encounters.

Encounter Data Flowchart - ACS Solutions
Figure 2: Encounter Data Flow – Standard Providers
The key success parameters for robust encounter management data consists of:

Completeness

  • Enablement of transaction intakes from multiple sources (such as core claims systems and supplemental linked/unlinked transactions) to ensure all applicable data is assimilated
  • Operationalization of intake and generation of compliant outbound encounters within client/state-derived SLA

Accuracy

  • Deployment of pre-built, upfront validations to ensure reasonable encounter data submissions
  • Automation of review and flagging of data inaccuracies before submission (such as the place of service, provider NPI & taxonomies, HCPCS, diagnosis codes, etc.)
  • Enablement of reporting for oversight of data submissions across providers and review acceptance/rejection rates

Consistency

  • Leveraging of state-specific managed Medicaid encounter modules along with business rules and companion guides to address individual state requirements
  • Streamlining of Medicare Advantage RAPS and EDPS submissions and consequent reconciliation to improve first-pass rate accuracy, visibility, and tracking

Timely

  • Consolidation of inbound encounters received from delegated vendors for oversight and timely submissions

Integrating Risk Adjustment with Encounter Management Systems

Claims/Encounter data is at the core of most risk adjustment submissions. All risk adjustment models depend on comprehensive healthcare analytics and evidence-based reporting of patient care. Every year, CMS requires a qualified healthcare provider to establish a health profile for each of their patients, recording all chronic conditions and severe diagnoses.

The CMS then calculates risk-adjusted payments via a risk-scoring formula based on a member's diagnostic and encounter data. The current risk adjustment model categorizes ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) diagnostic codes into hierarchical condition categories (HCCs). Each HCC is weighted according to the prevalent costs associated with the care needed for the underlying condition, and they contribute different values to a member's risk score. Capturing accurate ICD-10 codes documented during face-to-face encounters with appropriate providers is key to reporting accurate HCC data and appropriate risk-adjusted payments.

Risk Adjustment & Member/HCC Prioritization - ACS Solutions

Figure 3: Risk Adjustment & Member/HCC Prioritization

Subsequently, this data is used to predict health costs for the subsequent contract year using HCC risk adjustment coding. Inaccurate or non-specific diagnoses can impact patient care, outcomes, and reimbursement payment for the ongoing care of that patient.

The Medicare Payment Advisory Commission (MedPAC) has recommended that CMS withhold a portion of monthly payments as a penalty to those health plans that submit inaccurate or incomplete data. This ought to make rules about data accuracy and completeness more stringent.

Healthcare organizations are beginning to integrate CMS risk adjustment systems with encounter systems to help search and capture all the applicable conditions of each patient in their population. This enables the generation and transmission of complete, accurate, consistent, and timely data. Using computer-assisted coding that can synthesize the medical record and quickly associate evidence for HCC related disease helps CMS match insurance payment accurately to the resource requirements of a Medicare Advantage population.

Integrating Risk Adjustment with Medicare Encounter Submission

The encounter submission process varies for Medicare, Medicaid, and Marketplace. Hence the data available and the risk adjustment analytics need to be customized for each of these systems. Figure 2 covers the Medicaid process.

In Medicare, the encounter data from various sources (Providers, MS), Independent Physician Association (IPA), etc., are processed to generate RAPS and EDI files for submission to CMS systems (RAPS & EDPS systems). This data is used by CMS for payments to service providers.  Simultaneously, the same encounter data flows back to the payer systems to perform risk analytics and derive a premium amount of the population. The figure below shows the process flow in Medicare.

Medicare Encounters Submission to RAPS & EDPS
Medicare Encounter Submission to RAPS & EDPS - ACS Solutions
Figure 4: Medicare Encounter Submission to RAPS & EDPS

While the submission process for Marketplace is like Medicare, the file formats and the submission systems are different (HMS edge).  The risk adjustment analysis is performed in steps 10 and 11. The figure below details the encounter submission and risk adjustment process for Marketplace.

Marketplace Encounters Submission to EDGE
Marketplace Encounter Submission to EDGE - ACS Solutions
Figure 5: Marketplace Encounter Submission to EDGE

Benefits of Integrated Encounter Management & Risk Adjustment Systems

In addition to helping priority health providers get proper reimbursement and lower the cost of care for members, risk adjustment and accurate capture of conditions have many benefits for Patients, Providers, and Priority Health Programs, such as:

For Patients

  • Receive a treatment plan for their diagnoses
  • Participate in programs offered by Priority Health
  • Stay engaged in their health

For Providers

  • Capture, assess, and understand their patients' entire load of ailments to better manage care outcomes
  • Earn incentive money through our PCP Incentive Program (PIP) and Advanced Health Assessment (AHA) programs

For Priority Health Programs

  • Receive appropriate reimbursements from CMS to cover the costs of care for their patients’ conditions
  • Offer better benefits by lowering the cost of care

Key Building Blocks in Enabling Integrated Encounter Management & Risk Adjustment Systems

  • Create a centralized document repository – For process optimization, all medical records should be stored in one repository accessible to all appropriate users across the payer organization. Metadata should clearly identify the contents of each record for easy retrieval, whether by humans or software agents. Paper records can be converted to optical character recognition (OCR) files to make their data readable. Analytics tools can leverage multiple types of data from the repository and other sources to help identify and collect risk adjustment data. Tools can use claims data to identify diagnoses that may indicate the presence of other HCC codes or identify members with chronic HCCs reported in earlier years but not in a current payment year.

 

  • Invest in next-generation automation and intelligent tools – Intelligent OCR, NLP, and machine learning tools increasingly will automate manual processes, such as preparing EDPS and RAPS submissions, medical record retrieval, medical record coding and review, and analysis of RAPS return files to understand submission errors. Some of these functions may be available as-a-service, enabling payers to gain next-generation capabilities on a cost-effective subscription basis.

 

  • Ensure audit preparedness – Possible regulatory changes include implementing the recurring CMS proposal to extrapolate risk adjustment audit results for repayment across the entire plan population vs. the findings of only the sampled population, which could cost payers millions of dollars in repayments and penalties. Enable a risk adjustment accuracy program that uses automated analytics tools to facilitate coders with documented clinical evidence of precise coding between the claim and patient record codes. These tools help in the significant reduction of the reconciliation process and mitigation of risk exposure.

 

  • Pre-validate patient data – Improving patient care begins with identifying chronic ailments, regulating costs, and determining accurate HCC risk scores and reimbursements. Leveraging technology such as natural language processing (NLP) and machine learning (ML) to deliver data-driven insights to medical coders can enable the collection and assessment of large amounts of incongruent data. Additionally, it can also help in faster identification of risk score-relevant conditions than the traditional review process. Using advanced technology in these areas will allow quicker identification of missed HCC conditions and faster and more accurate closer of documentation gaps, which could mean the difference between receiving or paying out transfer payments.

 

  • Build forward-looking capabilities – Payers can use their experience to anticipate future risk adjustment scores and forecast revenues. The data also can be mined to predict population health trends, identify potential high utilizers, and address their issues and identify social determinants of health (SDoH). Payers gathering and tracking member SDoH data trends now will be equipped to respond quickly as CMS incorporates more of these into its risk models.

 

Conclusion & Next Steps

Payers must develop broad, holistic encounter & risk adjustment management strategies to optimize data accuracy. Quality assurance (healthcare effectiveness data and information set), provider contracting, IT, claims, and provider activities must be coordinated with the risk adjustment workflow to ensure the right data is captured and processed at optimal times, accuracy, consistency, and completeness.

At ACS Solutions, we have been serving leading healthcare organizations worldwide for 20+ years. During this time, we have built deep domain and technical capabilities that can be instrumental in helping you overcome challenges pertaining to the effective implementation of encounter and risk adjustment systems. Contact us today to get the conversation started.

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