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Unraveling the Importance of Retrospective Risk Adjustment for MA Health Plan Reimbursements

As reimbursement procedures grow more intricate, especially in the retrospective realm, risk adjustment plays a crucial role in sustaining the financial health of the healthcare provider/Clinician ecosystem.  Within the medical coding and billing system, retrospective risk adjustment becomes essential for effective risk management.

Let’s delve into Retrospective Risk Adjustment’s significance in optimizing reimbursement.

The primary review procedures for HCC comprise Prospective Review, designed to anticipate future patient encounters; Concurrent Review, which entails the real-time analysis of patients’ codes; and Retrospective Review, which concentrates on assessing the accuracy of codes used for past patients.

Looking back at past claims, retrospective risk adjustment ensures healthcare organizations receive accurate reimbursements by examining the services provided. Retrospective coding reviews, conducted after care delivery and claim submission, often reveal HCC codes that were either not reported despite being supported by medical records or inaccurately submitted without meeting documentation criteria. These assessments typically highlight recurring clinical documentation issues, prompting the need for corrective measures and the resubmission of accurate HCC codes to the payer.

Medicare Advantage Organizations allocate resources to retrospective risk adjustment Chart reviews – Know Why

RAF scores play a pivotal role in determining the payment structure for MAOs. Accuracy is paramount in this context. Retrospective chart reviews serve as a valuable tool for MAOs to enhance the precision of risk-adjustment payments.

By enabling the addition and removal of diagnoses in the encounter data, these reviews are based on a patient’s medical records found in the Electronic Medical Record (EMR). 

This process ensures that MAOs enrolling beneficiaries with more complex health needs receive appropriate compensation for the elevated costs associated with their care levels.

Additionally, inaccurately reported diagnoses to CMS may generate Health Condition Categories (HCCs) that lack validation, resulting in inaccurate enrollee risk scores. This, in turn, can cause CMS to make improper payments (overpayments) to Medicare Advantage (MA) organizations. On the flip side, accurately coded diagnoses that MA organizations fail to submit to CMS can result in improper payments (underpayments).

The contemporary approach to forecasting individual health costs relies on risk adjustment, utilizing diagnosis codes and demographics. Artificial Intelligence (AI) is bridging the divide between payers and medical coders, ensuring the submission of compliant and timely Risk Adjustment Factor (RAF) scores to the Centers for Medicare & Medicaid Services (CMS). Precision and compliance in data are crucial elements in calculating a patient’s risk score.

RAAPID’s Clinical NLP-powered explainable AI solutions leave no space for discrepancies in coding diagnoses and usher the following benefits.

NLP-powered AI-based chart review software solution helps identify chronic conditions along with evidence, look up ICD10-CM codes, map to HCC codes, and calculate enrollees’ RAF scores based on HCCs and demographics.

Key benefits:

  • Optimized Compensation: Healthcare ensures optimized reimbursement by meticulously reviewing clinical charts & past claims, preventing providers from missing entitled revenue, and ensuring financial accuracy in compensation.
  • Enhances Compliance: Regular retrospective reviews aid healthcare institutions in maintaining compliance, identifying and rectifying coding errors to prevent audit complications and penalties, and ensuring adherence to regulatory standards.
  • Optimizes Future Claims Processes: Retrospective risk adjustment not only corrects past mistakes but also refines future claims for Prospective Risk Adjustments. Healthcare plans improve coding practices, streamlining the entire Revenue Cycle Management (RCM) process.
  • Continuous Feedback: Retrospective reviews provide ongoing feedback, vis a vis our human in-loop philosophy keeps coders updated on the latest best practices. This ensures coders remain at the forefront of their profession, continuously enhancing the coding process.

Thus, RAAPID’s Clinical NLP-powered AI solutions enable (MAOs) to experience a positive return on investment (ROI), and increase the productivity of coders & reviewers using human-in-loop solutions, which is why MAOs invest significantly in NLP-powered AI chart review solutions.

Challenges

Conducting retrospective reviews is operationally cumbersome. Plans incur expenses as vendors pursue charts from providers, and both parties employ internal or outsourced coding teams for reviews, coding, and QA. This process, marked by human errors, is costly and time-consuming, lacking a comprehensive risk assessment.

Also, patient condition analysis occurs months post-doctor visits, rendering the risk picture outdated upon assembly. Plans lack real-time sickness data.

Lastly, retrospective risk adjustment leads to provider friction, diverting time from documenting encounters to collaborating with chart retrieval vendors for reviews by multiple payers, disrupting physician office operations.

Nevertheless, a solution to these challenges is what we have in our Kitty

Implementing RAAPID’s Clinical NLP-powered AI solutions can streamline various challenging aspects of retrospective risk adjustment, enhancing efficiency, reducing human error, and alleviating costs and stress for billing staff.

RAAPID can help your organization with retrospective HCC Chart review and has a demonstrated track record of enhancing HCC code capture.

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Disclaimer: All the information, views, and opinions expressed in this blog are inspired by Healthcare IT industry trends, guidelines, and their respective web sources and are aligned with the technology innovation, products, and solutions that RAAPID offers to the Risk adjustment market space in the US.