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

Health plans, globally have begun to realize the pompous value of Prospective risk adjustment programs, and deservedly so. Prospective risk adjustment programs authorize timely, effective interventions including the demonstration of probable gaps in care that support conditions coded to the highest degree of specificity. Paradoxically, retrospective risk adjustment programs limit the potency of code capture. A risk adjustment program that only consists of retrospective chart reviews is short-sighted and does not support the outcomes-driven, population health management which is now innate in most of the payment models.

A comprehensive retrospective risk adjustment solution technologies the traditional chart review process by shifting the focus from charts’ volume to precision targeting charts. The result is minor chart retrieval requests, which decrease provider erosion and increase the productivity of each review.

In prospective Risk adjustment, data is collected as the characteristics or circumstantial changes. In retrospective studies, individuals are sampled and information is gathered about their past.

Transitioning to the traditional Medicare Advantage to retrospective risk adjustment process, Millions of Medicare Advantage medical charts are retrieved and coded manually each year to generate a more complete picture of patient health status. Typical retrospective risk adjustment methods lacked the latest technology and tools to accurately identify medical charts that support unreported diagnosis codes.

RAAPID uses NLP Powered AI-Enabled Risk adjustment solution to optimize the retrospective risk adjustment process. It can be configured to automatically optimize HCC Risk Adjustment Coding & Chart Retrieval Solutions which do not support unreported diagnosis codes. RAAPID’s AI-enabled Risk adjustment solution maximize the productivity of the retrospective risk adjustment process by Prioritizing charts with precision to support unstructured diagnosis codes by Identifying the retrieval modality, and Predicting & prioritizing disease conditions possibly supported in the chart for coder review.

For years, the presiding approach for ensuring that patients’ diagnoses are accurately coded, involved retrospective risk adjustment—employing battalions of medical coders to scrub the medical chart after a patient encounter. Like all risk adjustment solutions, this approach requires documenting and reflecting the true disease burden of patients and populations, so that appropriate resources can be directed to them. Moreover, better coding ensures that providers are reimbursed based on the severity of illnesses which they are treating.

Which is the optimal approach to risk adjustment?

A technology-driven effective mix of all of the above is the answer – A triumphant risk adjustment strategy is heavily dependent on prospective interventions and programs but may need to add on some retrospective elements to address physicians’ needs. So that a deep dive insight on patient care is prioritized by the physicians eliminating “predictive” prescription.

Prospective programs, however being more operationally complex to deliver, are preferred because the ability to impact behavior, at the point of care, is powerful and has significant streaming effects, including higher overall value, impact on ROI, and lesser compliance risk.

Prospective risk adjustment can be the most unique method for obtaining a comprehensive insight into your member population. It also enables forecasting the cost of care for your Medicare Advantage, Medicaid, and Commercial lines of business.

The challenge:

Finding an authentic tech-driven prospective risk adjustment program for Risk-Score Accuracy Improvement that health plans, and physicians would collaborate to reduce costs and improve health, quality, and outcomes is crucial and critical too. It is essential to augment practices with dedicated clinical resources which can curate properly structured information to save physicians time by streamlining coding and mapping gaps in care.

Conclusion

As long as risk adjustment exists, there will be a requirement for retrospective chart review. Prospective programs succeed when they honor physicians’ time. In reality, small hindrances in coding and documentation practices can make a major difference to the accuracy of risk adjustment, while intensifying clinical value. A more effective approach requires the right amount of physician insight to support risk adjustment activity, and it effortlessly integrates that effort into existing workflows.

It’s not a cakewalk, but for plans willing to take on the risk, the ultimate results will be more than worth it.

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.