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Red Flag OIG Chart Reviews

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Red Flags: OIG Chart Reviews of Medicare Advantage records

Healthcare fraud and misuse is a federal offense and negatively impacts care delivery on the whole. It impacts healthcare costs exceptionally and therefore needs to be effectively mapped and measured. Timely prevention and identification of healthcare fraud will help save millions of dollars that can be better utilized in providing value-based care (VBC) for patients.

OIG chart review reports contain findings of its audits and evaluations, to evaluate how well the Department of Health and Human Services (HHS) programs are performing, point out ‘risks’ to the people they serve, and recommend necessary action items. It carries out its mission to safeguard the integrity of HHS programs and the health and welfare of the population served by those programs through audits, investigations, and evaluations, as well as outreach, compliance, and educational programs.

For everyone involved in the healthcare fraternity, it is important to understand the red flags and their occurrence & key impacts on revenue cycle management. These will help to identify suspicious medical claims and prevent payment to fraudsters. When a payer is alerted to the possibility of fraud, the medical records may be more closely scrutinized.

When is an investigation initiated?

OIG reviews the information and makes an initial resolution of what action is required. If acclaim or finding appears to be  not credible, the OIG is likely to take one of three actions: 

(1) Initiate an investigation

(2) Initiate an audit or inspection

(3) Refer the allegation to the respective agency/ authority 

After the investigation is initiated, the OIG will then further make out whether the allegation is criminal or administrative.

The Auditing Province

HCC auditing has become more compound with the increased use of electronic health records (EHRs). Some EHRs hang up around a problem list from a visit to visit, even when conditions are no longer being treated. It’s equally bothersome from a clinical perspective when patients have chronic diseases documented in the problem lists but there is no record of any treatment during a calendar year. This ignites a question about quality care delivery.

 Accurate hierarchical condition category (HCC) medical coding affirms the idea that diagnoses are clinically related to conditions with a similar cost of care. CMS obtains the diagnosis codes from claims and creates a risk adjustment factor (RAF) score for reimbursement.

Sounds Perfect! So where is the problem?

  • Are the claims improperly coded?
  • Are the retrospective audits inaccurately supporting services that were never provided?
  • Is CMS somehow reimbursing Medicare Advantage (MA) plans or Medicare Advantage Organizations (MAOs) based on faulty risk scores?
  • Has a value-based healthcare model issue been uncovered?

It’s an undeniable fact that not all claims are properly coded; CMS allows the MAOs to perform retrospective audits to determine whether (or not) diagnoses were present in the documentation to support assigning additional ICD-10 codes for the encounter(s).

The MAOs collaborate with in-house auditors to perform retrospective chart reviews to determine if diagnoses were documented but not included in the claim. Since the audit volume is high, it stands to reason there would also be a fair number of audits that result in lowering HCC assignments since CMS has specific requirements to assign an HCC.

Another key factor: Medical coding

Coding best practices, due to the significant built-in  risk associated with this area, must be regularly scrutinized for intentional and unintentional abuse. Since coding usually includes both the healthcare providers and the organization’s information technology group (integral to the billing process), it is important to establish and incorporate a working relationship between the groups to promote a common understanding of proper procedures.

The role of technology here:

The faster acceptance and implementation of advanced cutting-edge technology in the risk adjustment space can actually clear the way for fraud opportunities. Utilization of natural language processing, as well as structured data analysis to capture and validate HCC codes from clinical encounters, is critical here.

How can RAAPID help

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RAAPID’s HCC Compass and HCC Capture is the World’s first personalized AI Powered Risk Adjustment Coding, QA  & Audit Platform that leverages natural language processing (NLP) and deep learning (DL) and looks at clinical charts and claims both ways (ADDs and DELETEs) to capture to complete, defensible picture of member/patient risk.

RAAPID’s NLP-Powered Solutions are helping risk adjustment medical coders to automate and streamline the patient chart review and audit process, whilst ensuring accurate documentation of HCC and ICD-10-CM codes based on MEAT criteria and federal coding guidelines. 

Key Features:

  • Auto-suggested ADDs and DELETEs
  • 3 Click Chart Review
  • Integrated Codebooks & Resources

To Conclude

A risk adjustment coding professional is recommended to accurately examine all sections of progress notes to determine whether the documentation of the chronic conditions meets the requirement of the risk adjustment models. 

Moreover, the specificity of the clinical documentation is pivotal for risk adjustment coding professionals to determine whether the chronic condition is current and active.

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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.