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How AI and NLP technologies are a redeemer for Payers in Risk Adjustment Space

For a healthcare payer to ensure its population’s risk burden is accurately mapped, exquisite technology tools must be used to monitor encountered data, segregate aberrancies, and quickly & efficiently address any errors if any.

Even before the COVID-19 pandemic hit us, healthcare payers were facing a lot of defiances. From the rising costs and uncertainty about the Affordable Care Act (ACA) to brawls with systems integration, symmetry of incentives with providers, and patient engagement, these entities were challenged with attempting to improve members’ health while addressing an array of hindrances. Like many health care organizations, payers are adopting various types of technology to streamline their workflow and achieve financial and operational objectives.

Advantages of Artificial Intelligence

Perhaps the most harnessed form of technology that has gathered prominence over the years is artificial intelligence (AI). Through AI, payers can dissect large amounts of information with enhanced precision to identify inconsistencies and unusual data patterns. A lot of these variabilities are due to human error.

Adapting AI applications that ameliorate workflows and reduce activities not involved with patient care could save the health care industry by $18 billion annually. AI is even being applied to various applications in medical billing to address impediments to accuracy and productivity. *HBR

The Power of AI Enables Multiple Benefits

 There are many benefits health plans may achieve from a modified and modernized chart retrieval and review exercise powered by artificial intelligence.

  1. Deliver palpable, time-saving operational competence to your provider network – meticulous prediction and prioritization enable fewer charts to be requested because AI can automatically exclude charts that do not support unreported diagnosis codes before they are retrieved.
  2. Increase retrieval rates – analytics inform chart retrieval by identifying retrieval modalities that can be aligned with provider preferences.
  3. Intensify coding efficiency, accuracy, and plenitude – AI predicts and prioritizes suspected disease conditions for coder review. AI-enabled components of a comprehensive solution enable a smarter, highly efficient chart review process while maintaining coding accuracy and fullness.

RAAPID’s AI-enabled analytics can transform your chart review operations because of their accuracy when trained with large amounts of data. It modernizes the traditional chart review process by shifting the focus from the volume of charts targeted to the precision targeting of charts. The result is lesser chart retrieval requests, which decreases provider abrasion and increases the competence of each review.


 RAAPID applies AI to review medical charts and determine the pertinent type of coding review, which lead to accurate and complete records. When charts reach coding, AI-enabled analytics use a three-step process to facilitate structured chart routing.

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A reliable process for risk adjustment

report from KLAS Research stipulates that risk adjustment technologies with quality analytics are potent solutions for payers, ascertain multiple risk indicators, and foster provider collaboration. RAAPID’s Goal through its AI technology is not to replace human interventions but to make human interventions smarter so that patients have better healthcare. Our goal is to make human activities more meaningful. The idea is not to promote any manual reviews of basic information and Make human experts do what matters – Interpretations & Excellence of care.


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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.