In order to determine the health costs of a value-based care patient, Medicare Advantage Organizations (MAOs), Commercial Affordable Care Act (ACA) plans, Accountable Care Organizations (ACOs), and state Medicaid plans perform Risk Adjustment methodology that equates the health status of an individual to a number called Risk Adjustment Factor (RAF) score.
As a health plan organization, you will have a large team of medical coders who identify International Classification of Diseases, Tenth Revision (ICD-10) codes and validate them with the correct Hierarchical Condition Categories (HCCs) for the derivation of RAF scores.
A manual risk adjustment methodology for a vast amount of unstructured data can be energy-consuming, time-taking, and error-prone too.
To address these risk adjustment gaps, many health plans have increasingly been adopting technology options to streamline the process of chart review and audit in a more cost-effective and efficient manner.
How NLP and AI is helping Health plans in their Risk Adjustments
Natural Language Processing (NLP) is an augmented intelligence (AI) technology that is increasing its popularity amongst health plans in recent years. This latest technology is being used by health plans to analyze unstructured patient data including PDF medical records, call center transcripts, and electronic health record (EHR) exports, to enhance risk adjustment processes where manual review and audit are needed.
Considering a Chart Review
RAAPID’s HCC Capture is personalized AI-Powered Risk Adjustment Coding and Quality Assurance Solution that is allowing health plans to scale a chart review process and capture complete risk whilst having a greater understanding and visibility into their members’/patients’ clinical conditions, including symptoms, and medications.
HCC Capture uses NLP technology to accurately read unstructured and structured medical records to precisely identify risk, improving the overall efficiency and return on investment ROI for a health plan.
It is built upon state-of-the-art AI, NLP, Machine Learning (ML), and Deep Learning (DL) models that augment the context surrounding identified information to get a better clinical understanding.
The NLP technology will read charts just like any human coder would do and automatically suggests accurate risk adjustment codes (ICD-10 and HCC) along with MEAT (monitored, evaluated, assessed/addressed and treated) evidence and gaps.
Considering a Chart Audit
RAAPID’s HCC Compass is personalized AI-Powered Risk Adjustment Coding and Audit Solution that is allowing health plans to leverage natural language processing and deep learning to look at clinical charts and claims.
With the NLP technology, you can easily identify up-coded (over-claimed conditions), under-coded (unclaimed conditions), and properly coded conditions.
HCC Compass uses NLP technology to identify and substantiate risk, comply with federal regulations and minimize the Centers for Medicare & Medicaid Services (CMS) audit risk.
The NLP technology is a ‘Human-in-a-loop” solution that eliminates the manual process of HCC reviewers and helps them focus on important tasks including interpretation and decision-making.
The technology is built on modern cloud architecture and application programming interface (API) first approach and allows customized workflows for delivering personalized solutions.
In addition, leading health plans and payers are now using NLP to perform more accurate chart reviews in less time with effective outcomes in numerous business areas including care management, and HEDIS quality measures.