ForeSee Medical offers an AI-driven risk adjustment platform that enhances RAF scores and improves HCC coding productivity by up to 10x through advanced disease detection algorithms and natural language processing. The software integrates with existing EHR systems to streamline the coding process, ensuring accurate risk adjustment at the point of care and reducing administrative burdens.
Funding
$41.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
In value-based care, healthcare providers often struggle to accurately capture the complexity of their patient population's health conditions, leading to incomplete risk adjustment factor (RAF) scores and potentially impacting reimbursement and resource allocation. Traditional HCC coding methods can be time-consuming and may miss relevant diagnoses buried within unstructured patient data. This can result in under-documentation and a failure to reflect the true disease burden of the patient population.
Solution
ForeSee Medical offers an AI-powered risk adjustment platform, ForeSee ESP®, that improves the accuracy of RAF scores and increases HCC coding productivity. By leveraging advanced disease detection algorithms and natural language processing (NLP), the platform analyzes patient data from EHRs and clinical notes to identify potential diagnoses and provide real-time risk adjustment insights. The platform integrates directly into existing EHR workflows, offering clinical decision support at the point of care and streamlining both prospective and retrospective coding processes. ForeSee ESP® helps healthcare organizations ensure accurate risk adjustment, improve compliance, and optimize resource allocation for value-based care.
Target Audience
The primary target audience includes healthcare providers, coders, administrators, and quality teams participating in value-based care models, particularly those focused on Medicare risk adjustment and HCC coding.
Features
- AI-driven disease detection algorithms to identify potential HCCs from structured and unstructured data
- Natural language processing (NLP) to extract relevant information from clinical notes and other text-based sources
- Seamless integration with existing EHR systems via FHIR APIs
- Real-time clinical decision support at the point of care
- Prospective and retrospective coding workflows to support various coding models
- Risk Adjustment Analyzer to track medical group's average patient risk score and compare to projected benchmarks
- InstaVu® feature provides direct links to supporting evidence within the patient chart
- V24 to V28 Optimizer simplifies the transition between HCC models