We excel in Chase List Prioritization, ensuring accurate coding and documentation for maximum reimbursement, especially for complex medical conditions
Inefficient reviews delay payments, stressing finances and cash flow and impacting the healthcare organization’s financial stability.
Untimely reviews jeopardize compliance, exposing organizations to penalties and legal liabilities due to regulatory non-compliance.
Without proper review processes, unnoticed errors in documentation compromise patient records and billing accuracy.
Inefficient reviews miss chances to correct coding errors or billable services, resulting in provider revenue loss.
Manual reviews burden staff, consuming time and resources and reducing operational efficiency in healthcare settings.
Chase List Prioritization, powered by AI for Maximum ROI, optimizes chase list targeting by identifying promising charts before extraction, reducing the number of charts pulled, and potentially increasing revenue.
Assess your entire membership to pinpoint suspects for your chase list.
Leverage Artificial Intelligence to analyze encounter data and identify newly developed conditions impacting health risk.
Our approach matches your list against care providers, ensuring responsiveness and efficient chart fulfillment considering all compliant parameters.
Our automated system tracks members in real time, optimizing your campaign for success and resulting in better ROI
See a marked improvement in charting accuracy compared to traditional methods with our comprehensive Data-driven approach.
Our AI-driven Chase List Solution ingests diverse data, enriches it with medical knowledge, and applies deep learning to create precise clinically-tailored chase list models offering Compliant ROI
Chase List Prioritization offers the following benefits to MAOs
Our approach focuses on prioritizing high-ROI members, reducing chart retrieval and coding expenses for significant cost savings.
By prioritizing high-ROI members, our solution enhances coding accuracy, impacting reimbursement and ensuring precise health status representation.
We ensure accurate risk adjustment coding, maximizing MA reimbursement by capturing all relevant diagnoses and conditions for optimal revenue.
Prioritize high-ROI activities, streamline processes, and allocate resources efficiently to boost productivity and financial performance.
Our retrospective chart review solutions empower healthcare providers to accurately assess and mitigate risk while ensuring fair compensation. Integrating MEAT criteria into our efficient chart review processes is vital for meeting the changing healthcare landscape, enabling the delivery of top-tier care and maintaining financial stability.
NLP technology automates diagnosis review and audit tasks, saving time and reducing manual efforts in retrospective risk adjustment processes.
Automatic capturing and verification of HCC codes according to the MEAT risk adjustment guidelines in allows Healthcare organization to consider the overall patient past medical status alongside diagnoses for comprehensive risk assessment.
Knowledge Graph algorithms accurately extract historically relevant data from unstructured clinical notes, enhancing coding accuracy rates and reducing the need for manual coding.
By capturing subtle nuances in past patient diagnoses and comorbidities, In-built AI-powered diagnosis codebooks enable more accurate risk stratification, leading to better resource allocation and patient care.
NLP-powered risk adjustment solutions streamline the workflow, improving efficiency and reducing the complexity of the retrospective risk adjustment process.
Our Chase list prioritization approach optimizes members affecting reimbursement, emphasizing high-ROI activities like chart retrieval, coding, and documentation for efficiency and compliance.
Our AI-powered chase listing technique anticipates high-potential charts in advance, decreasing pulls and revealing members without claims data, resulting in more per capita revenue per chart.
Besides our Chase list prioritization tool, RAAPID’s AI-driven Risk Adjustment solution has numerous offerings that improve clinical workflows for accurate risk capture and optimized reimbursements.
RAAPID’s Technologies conducts detailed chart reviews, analyzing past service dates to extract precise HCC conditions (unreported/unsubstantiated HCC codes) and identifying gaps for improved risk-adjustment payments.
Using Clinical NLP, our Retrospective Risk Adjustment solution auto-captures HCC codes based on MEAT criteria, resulting in accurate coding and reimbursement.
RAAPID’s AI-powered technology conducts detailed chart reviews, analyzing past service dates to extract precise HCC conditions (unreported/unsubstantiated HCC codes) and identify gaps for improved risk-adjustment payments.
Leave all your technological concerns behind – we will take care of it. Let’s move forward from Risk to Revenue
