Experience unparalleled productivity and clinical accuracy with our AI-based innovative automated chart review solution with improved medical coding features, ensuring compliant ROI
Reviews are labor-intensive, requiring extensive hours of coordination with several medical teams
Managing and analyzing vast amounts of information can be overwhelming.
Errors in data entry or interpretation can lead to incorrect conclusions.
Failing to meet legal and regulatory standards can result in penalties.
High costs associated with staffing, training, and technological resources needed for thorough chart reviews.
Our three-click retrospective chart review solution boosts RAF accuracy, care quality, and HEDIS compliance for medical coders and auditors. It employs advanced algorithms and machine learning to identify practice gaps and integrates seamlessly with HCC coding tools, making it very cost-effective & seamlessly aligning with regulatory standards.
Deep data analysis to verify and correct discrepancies, optimizing RAF scores.
Data-driven identification of improvement areas, reducing claim rework.
Ensures compliance with standards, reducing penalty risks and improving quality scores.
Customizes services based on detailed data analysis.
Offers Streamlined Process & Integrates with existing health plan systems (MA, ACOs) to improve workflow without disruption.
Enhanced Customization: Adapts tools to meet specific organizational needs for better efficiency and effectiveness.
Our retrospective medical chart reviews solution aims to serve payers, providers, and auditors by proving to be a vital component in the healthcare reimbursement process, ensuring accuracy, compliance, and improved patient care quality.
Efficiently detects missed HCCs and suspicious codes, enhancing coding accuracy and revenue potential.
Skilled coders guarantee precision, ensuring accurate representation of patient conditions and treatments.
Evaluates adherence to guidelines, utilizing expertise in regulatory standards
Provides data for revenue recovery opportunities and process optimization, facilitating informed decision-making and efficiency improvements.
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 system adeptly handles 500-1000 charts containing 6-12 months of data, optimizing RAF scores and accommodating around 100 API requests, resulting in enhanced reimbursement outcomes.
Witness our AI-driven Adaptable solution that is flexible enough to meet organizational requirements for improved Chart Review efficiency and effectiveness.
Provides an extra layer of intelligence to ensure medical claims reviews are accurate.
We enable healthcare MA organizations to swiftly identify and prevent compliance issues, ensuring accurate claim data submission to the CMS.
Leave all your technological concerns behind – we will take care of it. Let’s move forward from Risk to Revenue
