Reimagine Medical Coding with aiH.Automate™

aiHealth goes beyond traditional rules-based engines and Computer-Assisted Coding (CAC) systems by delivering a fully autonomous AI-driven coding platform. Built on pre-trained, high-fidelity AI/ML models, our technology is uniquely tailored for specialty-specific coding, ensuring up to 95% accuracy across both evaluation & management (E/M) and surgical procedures. Our platform seamlessly converts unstructured clinical notes and documents into billable CPT® and ICD-10 codes, enabling direct-to-bill workflows that minimize manual intervention and optimize revenue cycle efficiency. Unlike CAC tools that merely suggest codes for human validation, aiHealth automates the entire coding process, reducing coder workload and accelerating reimbursement timelines.

aiHealth supports Revenue Cycle Management Companies, Management Service Organizations, and Ambulatory Specialty Practices.

aiH.Automate™ deploys advanced deep learning and AI-ML models to transform clinical notes into procedural and diagnosis codes. Our auto-coding engine sends completed encounters directly to billing while routing exceptions to coders for further review. Learn more about aiH.Automate here.

Computer-Assisted Coding (CAC) systems function as a rules-based engine and have historically been used in the inpatient setting, helping medical coders by highlighting potential codes based on clinical documentation. However, CAC systems still require manual review and coding input, leading to inefficiencies and a 100% reliance on human review. In contrast, aiH.Automate™ is a fully AI-enabled autonomous coding platform that goes beyond CAC by automatically generating accurate and compliant procedural & diagnostics codes without requiring manual coding for most encounters. Here’s how aiH.Automate™ stands out:

aiHealth’s platform and workflow engine delivers up to a 95% accuracy rate for select CPT and ICD code ranges. Clients report a decrease in charge lag by 2-3 days and a 3-5% increase in provider wRVUs​. Explore how Twin Cities Orthopedics decreased its backlog from 10.5 days to hours and saved $500,000 in coding costs:

aiH.Automate™ uses AI and deep learning to transform clinical notes into codes, sending them directly to billing and routing exceptions to coders. The analytics dashboard provides full audit trailing and detailed insight into the auto-coding process for full transparency. We put organizations in control of their coding with the ability to mitigate risk by manually setting accuracy thresholds and auto sampling rate that route autocoded notes for quality assurance and final human review.

Autonomous coding is the process of using artificial intelligence (AI) and machine learning (ML) models to automatically assign accurate and compliant medical codes (e.g., CPT®, ICD-10) to clinical encounters, reducing the need for manual coding intervention.

aiH.Automate™ deploys advanced deep learning and AI-ML models to transform clinical notes into procedural and diagnosis codes. Our auto-coding engine sends completed encounters directly to billing while routing exceptions to coders for further review. Learn more about aiH.Automate here.

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