Disease Prediction

The Disease Prediction Models reflect Onasi's advanced approach to precision medicine, utilizing clinically validated artificial intelligence algorithms designed in collaboration with experienced physicians to accurately stratify health risks. These models offer reliable prognostication of the likelihood of developing four significant chronic diseases within the forthcoming year. By employing explainable AI methodologies, the models elucidate their predictions, identifying critical clinical features, contributory risk factors, and autonomously derived decision rules based on comprehensive analysis of real-world data extracted from electronic health records.

Predictive Analytics
The interpretability of the predictions is ensured by providing detailed risk factors and decision-making pathways, enabling clinicians to integrate these insights into informed patient management strategies.

AI-Powered Claim Scrubbing

Onasi AI based claim scrubber plays a crucial role in reviewing claims for potential errors in real time. This sophisticated system thoroughly analyze the data in claims to detect issues such as coding mismatches, missing or incomplete information, and violations of payer-specific rules and guidelines. By cross-referencing claim details against extensive databases, including coding standards (such as ICD-AM, ACHI, or SBSC) and payer policies, the AI flags discrepancies that might lead to claim denials or delays. This pre-submission review ensures that errors are corrected promptly, reducing the risk of rejections and improving the efficiency of the claims process.

Fraud and Overutilization Detection

Onasi AI's intelligent bot is designed to identify and flag suspicious patterns in claims data that could result in audits, rejections, or compliance issues. By analyzing claim submissions in real time, the bot can detect anomalies such as duplicate claims, which occur when the same service is billed more than once, potentially leading to fraud investigations or payer disputes. It also monitors for over-utilization of services, where certain procedures or treatments exceed expected or approved limits according to payer policies or industry benchmarks. By identifying these irregularities early, Onasi AI helps organizations mitigate risks, improve compliance, and maintain the integrity of their revenue cycle management process. The system also provides actionable feedback to address flagged issues, ensuring claims are accurate and appropriately documented before submission.