Last updated on March 26th, 2024 at 10:46 am
Nowadays AI is utilized successfully and has proven to be an efficient, cost-effective, and reliable solution to cut down inappropriate payment claims worth a million dollars every year. The anomalies and patterns can be detected in less than a minute which helps to decrease fraud, system abuse, and future wastes.
From the provider's point of view, they can be educated well to ensure evidence-based and high-quality alternatives. Learn more to know how the AIML program by Imarticus uses AI to improve the payment integrity process.
AI and Payment Integrity
A huge data volume from the providers, facilities, labs, etc. is integrated with AI-based computer power systems. This recognizes patterns in the data in a very effective and automatic way and helps to identify false claims. However, the billing behavior of the providers is difficult to detect as they are usually dealing directly with third-party enterprises for handling billing and coding issues.
This outsourcing may result in missing clarity and inconsistent processes which can ultimately lead to upcoding errors and fraudulent claims.
Thanks to the AI certification course, the identification of errors and fraud is a quick procedure with high precision and accuracy and the errors can be avoided drastically.
Interoperability, APIs, and NLP Efficiency
The real innovation lies in the fact that the medical records of the patients can be directly obtained from the providers of EHRs with firm signed contracts.
This kind of interoperability helps in making the tasks work automatically like pre-authorization of the requests as per the need. This saves the manual working hours and makes the entire system run fluidly.
AI-based natural language processing (NLP) can further accelerate the time-saving process by around 40 percent when used on unfiltered data in the review stages. This helps in the augmentation of the staff efficiency and reduction of the costly human resources like nurses.
Integrating technologies like AI, NLP, robotic processing, and machine learning courses can give the payers the advantage of controlling the expenditure. Furthermore, it gives a helping hand to the providers to better manage the revenue systems to have a more unified and fluid cash flow within the system.
Prepayment cost avoidance model
One of the emerging trends of the industry is a significant shift to a prepayment from a post-payment cost avoidance model. It results in cost reduction related to reprocessing, reworking, and claim recoveries. But, the payers have to be super cautious when adopting this method as it is not yet well demonstrated and proven. Payment integrity based on AI is positioned very uniquely and this prepayment cost reduction model is close to becoming a reality in the industry soon.
Educating the providers
To overcome overutilization and fraud claims another approach that can be employed is their pre-detection by the providers themselves even before the claim submission. During the overpayment or appeal recovery process, the providers can be educated about the non-compliance, errors, overpayment issues, or the reasons for service rejection. This can increase the cooperation from the providers and helps decrease the number of appeals made.
On the same lines, AI-based technologies can analyze the data sets and send responses to the doctors, and list all the factors causing the denial of the claim and also about the unnecessary medical care as mentioned in the health plans.
Conclusion
Finally, analytics and solutions based on AI can ensure to cut down inappropriate claims significantly by identifying the wrong claims and acting upon them. Learn AI and improve the healthcare systems by making proper and efficient use of AI-based algorithms and methods.
