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How Providers Can Use AI to Improve the Payment Integrity Process

Posted on by Imarticus Learning

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.

artificial intelligence and machine learning coursesInteroperability, 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.

Explainable AI: Escaping The Black Box of AI and Machine Learning

Posted on by Imarticus Learning

With the introduction of machine learning, the vertices of Artificial Intelligence (AI) developed manifold and established their presence across multiple industries. Machine learning helps understand an entity and its behaviors through interpretations and detections of patterns. It has endless potential. But its difficulty is in forming a decision in the first place through a machine learning algorithm.

artificial intelligence and machine learning coursesThere are often concerns about the reliability of machine learning models because of the questions about processes adopted to arrive at an anonymous decision. AI and Machine learning courses help in comprehending extensive data through intelligent insights.

It is useful in applications like weather forecasting, fraud detection, etc. But there is a crucial requirement to understand the processes of ML because it can form decisions using insufficient, wrong, or biased information.

This is where Explainable AI comes into the picture. It is the bridge between the ML Black Box and AI. Experienced AI is a model that explains the logic, goals, and responsible decisive process behind a result to make it understandable to humans.

As per reports by Science Direct, certain models of AI developed early in the process were easy to interpret since they had a certain amount of observability and clarity in their processes. However, with the advent of complicated decision systems like Deep Neutral Network (DNN), the process has become more difficult.

The success of DNN models is a result of productive ML models and their parametric space. It comprises muliple parameters that result in making DNN a black-box model too complicated for users. The search for an understanding of how this mechanism works is at the other end of the black-box model.

A machine learning course makes the process a lot easier. As the need for transparency is rising, the information utilized in ML is no longer justifiable, as it does not provide any detailed explanations for their behavior. Explainable AI along with ML helps in addressing the partial innate of AI. These biases are detrimental in industries like healthcare, law, and recruitment.

Explainable AI consists of three basic core concepts, which are:

  1. Inspection
  2. Accurate predictions
  3. Traceability

Accurate predictions refer to the process of explanation of models about the results and conclusions reached that enhance decision understanding, and trust from users. The traceability factors help humans to intervene in the decision-making of AI and control their functioning in case of need. Because of these features, explainable AI is becoming more and more important these days. A machine learning career is on the rise. In recent predictions from Forrester, it was reported that 45% of AI decision-makers find trusting an AI system is very demanding.

To assist developers to understand ML and explainable AI in detail, IBM researchers open-sourced AI Explainability 360. Google also announced an advanced explainable AI tool. The field of explainable AI is growing. And with it, it will bring enhanced explainability, mitigation of biases, and greater results for every industry.