The healthcare industry must emphasize the importance of minimizing the often unnecessary cost burden associated with reworking of claims due to denials. This can be accomplished by identifying possible claim denials and correcting them before submission. The key to improving this aspect of the revenue cycle is real-time data.
More than 20% of initial claims are reported to be rejected by the payers. Once the claims are rejected, some of them are reworked and the payment is recovered. However, there is a significant percentage of healthcare claims that providers do not see reimbursement for. It is essential for the healthcare organization’s financial health to reduce the number of claim denials by identifying the possibility of such denials at an early stage.
Real-Time Data
Data that is available while being processed or as soon as it is processed is known as real-time data. In the healthcare industry, data cannot be evaluated until the relevant processes are completed within the system in which it is being generated. “Real-time” refers to when the data is first available for evaluation.
Acquiring Real-Time Data
Data that has been collected over time is called historical data. The analysis of historical data allows professionals to make better business decisions for the future. The healthcare industry has traditionally acquired data on claim denials only after the insurance payer sent back the remittance advice. Now, with the availability of real-time data and with the help of Artificial Intelligence (AI) tools, providers can know if a claim might be denied immediately after its creation.
AI models are trained on historical data to learn denial patterns. For example, a model could be trained on 1000 claims previously sent to the payer – 750 approved and 250 denied for various reasons. Once the model is trained, a new claim can be passed through and the model will predict whether the claim will be approved or denied. Advanced AI models can also identify the possible causes of denial. These claims can then be reviewed and corrected prior to submission.
Real-Time Machine Learning in RCM
Real-time machine learning uses real-time data to continuously train models. As the data is collected, the model can make improvements to its predictions. It can take providers anywhere from 45 to 60 days to receive remittances for claims previously submitted. On the other hand, real-time machine learning uses the remittances as soon as they are received to train and improve the model in real-time.
RCM Improvement
The goal of every revenue cycle management specialist is to ensure the revenue stream of the providers and maintain profitability. Real-time machine learning allows providers to identify anomalies within their dataset and correct possible claim denials prior to submitting the claims to the payer. Providers can now use AI solutions empowered by real-time data to ensure that they are submitting the most accurate claim possible.
Healthtek’s Solution
Healthtek offers solutions for providers to optimize their revenue cycle, including the management of claim denials. The solution uses AI models to not only predict possible denials but also provide potential reasons for denial as well. These solutions use real-time data to train and improve models. Medical coders and billers can then get an idea of what may need to be edited in order to submit the best claim possible. For more information, visit our tools page or contact Hannah Merachnik at [email protected].
