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Using ML to Resolve Denied Claims

by Muthu Krishnan

The maturity of AI and the recent explosion in the use of ChatGPT use has sparked enthusiasm for using advanced technologies to solve some of the oldest and most vexing challenges in the revenue cycle. Because Machine Learning (ML), can identify complex patterns in data, its use is of particular interest to healthcare revenue cycle leaders, CFOs and other executives. Because of ML’s ability to detect patterns in medical claims data, it is uniquely positioned to forecast actions with the highest probability of successfully resolving a denied claim. Here are some of my thoughts on using ML in the revenue cycle.

Data Preparation

Before ML can be unleashed on claims data, it is imperative that data is preprocessed. Preprocessing refers to a category of work to ensure the claims data is accurate, complete, and properly organized. Often, edits, data cleansing, grouping, and normalizing of the data set occur here. Organizations should have a well-developed data governance strategy for consolidating data from disparate sources and a robust data plan to ensure that ML learns with fresh, high-quality data. Governance may specify how data is cleansed, how missing values are handled, clarification of formatting standards and encoding categorical variables.

Feature Selection

Feature selection involves identifying the most important and informative variables that contribute to the prediction of successful claim resolution. ML models rely on relevant features or variables to make predictions. In the case of resolving denied claims, the model might consider various factors such as demographic and other patient information, as well as diagnosis codes and procedure codes. Other claims data such as provider information, insurance coverage, and prior claim history would be relevant and helpful to understand denied claims and how to resolve them.

Training the ML Model

ML models identify patterns from historical data to make evidence-based predictions. The ML algorithm is trained using a labeled dataset that includes information about the denied claims and the actions that were taken to successfully resolve them. As the model learns the relationship between the input features (claim characteristics, actions taken, etc.) and the desired outcome (successful claim resolution), it begins to self-adjust the model's parameters to minimize the prediction errors

Pattern Detection

Once the ML model is trained, it can detect patterns within the medical claims data. It can identify combinations of features or actions that are most likely to lead to successful claim resolution. By analyzing a large volume of historical claims data, the model can uncover hidden correlations, dependencies, and trends that humans may not easily recognize

Probability Estimation

ML models can provide probability estimates for different actions or interventions based on the detected patterns. For example, the model might predict that resubmitting a claim with additional supporting documentation has a higher probability of success compared to other actions. By assigning probabilities to each potential action, the model helps prioritize the most effective strategies for resolving denied claims.

Decision Support

ML models can also act as decision support tools for claim resolution. By considering the patterns and probabilities, the model can provide recommendations to revenue cycle staff. It can suggest the most appropriate actions to take for a specific denied claim, increasing the likelihood of successful resolution, optimizing the efficiency of the revenue cycle, and lowering denial resolution expenses.

Continuous Learning

ML models can continuously learn and improve their predictions over time. As new claims data becomes available, the model will update its learnings to incorporate the latest patterns and adjust its predictions. By iteratively updating the model, AI can adapt to evolving claim denial trends and improve the success rates of resolving denied claims.

ROI in Machine Learning

There is strong return on investment in machine learning and that benefit gets stronger as more data points are fed into the algorithm. The ROI of the WhiteSpace Health Platform leverages ML techniques to analyze patterns in medical claims data and predict the actions most likely to successfully resolve denied claims. ML translates its findings to guided steps that empower our clients’ revenue cycle teams. ML allows even the newest staff member to prioritize their efforts, take actions that have the highest likelihood of resulting in cash collections, optimize resource allocation, and improve the overall efficiency of the claims resolution process. The combination of ML recommendations and human interaction with this intelligence accelerates and enhances cash collections, lowers administrative expenses, and improves financial performance.

About Muthu Krishnan

Muthu Krishnan is the Chief Technology Officer at WhiteSpace Health. Muthu is known for helping early and mid-stage companies embrace agile methodology that rapidly brings products to market to successfully solve customer problems.

Contact:muthu.krishnan@whitespacehealth.com