Automated claim integrity system

A system for managing claim integrity in healthcare revenue cycle management, comprising:

• a processor;
• a memory storing instructions which, when executed by the processor, cause the system to execute operations, to:
• receive a first dataset from a plurality of first data sources, including one or more electronic health records and one or more payer records, wherein the first dataset includes a plurality of historical medical claims associated with approval outcomes and denial outcomes, wherein each historical claim includes one or more first attributes, including procedure codes, diagnosis codes, payer identifiers, and historical denial reasons;
• using a plurality of machine learning engines, train a plurality of machine learning models based on the received first dataset, wherein the training includes adjusting weights of nodes in gradient boosted decision trees based on patterns detected in one or more claim denial outcomes from the plurality of historical claims;
• receive a second dataset from a plurality of second data sources in real-time, wherein the second dataset includes a plurality of medical claims awaiting submission and associated with one or more medical procedures, treatments, or services, wherein each medical claim comprises one or more second attributes;
• based on the trained plurality of machine learning models, process each medical claim from the plurality of medical claims by one or more machine learning engines, by:
• extracting one or more claim features from each medical claim, including one or more of payer-specific patterns, provider histories, and coding relationships;
• applying the trained machine learning models to generate a denial probability score and a denial category classification for each medical claim;
• and
• generating explainability values using Shapley additive explanations (SHAP) analysis to identify one or more features that contribute to the denial probability score;
• for each medical claim having the denial probability score exceeding a threshold value:
• identify one or more corrective actions based on the denial category classification and the explainability values;
• execute automated corrections to one or more medical claims using one or more automation agents configured for specific denial types;
• and
• re-score the one or more medical claims based on the automated corrections;
• and
• upon verifying the denial probability score is below the threshold value, send
• the one or more medical claims to payer systems for processing;
• receive remittance data from the payer systems, including the medical claim adjustment reason codes for any denied claims;
• and
• retrain the plurality of machine learning models based on the remittance data to improve future denial predictions,
• wherein the system for claim integrity in healthcare revenue cycle management operates as a closed-loop learning architecture that continuously improves denial prediction accuracy and maintains medical claim integrity by detecting and correcting deficiencies in medical claims before submission to payers.