Intelligent Prior Authorization

By analyzing two years of claims data from more than 2,700 healthcare institutions across Saudi Arabia’s private sector, and employing machine learning algorithms, our study demonstrates the potential for significant improvements in prior authorization efficiency. The study contains five key steps: 1) We meticulously mapped ICD-10 AM coding rules and service codes to specific clinical use cases to ensure accuracy and relevance; 2) We incorporated time and date patterns to identify trends and optimize authorization timing; 3) We developed advanced prediction models to forecast authorization outcomes based on historical data; 4) We leveraged comprehensive edit code sets to refine and validate coding accuracy.; and 5) We utilized national validation tools and adjudication rules to ensure compliance and accuracy in the authorization process. These rules and insights derived from historical data enabled us to predict prior authorization outcomes with 80 percent accuracy in the first phase of implementation. Our findings indicate a reduction in authorization turnaround times and an improvement in the accuracy of approvals, ultimately benefiting both healthcare providers and patients.