Optimize Prior Authorization Processes

The prior authorization (PA) process is a significant barrier to timely patient care, particularly for oncology treatments. PA requires manual review of requests, causing delays that negatively impact care quality [1].

Over 80% of PA requests are initially denied [2], and the process can take up to 14 hours per week per physician in administrative work [3].

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• Each year, millions of patients experience delays due to PA.
• In the U.S., more than 50% of physicians report significant delays caused by PA, directly affecting patient health [4].
• This problem generates additional costs to the healthcare system and increases administrative burdens.

Implementing a predictive AI model to automate validation and predict approval probabilities for PA requests. This would reduce review times and speed up treatments. AI can efficiently process large volumes of data, reducing administrative time, improving accuracy, and minimizing human error [2].

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• The synthetic dataset for the model was constructed informed by industry research and articles, ensuring a realistic representation of conditions surrounding PA requests. The model leverages variables such as comorbidities, the severity of health conditions, and medication details, as analyzed in works by McKinsey & Company, Joseph from Forbes, and Psotka et al. from the Value in Healthcare Initiative's publication. These resources allow the model to simulate the intricacies of real-world PA processes.

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