SBIR Phase I: AI-Driven Platform for Prior Authorization Automation and Workflow Optimization in Oncology Specialty Healthcare

Award ID: 2507367

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project lies in transforming oncology prior authorization (PA) processes through artificial intelligence (AI) automation. Current PA systems are labor-intensive, causing costly delays in life-saving cancer treatments. This innovation aims to streamline treatment approvals, reducing administrative burdens and supporting faster patient access to care. By leveraging natural language processing and machine learning, the platform could improve workflow efficiency, decrease PA processing times, and increase initial PA approval rates. The commercial potential is significant, with the U.S. oncology clinics market of $454 million. The business model follows a subscription-based software approach, ensuring scalability and recurring revenue. The company projects over $8 million in revenue and break even by year three, positioning itself for long-term growth and acquisition by major healthcare IT firms. This project will advance scientific understanding of AI applications in healthcare administration while providing a scalable, competitive solution to a critical inefficiency in US healthcare. This Small Business Innovation Research (SBIR) Phase I project aims to develop and validate an AI-driven platform to automate prior authorization (PA) processing in oncology clinics, addressing a critical bottleneck in timely cancer treatment. Current PA workflows are manual, time-consuming, and prone to missing documents and errors, delaying treatment initiation by an average of two weeks and leading to increased patient mortality. This project will integrate natural language processing (NLP), machine learning (ML), and reinforcement learning (RL) to streamline PA submission, predict approval likelihoods, and optimize workflows. The research objectives include (1) developing NLP models to extract key clinical data from unstructured medical records, (2) training ML models to predict PA outcomes with high accuracy based on historical and real-time data, and (3) implementing RL-driven workflow automation to optimize PA submissions and follow-ups. The outcomes will establish a foundation for broader deployment across multiple specialties, enhancing healthcare operational efficiency nationwide. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Alastair Monk