Nitin Sen

• Developed and implemented an automated pipeline to process patient visit data from barcodes.

• Managed and optimized data processing at scale, handling up to 2M barcodes per week

• Identified high-risk patients by filtering barcodes with high disease probability scores, reducing false positives by 25% and increasing detection rate by 30%

• Generated projections for business performance and patient outcomes, improving strategic planning and resource allocation

• Compared performance of two NLP models through projection analysis, selecting superior model which improved accuracy by 15%

• Worked on a clustering project to optimize coder performance, assigned coders to clusters (C1, C2, C3, C4, C5) based on their past performance using clustering algorithms.

• Enhanced team efficiency by categorizing coders into performance-based clusters, leading to more effective workload distribution and improved coding accuracy

• Developed Risk Adjustment engine to project revenue for different sweeps.
• Improved FRAME efficiency by implementing accurate RAF score calculations, increasing accuracy of total RAF score from 63% to 94%, matching CMS RAF scores.
• Delivered ad-hoc analyses on time for year-long project, supporting critical business decisions.
• Worked with CMS datasets (MMR, MOR, EDPS, RAPS) with strong understanding of retrospective programs and CMS methodologies for calculating RAF scores and Medicare risk adjustment.