Performance Data Analyst
Bridge Medical GPO Trading LLC
مجموع سنوات الخبرة :7 years, 2 أشهر
Identified gaps/missing data fields in master file. Implemented formula for PCI (Price Competitive Index) items against market data. Generated comprehensive analysis report for major tender contract. Lead and manage client data collection, processing, data cleansing and maintenance. Review supply chain methodologies and identify process improvements and cost saving opportunities. Work directly with CEO, managing director, sales team and support for ad-hoc reports required to aid crucial business decision making. Conducted descriptive statistical analysis, and quality-profiled data with preprocessing and cleansing using Python and visualized it with Tableau for individual clients, resulting in a 30% improvement (in an average) in data quality. Developed process flow charts and documents for the methodologies being followed while handling client's data. Continually monitor and track client performance, Automated the calculation process of potential savings and Price Competitive Index (PCI) for the registered customers.
• Filtered large data set of 400 hospital claims and insurance data using excel, thereby making it easier to analyze and identify trends in insurance denials.
• Updated patient database systems accurately, analyzed Medical records performed monthly routine reporting of the data.
• Designed powerful and interactive visualization of EDI rejections using Tableau, which enabled real time tracking of rejected claims resulting in 50% more enhanced claims correction.
• Used PowerPoint presentations to effectively implement and communicate daily workflow to fellow team members, resulting in 20% increase in productivity.
• Assisted and supervised a team of 14 Junior Data Analysts, helping them develop analytical and organizational skills.
• Brought down total AR TAT from 65 to 40 days through timely follow-up on customer claims & received client’s appreciation.
• Delivered excellent customer service and claims management at 99.9% accuracy which was well above stakeholder’s expectation.
