The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to empower individuals to make financially sound healthcare decisions; helping them navigate the otherwise convoluted and confusing healthcare cost landscape by enabling them to manage, plan, and better understand healthcare expenditures and budget for upcoming and future costs. This project takes a data-driven approach, developing sophisticated, innovative, and large-scale data mining and machine learning algorithms to automatically learn a plethora of cost patterns from over a 100 million healthcare records.
The proposed project will provide a user-friendly, engaging interface for individuals to manage and understand healthcare expenses for themselves and their families. By bringing together ideas in data mining, machine learning and natural language processing to enable our technology, we make fundamental progress in research and development in the field of healthcare informatics. The anticipated results are the development of an algorithmic suite that can be used to model and predict the nature of healthcare costs across regional boundaries and demographic groups in the United States.
Our project focused on empowering individuals to make financially sound healthcare decisions by enabling them to manage, plan, and better understand healthcare expenditures and budget for upcoming and future costs. We took a data-driven approach to the problem, to automatically learn cost patterns from millions of healthcare records, and set out to develop a user-friendly, engaging interface for individuals to manage and understand healthcare expenses for themselves and their families. Our analytics platform bridges ideas from data mining, machine learning, and natural language processing: it integrates data from various disparate sources and it processes data from over 200 million episodes of care. In addition, we have developed a suite of technologically sophisticated, innovative, and large-scale data mining and machine learning algorithms to estimate and forecast personalized charges. Moreover, we have made fundamental progress in the field of healthcare analytics: our algorithms perform temporal analysis (time between events is just as important as the sequence itself) and model high-dimensional interactions. During the process of developing our mobile and web interface, we realized the key to disrupting the healthcare system was to partner with progressive healthcare entities to improve their analytics capabilities using our platform. The broader impact of our work is that we provide actionable insights to these entities to provide better quality care at lower cost.
