These are slides from a talk that I gave at Strata Rx in San Francisco on 10/17/2012.
It is clear that data are core to solving big problems in health care, and data science is the skill set needed to extract insights and make them actionable. Using lessons from experience from consumer internet (LinkedIn & online advertising) and a large dataset of clinical and claims data from across the US, we will discuss results from efforts to increase the quality of care, decreasing cost, and increasing hospital efficiency. Real-world use cases will be presented detailing the use, implementation and impact of deploying predictive analytics.
Examples of use cases to be discussed: - predictive modeling around identifying patients at high risk for overutilization (e.g., many return visits to the ED), allowing for proactive and less expensive care to be provided - using recommendation systems to identify procedures and charges missed during billing, resulting in recovered revenue for the hospital - identifying payer claims likely to be denied and why, to enable more efficient coding of charges - providing rich contextual data for physicians to allow them to maintain or increase the quality of care while decreasing cost