Lessons from consumer web data science applied to personalized medicine and hospital efficiency

Scott Nicholson (Poynt)
Data and Analytics
Location: Plaza Room A Level: Non-technical
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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

Photo of Scott Nicholson

Scott Nicholson

Poynt

Scott Nicholson is Chief Data Scientist at Accretive Health and has a PhD in Economics from Stanford. His team works to find innovative uses of data to help doctors make better decisions about care and to help people be more proactive about their own care. Before moving into the health care industry Scott was a Team Lead, Senior Data Scientist and Economist at LinkedIn where his work focused on using econometric techniques to increase user engagement and identify economically relevant insights from the rich LinkedIn data. Before LinkedIn Scott built real-time bidding and ad selection algorithms at an online advertising startup.

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