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Triggered by legislative reform, health care institutions are adopting Electronic Health Records (EHRs) at an ever increasing pace. These include a detailed account of the context of a patient’s visit, symptoms, procedures, medications, notes, outcomes, and costs. Modeling and understanding this data at scale can give us a powerful lens into the workings of our health system.

At 2.5 trillion dollars, the US spends the highest amount per capita on healthcare while ranking only 36th in the world in terms of the quality of care it offers. At our current growth rate, the system is unsustainable. There is a recognized need to make better use of our health system data to improve the quality of care delivered. For example, can we identify early which patients are likely to benefit from preventative services? Or, mine this data to identify where lapses in care are taking place. By modeling individuals and populations, one can better tailor treatment to individuals.

The purpose of this multi-disciplinary workshop is two-fold:

  1. Learning from domain experts from large healthcare organizations (e.g., Kaiser) and senior researchers from related disciplines like operations research, health services, and statistics.
  2. Techniques and methodologies machine learning community is using and in process of developing to address these challenges

Date: June 20-21, 2013
Venue: Atlanta Marriott Marquis Room L508

A big thanks to everyone who attended to make this workshop a success.

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