pr4healthanalytics 2012 - Workshop on Pattern Recognition for Healthcare Analytics

In today's environment, health care industry must balance between the often contradictory goals of cost reduction and improving quality of care. With growing costs and rising populations comes an inevitable paradigm shift towards accountable care where organizations are focusing on cost reduction, standardized care and quality improvement like never before. In addition, with the information overload in clinical literature coupled with the difficulty in extrapolating evidence from clinical trials to real world settings, providers find it difficult to select appropriate therapy for each patient. Thus far, health care has lagged behind other industries in improving operational performance and adopting technology-enabled process improvements.
It is possible to address many of these challenges by emulating and implementing best practices in health care by analyzing large amount of available information (extensive electronic health records recording patient conditions, diagnostic tests, labs, imaging exams, genomics, proteomics, treatments, outcomes, claims, financial records, clinical guidelines and best practices etc.). This data contains tremendously valuable hidden information relevant both for clinical and non-clinical decision support. At the heart of healthcare analytics is the ability to recognize (identify, classify and discover) patterns from the plethora of information available. As such, pattern recognition plays a pivotal role in the future of healthcare, specifically in healthcare analytics.