Presenter: Zach Landis-Lewis, PhD


Introduction: Sub-optimal performance of healthcare providers in low-income countries is a critical and persistent global problem. The use of eHealth in these settings is creating unprecedented opportunities to automate healthcare performance measurement and the creation of performance feedback for healthcare providers to support clinical learning and behavior change. However, barriers to generating individualized, eHealth-based performance feedback in low-resource settings are not well understood.

Presenter: Louis Leff, MD, FACP

Abstract: UPMC has collected data on EHR use since the mid 2000’s, initially tracking the adoption of computerized provider order entry at UPMC hospitals. CPOE use data was extracted from the Cerner EHR and collated in a custom database called eSTATS. By 2009 eSTATS grew to include information about the vast collection of UPMC order sets and their use patterns at UPMC. Soon, a comprehensive order set development and management interface was added to eSTATS, and eSTATS now serves as the down-time solution to order set access should Cerner EHR be unavailable.

Presenter: Michael Becich, MD, PhD

Abstract: University of Pittsburgh is leading the PaTH (Pitt/Penn State, Temple and Hopkins) Patient Centered Outcomes Research Institute (PCORI) Clinical Data Research Network (CDRN) project which is focused on building a Learning Health System (LHS) for the Mid-Atlantic region. This seminar provides an update on this important project and highlights the work of our partners at Penn State, Temple and Hopkins.

Presenter: Xia Jiang, PhD

Abstract: This talk is mainly about an introduction to the proposed research of a newly funded R01 grant titled “A New Generation Clinical Decision Support System.”

Presenter: Victoria Khersonsky
Friday, April 10, 2015
1:15 - 2:15 pm 
IS Building, Room 501, School of Information Sciences, 135 N. Bellefield Ave.

Dr. Fei Wang, Associate Professor
Department of Computer Science and Engineering, University of Connecticut

"Generating Effective Features from Electronic Health Records"