Finding a Solution to Healthcare’s Patient Matching Problem

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Ben Moscovitch
Manager, Health Information Technology
The Pew Charitable Trusts

Ben Moscovitch manages the health information technology initiative at The Pew Charitable Trusts, which aims to improve the safety of electronic health records and enhance the exchange of patients’ information among health care providers. Previously, he worked on Pew’s medical devices project, advancing policy reforms to support innovation, patient safety, and quality improvement. Before joining Pew, Moscovitch previously worked as a journalist, covering medical product regulation and legislation. Moscovitch received a Master of Arts from Tel Aviv University and a bachelor's degree from Georgetown University.

Pew is an independent, nonprofit organization which conducts fact-based research and rigorous analysis to improve public policy, inform the public, and invigorate civic life.

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February 28th, 2018 1:00PM ET

Patient matching errors can cause unnecessary harm to individuals trying to access care by hindering effective care coordination and utilization of care services. A multitude of common problems — ranging from incorrect and missing information to variation in data standardization and formatting — makes tackling patient matching a complicated task. Despite large-scale support for the development of patient matching solutions based on the use of unique identifiers or algorithms, patient identification remains a challenge for providers.

With errors in patient identification leading to safety risks and increasing the cost of care, patient matching should be a top priority for healthcare leaders. The inability to positively identify patients undermines efforts to improve clinical quality and patient experience, both of which factor highly in value-based care initiatives.

The Pew Charitable Trusts Manager of Health Information Technology Ben Moscovitch and Regenstrief Center for Biomedical Informatics Director Shaun Grannis, MD, MS, FACMI, will lead a discussion of the impact of patient matching on utilization, revenue, patient satisfaction and interoperability and potential solutions to the problem.

Learning objectives:
  • Making the business case for prioritizing patient matching
  • Understanding limitations of current patient identification methods
  • Assessing the merits of available patient matching tools
Dr. Shaun Grannis
Director of the Regenstrief Center for Biomedical Informatics and Associate Professor of Family Medicine
Indiana University School of Medicine

Dr. Shaun Grannis, MD MS FACMI, is Director of the Regenstrief Center for Biomedical Informatics and Associate Professor of Family Medicine at the Indiana University School of Medicine.  He co-leads the Informatics pillar for Indiana University’s Precision Health Initiative and collaborates closely with national and international public health stakeholders to advance technical infrastructure and data-sharing capabilities. His research focuses on developing, testing, and implementing novel patient matching approaches and other data integration strategies to improve discovery and decision support in a variety of contexts.

This webcast is approved for up to 1.0 continuing education (CE) hours for use in fulfilling the continuing education requirements of the Certified Professional in Healthcare Information & Management Systems (CPHIMS) and the Certified Associate in Healthcare Information & Management Systems (CAHIMS).