dination, analytics, and others undertaking data-oriented
Consider whether you should or can apply innovation to
data creation (patient access or registration) processes,
along with data governance through standardization of
procedures, processes, and data fields.
Build a sample database to support the proof of concept/
proof of technology (POC/POT), but don’t think you can
adequately test with 10,000 or 100,000 records. As the neural network discussion illustrates, using more data to test
will produce stronger results and greater efficiency.
Don’t forget about the impact to downstream systems if
auto-stewardship creates a high volume of resolved tasks.
Incorporate identity data goals into an organizational data
As the world becomes more digitized, the authors hope that
the innovations and guidance shared in this article inspire HIM
professionals to apply innovation to the challenge of achieving accurate patient identification. All components of the vast
healthcare ecosystem will benefit as a result of these efforts, including each of us as healthcare consumers. ¢
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Connecting Health and Care for the Nation: A Shared Nationwide
Interoperability Roadmap.” www.healthit.gov/sites/de-fault/files/hie-interoperability/nationwide-interoperabil-ity-roadmap-final-version- 1.0.pdf.
2. Gillick, Dan. “A Resurgence of Neural Networks in Machine Learning.” Berkeley School of Information blog post.
November 21, 2013. https://datascience.berkeley.edu/
3. Jones, Graham. “The Resurgence of Neural Networks.”
LinkedIn blog post. July 27, 2015. www.linkedin.com/
Butler, Mary. “Congress Passes the 21st Century Cures Act,
Impacting Health Record Privacy, Documentation and
Exchange.” Journal of AHIMA website. December 7, 2016.
College of Healthcare Information Management Executives.
“Chime National Patient ID Challenge.” https://herox.com/
Fernandes, Lorraine M. et al. “Losing the Match Game: Study
Reveals Gaps in HIM’s Patient Identity Integrity Practices.”
Journal of AHIMA 87, no. 10 (October 2016): 39-47. http://
Fernandes, Lorraine and Michele O’Connor. “Accurate Patient
Identification—A Global Challenge.” Perspectives in Health
Information Management. 2015. http://perspectives.ahima.
Hall, Susan. “How New York RHIO Tackles Patient Matching.”
FierceHealthcare. December 5, 2016. www.fiercehealthcare.
Le, Quoc V., Navdeep Jaitly, and Geoffrey E. Hinton. “A Simple
Way to Initialize Recurrent Networks of Rectified Linear
Units.” Cornell University Library. April 7, 2015. https://arxiv.
Office of the National Coordinator for Health IT. “Patient
Identification and Matching Final Report.” February 7, 2014.
Weber, Gerald. “Achieving a Patient Unit Record Within
Electronic Record Systems.” March 1995. http://sce2.umkc.
Weber, Gerald I. and Max G. Arellano. “Issues in Identification
and Linkage of Patient Records Across an Integrated Delivery
System.” Journal of Healthcare Information Management 12,
no. 3 (Fall 1998). http://sce2.umkc.edu/csee/leeyu/Mahi/
Lorraine Fernandes ( firstname.lastname@example.org) is principal with Fernandes
Healthcare Insights, and president-elect of the International Federation of Health Information Management Associations (IFHIMA). Jim
Burke ( email@example.com) is EMPI and HIE practice lead
at Himformatics. Michele O’Connor ( Michele.OConnor@Collibra.com)
is services manager, North America at Collibra.
Applying Innovation to the
Patient ID Challenge
Patient Identity Governance at St. Joseph Health
A ROBUST PROOF of concept project was undertaken, applying governance principles, at St. Joseph Health based in Orange County, CA, in order to improve patient data matching. Key considerations for their cost analysis included:
Traditional cost for data stewards ($21 to $25 per hour)
Volume of tasks data stewards can typically resolve ( 50 to 100 per day)
Estimated cost per task resolved by human intervention ($3.11 based upon rate of $25 per hour)
Auto-stewardship estimated costs of $0.40 to $0.75 per task
Financial savings of up to 80 percent per task can be achieved using automated tools, exclusive of management and
Beyond the readily quantifiable savings, the value of having accurate, linked data to support consumer engagement and
population health activities is immeasurable at this juncture.