1. Patient Matching In Health Information Exchange
Eskendir Argaw B.S and Roderick Mayberry B.S
The University of Texas at Austin Health Informatics and Health IT Professional Education Program, Spring 2016
ABSTRACT
ACKNOWLEDGMENTS
DISCUSSION AND CONCLUSION
REFERENCES
CONTACT
Eskendir Argaw Roderick Mayberry
eskendirf@gmail.com roderickmayberry@utexas.edu
INTRODUCTION
RESULTS
PURPOSE
Participants within health organizations will require health information
standards to gather and exchange data precisely. Standard Development
Organizations (SDOs) create and maintain a variety of health information
standards, all of which are intended to support interoperability.
Integrating the Healthcare Enterprise (IHE) supports interoperability
through the inclusion of key health information standards in profiles that
are developed through a collaborative process directed towards priority
health information needs.1,2
METHODS
1. Goodlove T, Ball AW. Patient matching within a health information
exchange. Perspect Health Inf Manag. 2015;12:1g.
2. Witting K. Health Information Exchange: Integrating the Healthcare
Enterprise(IHE). Introduction to Nursing Informatics. 2014; 79-96.
3. Knudson J. Identifying Patients in HIEs. For The Record. 2012;8(24):10.
4. Yeager M, Matthews M. The Framework for Cross-Organizational Patient
Identity management. 2015.
5. Meeks DW, Smith MW, Taylor L, et al. An analysis of electronic health
record-related patient safety concerns. J Am Med Inform Assoc.
2014;21(6):1053-9.
Health Information Exchange (HIE) is an electronic and integral part of
providing patient-centric, responsible, cost effective and quality
healthcare. Health information exchanges between multiple organizations
require two points of reference, organization accountability and
verification of patient’s consent to exchange health information.
Currently, these standards are maintained by Nationwide Health
Information Network (NwHIN). Standardization issues with the data
recorded result in false non-matches and even false matches, both of
which can cause major patient safety issues.1,3,4
The accuracy and usability of the shared records is crucial in patient
matching. The most challenging obstacle with the nation’s largest health
data sharing network is accurate patient matching. Health organizations
must posses the ability to consistently and accurately match patient data
in order to avoid complications for physicians and other healthcare
providers.
Absence of accurate patient matching creates a number of problems for
healthcare providers. They may experience delay in patient care, not
meeting patient safety standards, acquire additional cost and patient
dissatisfaction. Therefore, patient matching plays a significant role in
health information exchange.
The purpose of this project is to describe the patient matching problems &
solutions resulting from the nation wide network automated health
information exchange between health organizations and propose a more
effective and secure approach for patient matching for all participating
entities.
We used Google Scholar, PubMed and UT Austin library to gather and
examine articles that relate to the research. We used the Keywords:
“Health Information Exchange”, “Electronic Health Record”,
“Interoperability”, “Patient Matching”, “Deterministic Patient Matching Vs.
Probabilistic Patient Matching” . We reviewed articles from the Journal of
the American Medical Informatics Association (JAMIA) and Perspectives in
Health Information Management that were published between 2013 and
2015.
We would like to thank our mentors Mr. Bob Ligon, Dr. Richard Nauret,
and Dr. Leanne Field for all their valuable time and resources they
provided for the completion of the project. We would also like to thank
The University of Texas at Austin and The University of Texas
Southwestern Medical Center.
HIEs with strong patient matching are necessary to achieve the value and
quality needed to support our modern health system. Current matching
techniques are not efficient enough to utilize the potential benefits
available.
Key improvements in strategy include:
• Normalize fields and identify important identifiers
• Complete missing data, correct mistakes
• Implement/Refine algorithm
• Address user error, patient authorization, network, interfacing
messages and algorithm problems
• Implement and follow best practices - HIE and member organizations
In order for HIEs to continue to progress, organizations must proactively
work to prevent records from being made with insufficient data as well as
ensure past links between records can be used to improve matching in the
future.
Potential future improvements to patient matching could include:
• Increased patient involvement in correcting/completing data
• Biometrics as patient identifiers4
Steps To Increase Patient Matching Rates
Figure 2. Identifies steps used to improve matching rates
Lessons Learned
Pre-worked & Reused Correlations
95%+
Algorithmic Refinement, Operational Improvement
85-90%
Data Cleaning, Normalization
60-70%
Unconstrained Demographics
10-15%
Figure 3. Shows final patient match rate after strategic
improvements were made. (n=340,000 patients)
Patient Identifier Completeness Validity Distinctiveness Comparability Stability
Medical
Record
Number
✔ ✔ ✔ ✔
Last Name ✔ ✔ ✔
First Name ✔ ✔ ✔
Sex ✔ ✔ ✔ ✔
Date of
Birth
✔ ✔ ✔ ✔
SSN ✔ ✔ ✔
Table 1. Patient Attribute Analysis
Figure 1. Shows initial patient match rate before strategic
improvement. (n=10,000 patients)