Patient Matching In Health Information ExchangeEskendir 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 Correlations95%+

Algorithmic Refinement, Operational Improvement85-90%

Data Cleaning, Normalization60-70%

Unconstrained Demographics10-15%

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)