Appl Clin Inform 2016; 07(02): 330-340
DOI: 10.4338/ACI-2015-11-RA-0158
Research Article
Schattauer GmbH

Measuring the Degree of Unmatched Patient Records in a Health Information Exchange Using Exact Matching

John Zech
1  Icahn School of Medicine at Mount Sinai, New York, NY, USA
,
Gregg Husk
2  Lenox Hill Hospital, New York, NY, USA
,
Thomas Moore
3  Healthix, Inc., New York, NY, USA
,
Jason S. Shapiro
4  Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
› Author Affiliations
Further Information

Correspondence to:

Jason S. Shapiro
Associate Professor
Department of Emergency Medicine
Icahn School of Medicine at Mount Sinai
Box 1620
One Gustav Levy Place
New York
NY 10029
jason.shapiro@mountsinai.org

Publication History

received: 01 December 2015

accepted: 26 February 2016

Publication Date:
16 December 2017 (online)

 

Summary

Background

Health information exchange (HIE) facilitates the exchange of patient information across different healthcare organizations. To match patient records across sites, HIEs usually rely on a master patient index (MPI), a database responsible for determining which medical records at different healthcare facilities belong to the same patient. A single patient’s records may be improperly split across multiple profiles in the MPI.

Objectives

We investigated the how often two individuals shared the same first name, last name, and date of birth in the Social Security Death Master File (SSDMF), a US government database containing over 85 million individuals, to determine the feasibility of using exact matching as a split record detection tool. We demonstrated how a method based on exact record matching could be used to partially measure the degree of probable split patient records in the MPI of an HIE.

Methods

We calculated the percentage of individuals who were uniquely identified in the SSDMF using first name, last name, and date of birth. We defined a measure consisting of the average number of unique identifiers associated with a given first name, last name, and date of birth. We calculated a reference value for this measure on a subsample of SSDMF data. We compared this measure value to data from a functioning HIE.

Results

We found that it was unlikely for two individuals to share the same first name, last name, and date of birth in a large US database including over 85 million individuals. 98.81% of individuals were uniquely identified in this dataset using only these three items. We compared the value of our measure on a subsample of Social Security data (1.00089) to that of HIE data (1.1238) and found a significant difference (t-test p-value < 0.001).

Conclusions

This method may assist HIEs in detecting split patient records.


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Conflicts of Interests

The authors declare that they have no conflicts of interest in the research.


Correspondence to:

Jason S. Shapiro
Associate Professor
Department of Emergency Medicine
Icahn School of Medicine at Mount Sinai
Box 1620
One Gustav Levy Place
New York
NY 10029
jason.shapiro@mountsinai.org