CC BY-NC-ND 4.0 · Appl Clin Inform 2017; 08(02): 322-336
DOI: 10.4338/ACI-2016-11-RA-0196
Research Article
Schattauer GmbH

The Building Blocks of Inter-operability

A Multisite Analysis of Patient Demographic Attributes Available for Matching
Adam Culbertson
1  HIMSS, Chicago, IL
,
Satyender Goel
2  Northwestern University, Chicago, IL
,
Margaret B. Madden
2  Northwestern University, Chicago, IL
,
Niloufar Safaeinili
2  Northwestern University, Chicago, IL
,
Kathryn L. Jackson
2  Northwestern University, Chicago, IL
,
Thomas Carton
3  Lousiana Public Health Institute, New Orleans, LA
,
Russ Waitman
4  Kansas University Medical Center, Kansas City, KS
,
Mei Liu
4  Kansas University Medical Center, Kansas City, KS
,
Ashok Krishnamurthy
5  University of North Carolina, Chapel Hill, NC
,
Lauren Hall
6  BaylorScott & White Health, Dallas, TX
,
Nickie Cappella
7  University of Pittsburgh School of Medicine, Pittsburgh, PA
,
Shyam Visweswaran
7  University of Pittsburgh School of Medicine, Pittsburgh, PA
,
Michael J. Becich
7  University of Pittsburgh School of Medicine, Pittsburgh, PA
,
Reuben Applegate
8  University of Texas at Houston, Houston, TX
,
Elmer Bernstam
8  University of Texas at Houston, Houston, TX
,
Russell Rothman
9  Vanderbilt University, Nashville, TN
,
Michael Matheny
9  Vanderbilt University, Nashville, TN
,
Gloria Lipori
10  University of Florida, Gainesville, FL
,
Jiang Bian
10  University of Florida, Gainesville, FL
,
William Hogan
10  University of Florida, Gainesville, FL
,
Douglas Bell
11  University of California, Los Angeles, CA
,
Andrew Martin
12  Regenstrief Institute, Indianapolis, IN
,
Shaun Grannis
12  Regenstrief Institute, Indianapolis, IN
,
Jeff Klann
13  Harvard Medical School, Boston, MA
,
Rebecca Sutphen
14  USF Morsani College of Medicine, Tampa, FL
,
Amy B. O’Hara
15  Census Bureau, Suitland, MD
,
Abel Kho
2  Northwestern University, Chicago, IL
› Author Affiliations
Funding Mike Becich received grants by CTSI: UL1TR001857–01, and PCORI PaTH: CDRN-1306–04912.
Further Information

Publication History

received: 16 November 2016

accepted: 21 January 2017

Publication Date:
21 December 2017 (online)

Summary

Background: Patient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching.

Objectives: We sought to determine what patient demographic attributes are collected at multiple institutions in the United States and see how their availability changes over time and across clinical sites.

Methods: We compiled a list of 36 demographic elements that stakeholders previously identified as essential patient demographic attributes that should be collected for the purpose of linking patient records. We studied a convenience sample of 9 health care systems from geographically distinct sites around the country. We identified changes in the availability of individual patient demographic attributes over time and across clinical sites.

Results: Several attributes were consistently available over the study period (2005–2014) including last name (99.96%), first name (99.95%), date of birth (98.82%), gender/sex (99.73%), postal code (94.71%), and full street address (94.65%). Other attributes changed significantly from 2005–2014: Social security number (SSN) availability declined from 83.3% to 50.44% (p<0.0001). Email address availability increased from 8.94% up to 54% availability (p<0.0001). Work phone number increased from 20.61% to 52.33% (p<0.0001).

Conclusions: Overall, first name, last name, date of birth, gender/sex and address were widely collected across institutional sites and over time. Availability of emerging attributes such as email and phone numbers are increasing while SSN use is declining. Understanding the relative availability of patient attributes can inform strategies for optimal matching in healthcare.

Citation: Culbertson A, Goel S, Madden MB, Jackson KL, Carton T, Waitman R, Liu M, Krishnamurthy A, Hall L, Cappella N, Visweswaran S, Safaeinili N, Becich MJ, Applegate R, Bernstam E, Rothman R, Matheny M, Lipori G, Bian J, Hogan W, Bell D, Martin A, Grannis S, Klann J, Sutphen R, O’Hara AB, Kho A. The building blocks of interoperability: A multisite analysis of patient demographic attributes available for matching. Appl Clin Inform 2017; 8: 322–336 https://doi.org/10.4338/ACI-2016-11-RA-0196

Clinical Relevance Statement

Patient matching is a critical barrier to achieving interoperability. The ability to matching patients is a function of the patient demographic elements available to match patients and the algorithms or methods used.


Human Subjects Protections

This study did not collect actual patient data. The work only collected statistics on the meta-data about how often a demographic field contained a value other than null or default values. Therefore the work was exempt from requiring IRB approval since no actual patient data was used.