The Building Blocks of Inter-operability

Adam Culbertson; Satyender Goel; Margaret B. Madden; Niloufar Safaeinili; Kathryn L. Jackson; Thomas Carton; Russ Waitman; Mei Liu; Ashok Krishnamurthy; Lauren Hall; Nickie Cappella; Shyam Visweswaran; Michael J. Becich; Reuben Applegate; Elmer Bernstam; Russell Rothman; Michael Matheny; Gloria Lipori; Jiang Bian; William Hogan; Douglas Bell; Andrew Martin; Shaun Grannis; Jeff Klann; Rebecca Sutphen; Amy B. O’Hara; Abel Kho

doi:10.4338/ACI-2016-11-RA-0196

Applied Clinical Informatics, Inhaltsverzeichnis

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

¹HIMSS, Chicago, IL

,

Satyender Goel

²Northwestern University, Chicago, IL

,

Margaret B. Madden

²Northwestern University, Chicago, IL

,

Niloufar Safaeinili

²Northwestern University, Chicago, IL

,

Kathryn L. Jackson

²Northwestern University, Chicago, IL

,

Thomas Carton

³Lousiana Public Health Institute, New Orleans, LA

,

Russ Waitman

⁴Kansas University Medical Center, Kansas City, KS

,

Mei Liu

⁴Kansas University Medical Center, Kansas City, KS

,

Ashok Krishnamurthy

⁵University of North Carolina, Chapel Hill, NC

,

Lauren Hall

⁶BaylorScott & White Health, Dallas, TX

,

Nickie Cappella

⁷University of Pittsburgh School of Medicine, Pittsburgh, PA

,

Shyam Visweswaran

⁷University of Pittsburgh School of Medicine, Pittsburgh, PA

,

Michael J. Becich

⁷University of Pittsburgh School of Medicine, Pittsburgh, PA

,

Reuben Applegate

⁸University of Texas at Houston, Houston, TX

,

Elmer Bernstam

⁸University of Texas at Houston, Houston, TX

,

Russell Rothman

⁹Vanderbilt University, Nashville, TN

,

Michael Matheny

⁹Vanderbilt University, Nashville, TN

,

Gloria Lipori

¹⁰University of Florida, Gainesville, FL

,

Jiang Bian

¹⁰University of Florida, Gainesville, FL

,

William Hogan

¹⁰University of Florida, Gainesville, FL

,

Douglas Bell

¹¹University of California, Los Angeles, CA

,

Andrew Martin

¹²Regenstrief Institute, Indianapolis, IN

,

Shaun Grannis

¹²Regenstrief Institute, Indianapolis, IN

,

Jeff Klann

¹³Harvard Medical School, Boston, MA

,

Rebecca Sutphen

¹⁴USF Morsani College of Medicine, Tampa, FL

,

Amy B. O’Hara

¹⁵Census Bureau, Suitland, MD

,

Abel Kho

²Northwestern University, Chicago, IL

› Institutsangaben

Abstract

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

Keywords

Record linkage - master patient index - data completeness - data collection - data validation and verification - data processing

Volltext

Referenzen

References
1 Where Is HITECH’s $35 Billion Dollar Investment Going?. 2016 Available from: http://healthaffairs.orgblog/2015/03/04/where-is-hitechs-35-billion-dollar-investment-going/
2 Hsiao C-J, Jha AK, King J, Patel V, Furukawa MF, Mostashari F. Office-based physicians are responding to incentives and assistance by adopting and using electronic health records. Health Affairs. 2013: 10. 1377/hlthaff. 2013.0323.
3 The Office of the National Coordinator for Health Information Technology. Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap. October 6th 2015. Available from https://www.healthit.gov/sites/default/files/hie-interoperability/Interoperibility-Road-Map-Supplemen tal.pdf
4 Hillestad R, Bigelow J, Bower A, Girosi F, Meili R, Scoville R, Taylor R. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health affairs 2005; 24 (05) 1103-1117.
5 Hillestad R, Bigelow J, Chaudhry B, Dreyer P, Greenberg MD, Meili RC, Ridgely MS, Rothenbery J, Taylor R. Identity crisis: An examination of the costs and benefits of a unique patient identifier for the US health care system. RAND Corporation; 2008
6 Thornton SN, Hood SK. editors. Reducing duplicate patient creation using a probabilistic matching algorithm in an open-access community data sharing environment. AMIA Annual Symposium Proceedings; 2005. American Medical Informatics Association;
7 McCoy AB, Wright A, Kahn MG, Shapiro JS, Bernstam EV, Sittig DF. Matching identifiers in electronic health records: implications for duplicate records and patient safety. BMJ quality & safety 2013; 22 (03) 219-224.
8 McDonald CJ. Computerization can create safety hazards: a bar-coding near miss. Annals of Internal Medicine 2006; 144 (07) 510-516.
9 Joffe E, Bearden CF, Byrne MJ, Bernstam EV. AMIA Annual Symposium Proceedings. 2012. November 3: 1269–1275; Chicago, IL:
10 ECRI. Top Ten Patient Safety Concerns for 2016 [cited 2016 August 8th]. Available from: https://www.ecri org/Pages/Top-10-Patient-Safety-Concerns.aspx
11 Kho AN, Lemmon L, Commiskey M, Wilson SJ, McDonald CJ. Use of a regional health information exchange to detect crossover of patients with MRSA between urban hospitals. Journal of the American Medical Informatics Association 2008; 15 (02) 212-216.
12 Christen P. Data matching: concepts and techniques for record linkage, entity resolution, and duplicate detection. Springer Science & Business Media; 2012
13 HIMSS. Patient Identity Integrity Toolkit. Chicago, IL: HIMSS; December 2009. Available from: http://www.himss.org/library/healthcare-privacy-security/patient-identity
14 Dimitropoulos Linda L. Privacy and security solutions for interoperable health information exchange[Internet]. Chicago; RTI International; 2007 [cited 2016 Nov 2016]. Available from: http://media.khi.org/news/documents/2009/08/28/HISPC_Privacy_and_Security_Solutions.pdf
15 Morris G, Farnum G, Afzal S, Robinson C, Greene J, Coughlin C. Patient identification and matching final report [Internet]. Bailtimore: Audicous Inquiry; 2014 [cited 2016 Sep 12]. Available from: https://www.healthit.gov/sites/default/files/patient_identification_matching_final_report.pdf
16 Lusk KG, Neysa Noreen RH, Godwin Okafor RH, Kimberly Peterson MH, Erik Pupo MB. Patient Matching in Health Information Exchanges. Perspectives in Health Information Management AHIMA [Internet]. 2014 [cited 2016 Nov 11]]; Available from: http://perspectives.ahima.org/wp-contentuploads/2014/12/PatientMatchinginHIEs.pdf
17 The Sequoia Project. A Framework for Cross-Organizational Patient Identity Matching. The Sequoia Project; 2015 Nov. 10th. Available from http://sequoiaproject.org/wp-content/uploads/2015/11/The-Sequoia-Project-Framework-for-Patient-Identity-Management.pdf
18 Timothy D, McFarlane BED, Shaun J. Grannis. Client Registries: Identifying and Linking Patients. 1st Edition. Elsevier; Chapter 11, p. 163-182.
19 Pcornetwork. About PCORnet –PCORnet. 2016. Available from: http://www.pcornet.org
20 ReachNET 2016. Available from: http://www.reachnet.org
21 What is the Greater Plains Collaborative? Greater Plains Collaborative (GPC). 2016 Available from: http://www.gpcnetwork.org
22 CAPriCORN. The Chicago Area Patient Outcomes Research Network 2016. Available from: http://capricorncdrn.org
23 COLLABORATE. Welcome to the Mid-South Clinical Data Research Network (CDRN) 2016. Available from: https://midsouthcdrn.mc.vanderbilt.edu/collaborate
24 OneFlorida 2016. Available from: http://onefloridaconsortium.org
25 PaTH Network. 2016. Available from http://pathnetwork.org
26 Amin W, Tsui FR, Borromeo C, Chuang CH, Espino JU, Ford D, Hwang W, Kapoor W, Lehmann H, Martich GH, Morton S, Paranjape A, Shirey W, Sorensen A, Becich MJ, Hess R. Path Network Team. PaTH: towards a learning health system in the Mid-Atlantic region. Journal of the American Medical Informatics Association 2014; 21 (04) 633-636.
27 Ohno-Machado L, Agha Z, Bell DS, Dahm L, Day ME, Doctor JN, Gabriel D, Kahlon MK, Kim KK, Hogarth M, Matheny ME, Meeker D, Nebeker JR. pScanner Team. pSCANNER: patient-centered Scalable National Network for Effectiveness Research. Journal of the American Medical Informatics Association 2014; 21 (04) 621-626.
28 Rainie L, Zickuhr K. Always on Connectivity. Washington, DC: Pew Research; 2015 Aug. 26th. Available from http://www.pewinternet.org/2015/08/26/americans-views-on-mobile-etiquette
29 Busse B, Fuchs M. Prevalence of Cell Phone Sharing. Survey Methods: Insights from the Field. 2013. 2013 March 3 [Cited 2016 Nov 12th]. Available from Retrieved from: http://surveyinsights.org/?p=1019
30 Madden M. More online Americans say they’ve experienced a personal data breach. Washington, DC: Pew Research Center; 2014 April 14th. Available from: http://www.pewresearch.org/fact-tank2014/04/14/more-online-americans-say-theyve-experienced-a-personal-data-breach/
31 Smartbridge. Data Done Right: 6 Dimensions of Data Quality (Part 1). Houston, TX: Smartbridge; 2013 Aug 9th. Available from: http://smartbridge.com/data-done-right-6-dimensions-of-data-quality-part-1