The Building Blocks of Inter-operabilityA Multisite Analysis of Patient Demographic Attributes Available for Matching Funding Mike Becich received grants by CTSI: UL1TR001857–01, and PCORI PaTH: CDRN-1306–04912.
16 November 2016
accepted: 21 January 2017
21 December 2017 (online)
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
KeywordsRecord linkage - master patient index - data completeness - data collection - data validation and verification - data processing
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.
- 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