CC BY-NC-ND 4.0 · Yearb Med Inform 2020; 29(01): 081-086
DOI: 10.1055/s-0040-1701984
Section 1: Health Information Management
Survey
Georg Thieme Verlag KG Stuttgart

Patient Identification Techniques – Approaches, Implications, and Findings

Lauren Riplinger
1  AHIMA, Washington DC, USA
,
Jordi Piera-Jiménez
2  AHIMA International, Barcelona, Spain; Open Evidence Research Group, Universitat Oberta de Catalunya, Barcelona, Spain
,
Julie Pursley Dooling
3  AHIMA, Chicago, IL, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
21 August 2020 (online)

Summary

Objectives: To identify current patient identification techniques and approaches used worldwide in today’s healthcare environment. To identify challenges associated with improper patient identification.

Methods: A literature review of relevant peer-reviewed and grey literature published from January 2015 to October 2019 was conducted to inform the paper. The focus was on: 1) patient identification techniques and 2) unintended consequences and ramifications of unresolved patient identification issues.

Results: The literature review showed six common patient identification techniques implemented worldwide ranging from unique patient identifiers, algorithmic approaches, referential matching software, biometrics, radio frequency identification device (RFID) systems, and hybrid models. The review revealed three themes associated with unresolved patient identification: 1) treatment, care delivery, and patient safety errors, 2) cost and resource considerations, and 3) data sharing and interoperability challenges.

Conclusions: Errors in patient identification have implications for patient care and safety, payment, as well as data sharing and interoperability. Different patient identification techniques ranging from unique patient identifiers and algorithms to hybrid models have been implemented worldwide. However, no current patient identification techniques have resulted in a 100% match rate. Optimizing algorithmic matching through data standardization and referential matching software should be studied further to identify opportunities to enhance patient identification techniques and approaches. Further efforts to improve patient identity management include adoption of patients’ photos at registration, naming conventions, and standardized processes for recording patients’ demographic data attributes.