Enabling Patient Traceability Using Anonymized Personal Identifiers in Japanese Universal Health Insurance Claims Database.


Journal

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
ISSN: 2153-4063
Titre abrégé: AMIA Jt Summits Transl Sci Proc
Pays: United States
ID NLM: 101539486

Informations de publication

Date de publication:
2019
Historique:
entrez: 2 7 2019
pubmed: 2 7 2019
medline: 2 7 2019
Statut: epublish

Résumé

Anonymization of medical data helps protect patient identities. However, with conventional anonymized personal identifiers it is difficult to trace patients, which hinders longitudinal analyses in insurance claim database. Herein, we describe the development of a method to identify unique patients by using partial equivalence relationships of multiple anonymized personal identifiers. By using two conventional anonymized personal identifiers, we have developed virtual patient identifiers (vPIDs) to indicate unique patients. To verify the effectiveness of the developed identifiers, we have applied vPIDs to a six-year dataset of national-level Japanese insurance claims dataset and a prefectural-level insurance claims dataset with enrollee master data. In addition, we have applied vPIDs to practical analyses of medical expenditures and doctor consultations. vPID has enabled the continued tracing of patients throughout the six-year study period, and demonstrated the validity of our method. Therefore, the proposed method can be used to improve patient traceability in insurance claims database.

Identifiants

pubmed: 31258987
pmc: PMC6568060

Types de publication

Journal Article

Langues

eng

Pagination

345-352

Références

Am J Cardiol. 2006 Apr 17;97(8A):61C-68C
pubmed: 16581331
Respir Res. 2015 Apr 23;16:52
pubmed: 25899176
J Am Med Inform Assoc. 2015 Sep;22(5):1072-80
pubmed: 26104741
Int J Cardiol. 2016 Jul 15;215:277-82
pubmed: 27128546

Auteurs

Jumpei Sato (J)

The University of Tokyo, Tokyo.

Hiroyuki Yamada (H)

The University of Tokyo, Tokyo.

Kazuo Goda (K)

The University of Tokyo, Tokyo.

Masaru Kitsuregawa (M)

The University of Tokyo, Tokyo.

Naohiro Mitsutake (N)

Institute for Health Economics and Policy, Tokyo.

Classifications MeSH