Evidence map›Paper›PMID 31706230›Full record

ArticleInternational journal of medical informatics2020

Feasibility and acceptability of an iris biometric system for unique patient identification in routine HIV services in Kenya.

Njoroge Anne, Matthew D Dunbar, Felix Abuna, Peter Simpson, Paul Macharia, Bourke Betz, Peter Cherutich, David Bukusi, Farquhar Carey

Open access · greenAbstract read
In one paragraph

Article in International journal of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 2 countries.

Njoroge AnneUniversity of Washington, Department of Global Health, Seattle, United States; Kenyatta National Hospital, Research & Programs, Nairobi, Kenya. Electronic address: anjoroge@uw.edu.
Matthew D DunbarUniversity of Washington, Centre for Demography and Ecology, Seattle, United States. Electronic address: mddunbar@uw.edu.
Felix AbunaKenyatta National Hospital, Research & Programs, Nairobi, Kenya. Electronic address: fabuna@yahoo.com.
Peter SimpsoniRespond ®, Seattle, United States. Electronic address: petersimpson@irespond.org.
Paul MachariaNational AIDS & STI Control Program, MOH, Nairobi, Kenya. Electronic address: paulmachariah@gmail.com.
Bourke BetzUniversity of Washington, Department of Global Health, Seattle, United States. Electronic address: bbetz@uw.edu.
Peter CherutichMinistry of Health, Nairobi, Kenya. Electronic address: pcheru@uw.edu.
David BukusiKenyatta National Hospital, VCT and HIV Prevention/ Youth Centre, Nairobi, Kenya. Electronic address: davidbukusi@gmail.com.
Farquhar CareyUniversity of Washington, Department of Global Health, Seattle, United States; University of Washington, Department of Medicine, Seattle, United States; University of Washington, Department of Epidemiology, Seattle, United States. Electronic address: cfarq@uw.edu.
Kenyatta National Hospital · KESeattle University · USUniversity of Washington · USMinistry of Health · KENational Aids Control Council · KE

Funding

Women and HIV: Translation of Research into Practice-WLHIV Post-Graduate Research FellowshipD43TW009783 · FIC · UNIVERSITY OF WASHINGTON · PI Elizabeth Anne BUKUSI, CAREY FARQUHAR · 2015 to 2026
$4.1M
Assisted Partner Notification to Augment HIV Treatment and Prevention in KenyaR01AI099974 · NIAID · UNIVERSITY OF WASHINGTON · PI FARQUHAR, CAREY · 2012 to 2014
$2.6M
FIC NIH HHS D43 TW009783NIAID NIH HHS R01 AI099974
6 · The paper itself

Abstract

backgroundUse of routine HIV programme data for surveillance is often limited due to inaccuracies associated with patient misclassification which can be addressed by unique patient identification.We assessed the feasibility and acceptability of integrating an iris recognition biometric identification system into routine HIV care services at 4 sites in Kenya.

methodsPatients who had recently tested HIV-positive or were engaged in care were enrolled. Images of the iris were captured using a dual-iris camera connected to a laptop. A prototype iris biometric identification system networked across the sites, analysed the iris patterns; created a template from those patterns; and generated a 12-digit ID number based on the template. During subsequent visits, the patients' irises were re-scanned, and the pattern was matched to stored templates to retrieve the ID number.

resultsOver 55 weeks 8,614 (98%) of 8,794 new patients were assigned a unique ID on their first visit. Among 6,078 return visits, the system correctly re-identified patients' IDs 5,234 times (86%). The false match rate (a new patient given the ID of another patient) was 0·5% while the generalized false reject rate (re-scans assigned a new ID) was 4·7%. Overall, 9 (0·1%) agreed to enrol but declined to have an iris scan. The most common reasons cited for declining an iris scan were concerns about privacy and confidentiality.

conclusionImplementation of an iris recognition system in routine health information systems is feasible and highly acceptable as part of routine care in Kenya. Scale-up could improve unique patient identification and tracking, enhancing disease surveillance activities.

Indexed as

Biometric IdentificationHIV InfectionsRecordsAdultFeasibility StudiesFemaleHealth Information SystemsHumansIrisKenyaMaleMiddle AgedBiometricsHIVImplementation scienceIrisKenyaPatient identification

Identifiers

PMID31706230
PMCPMC6939164
OpenAlexW2980530640

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.