Evidence map›Paper›PMID 36170880›Full record

ArticleApplied clinical informatics2022

Unlocking Potential within Health Systems Using Privacy-Preserving Record Linkage: Exploring Chronic Kidney Disease Outcomes through Linked Data Modelling.

David Lim, Sean Randall, Suzanne Robinson, Elizabeth Thomas, James Williamson, Aron Chakera, Kathryn Napier, Carola Schwan, Justin Manuel, Kim Betts and 2 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.0field-weighted citation impact, top 12% 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

7 citing papers in PubMed, 13 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

12 authors at 5 institutions in 1 country.

David LimCurtin School of Population Health, Curtin University, Perth, Western Australia, Australia.
Sean RandallCurtin School of Population Health, Curtin University, Perth, Western Australia, Australia.
Suzanne RobinsonCurtin School of Population Health, Curtin University, Perth, Western Australia, Australia.
Elizabeth ThomasCurtin School of Population Health, Curtin University, Perth, Western Australia, Australia.
James WilliamsonWA Department of Health, Perth, Western Australia, Australia.
Aron ChakeraMedical School, The University of Western Australia, Perth, Western Australia, Australia.
Kathryn NapierCurtin Institute for Computation, Curtin University, Perth, Western Australia, Australia.
Carola SchwanWA Country Health Service, Perth, Western Australia, Australia.
Justin ManuelWA Country Health Service, Perth, Western Australia, Australia.
Kim BettsCurtin School of Population Health, Curtin University, Perth, Western Australia, Australia.
Chris KaneWA Primary Health Alliance, Perth, Western Australia, Australia.
James BoydCurtin School of Population Health, Curtin University, Perth, Western Australia, Australia.
Curtin University · AUGovernment of Western Australia Department of Health · AUWA Country Health Service · AULa Trobe University · AUUniversity of Western Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic kidney disease (CKD) is a major global health problem that affects approximately one in 10 adults. Up to 90% of individuals with CKD go undetected until its progression to advanced stages, invariably leading to death in the absence of treatment. The project aims to fill information gaps around the burden of CKD in the Western Australian (WA) population, including incidence, prevalence, rate of progression, and economic cost to the health system.

methodsGiven the sensitivity of the information involved, the project employed a privacy preserving record linkage methodology to link data from four major pathology providers in WA to hospital records, to establish a CKD registry with continuous medical record for individuals with biochemical specification for CKD. This method uses encrypted personal identifying information in a probability-based linkage framework (Bloom filters) to help mitigate risk while maximizing linkage quality.

resultsThe project developed interoperable technology to create a transparent CKD data catalogue which is linkable to other datasets. This technology has been designed to support the aspirations of the research program to provide linked de-identified pathology, morbidity, and mortality data that can be used to derive insights to enable better CKD patient outcomes. The cohort includes over 1 million individuals with creatinine results over the period 2002 to 2021.

conclusionUsing linked data from across the care continuum, researchers are able to evaluate the effectiveness of service delivery and provide evidence for policy and program development. The CKD registry will enable an innovative review of the epidemiology of CKD in WA. Linking pathology records can identify cases of CKD that are missed in the early stages due to disaggregation of results, enabling identification of at-risk populations that represent targets for early intervention and management.

Indexed as

PrivacyRenal Insufficiency, ChronicAdultAustraliaCreatinineHumansMedical Record LinkageSemantic WebCreatinine

Identifiers

PMID36170880
PMCPMC9519263
OpenAlexW4298087642

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.