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A data flow process for confidential data and its application in a health research project.
Crossfield, Samantha S R; Zucker, Kieran; Baxter, Paul; Wright, Penny; Fistein, Jon; Markham, Alex F; Birkin, Mark; Glaser, Adam W; Hall, Geoff.
  • Crossfield SSR; Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom.
  • Zucker K; Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Baxter P; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom.
  • Wright P; Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Fistein J; Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom.
  • Markham AF; Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom.
  • Birkin M; Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Glaser AW; Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom.
  • Hall G; Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom.
PLoS One ; 17(1): e0262609, 2022.
Article in English | MEDLINE | ID: covidwho-1643269
ABSTRACT

BACKGROUND:

The use of linked healthcare data in research has the potential to make major contributions to knowledge generation and service improvement. However, using healthcare data for secondary purposes raises legal and ethical concerns relating to confidentiality, privacy and data protection rights. Using a linkage and anonymisation approach that processes data lawfully and in line with ethical best practice to create an anonymous (non-personal) dataset can address these concerns, yet there is no set approach for defining all of the steps involved in such data flow end-to-end. We aimed to define such an approach with clear steps for dataset creation, and to describe its utilisation in a case study linking healthcare data.

METHODS:

We developed a data flow protocol that generates pseudonymous datasets that can be reversibly linked, or irreversibly linked to form an anonymous research dataset. It was designed and implemented by the Comprehensive Patient Records (CPR) study in Leeds, UK.

RESULTS:

We defined a clear approach that received ethico-legal approval for use in creating an anonymous research dataset. Our approach used individual-level linkage through a mechanism that is not computer-intensive and was rendered irreversible to both data providers and processors. We successfully applied it in the CPR study to hospital and general practice and community electronic health record data from two providers, along with patient reported outcomes, for 365,193 patients. The resultant anonymous research dataset is available via DATA-CAN, the Health Data Research Hub for Cancer in the UK.

CONCLUSIONS:

Through ethical, legal and academic review, we believe that we contribute a defined approach that represents a framework that exceeds current minimum standards for effective pseudonymisation and anonymisation. This paper describes our methods and provides supporting information to facilitate the use of this approach in research.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Confidentiality / Biomedical Research / Data Anonymization Type of study: Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0262609

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Confidentiality / Biomedical Research / Data Anonymization Type of study: Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0262609