Electronic linkage and interrogation of administrative health, social care, and criminal justice datasets: feasibility concerning process and content

TY - JOUR

T1 - Electronic linkage and interrogation of administrative health, social care, and criminal justice datasets

T2 - feasibility concerning process and content

AU - Higgins, Cassie

AU - Matthews, Keith

N1 - Funding Information: The work was supported by Scottish Government with an award to Professor Keith Matthews and Dr Brian Kidd. Additionally, we would like to acknowledge the work of Dr Mark McGilchrist, University of Dundee, in facilitating the inclusion of non-CHI datasets within the present study and also Neil Fraser in Chairing our Project Stakeholders’ Reference Group. Keith Matthews has chaired advisory boards for studies of Deep Brain Stimulation for Obsessive-Compulsive Disorder sponsored by Medtronic. He has received educational grants from Cyberonics Inc. & Schering Plough and has received research project funding from Merck Serono, Lundbeck, Reckitt Benckiser, St Jude Medical, and Indivior. He has received travel and accommodation support from Medtronic and St Jude Medical to attend scientific meetings. Publisher Copyright: © 2020 Taylor & Francis Group, LLC.

PY - 2020

Y1 - 2020

N2 - The objective was to test the feasibility of a novel model of electronic linkage and interrogation of large, sensitive, administrative datasets derived from health care, social care, and criminal justice. Participants comprised all individuals having completed suicide or drug-related death in Tayside between 2009 and 2014. Data were hosted, linked, and pseudo-anonymized by a Trusted Third Party and were interrogated via secure access to the HIC Scottish Government-certified Safe Haven. Several barriers were encountered concerning data access, with all but one issue (obtaining criminal justice data) ultimately soluble. However, each barrier led to a substantial delay in either obtaining the required approvals or in receiving the specified data extracts. Generally, data coverage was good but data quality was poor, with almost a fifth of the data fields (17%) being less than 10% complete. The feasibility of this novel approach was demonstrated. Critically, this was achieved because of the central involvement of a Trusted Third Party and the use of a Government-certified Safe Haven. Future studies using a similar model of data acquisition and analysis should consider the potential delays resulting from organizations’ lack of familiarity with their data-sharing protocols and procedures.

AB - The objective was to test the feasibility of a novel model of electronic linkage and interrogation of large, sensitive, administrative datasets derived from health care, social care, and criminal justice. Participants comprised all individuals having completed suicide or drug-related death in Tayside between 2009 and 2014. Data were hosted, linked, and pseudo-anonymized by a Trusted Third Party and were interrogated via secure access to the HIC Scottish Government-certified Safe Haven. Several barriers were encountered concerning data access, with all but one issue (obtaining criminal justice data) ultimately soluble. However, each barrier led to a substantial delay in either obtaining the required approvals or in receiving the specified data extracts. Generally, data coverage was good but data quality was poor, with almost a fifth of the data fields (17%) being less than 10% complete. The feasibility of this novel approach was demonstrated. Critically, this was achieved because of the central involvement of a Trusted Third Party and the use of a Government-certified Safe Haven. Future studies using a similar model of data acquisition and analysis should consider the potential delays resulting from organizations’ lack of familiarity with their data-sharing protocols and procedures.

KW - Health informatics

KW - data governance

KW - electronic data linkage

KW - safe haven

UR - https://www.scopus.com/pages/publications/85088505816

U2 - 10.1080/17538157.2020.1793346

DO - 10.1080/17538157.2020.1793346

M3 - Article

C2 - 32706275

SN - 1753-8157

VL - 45

SP - 444

EP - 460

JO - Informatics for Health and Social Care

JF - Informatics for Health and Social Care

IS - 4

ER -