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Related Experiment Video

Updated: Jun 16, 2026

Standardized Hemorrhagic Shock Induction Guided by Cerebral Oximetry and Extended Hemodynamic Monitoring in Pigs

Standardized Hemorrhagic Shock Induction Guided by Cerebral Oximetry and Extended Hemodynamic Monitoring in Pigs

Published on: May 21, 2019

Generalisable Overview of Study Risk for Lead Investigators Needing Guidance (GOSLING): A data governance risk tool.

Anmol Arora¹, Adam Loveday², Sarah Burge³

¹School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.

|August 20, 2024

Summary

The Generalisable Overview of Study Risk for Lead Investigators Needing Guidance (GOSLING) tool offers the first quantitative measure for assessing health data research risks. This standardized approach aids ethical data use and streamlines governance review.

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Last Updated: Jun 16, 2026

Standardized Hemorrhagic Shock Induction Guided by Cerebral Oximetry and Extended Hemodynamic Monitoring in Pigs

Standardized Hemorrhagic Shock Induction Guided by Cerebral Oximetry and Extended Hemodynamic Monitoring in Pigs

Published on: May 21, 2019

Area of Science:

Health Informatics
Clinical Research Governance
Data Management

Background:

Digitized patient records and the ethical use of routinely collected data for research necessitate robust data governance frameworks.
Existing methods for assessing data-related risks in clinical research are often qualitative and lack standardization.

Purpose of the Study:

To introduce the Generalisable Overview of Study Risk for Lead Investigators Needing Guidance (GOSLING), the first quantitative risk-measurement tool for data-related risks in clinical research.
To provide researchers with a standardized self-assessment tool to evaluate and mitigate risks associated with health data research projects.

Main Methods:

GOSLING utilizes a self-assessment questionnaire covering data type, security, and public involvement to categorize projects into low, medium, or high-risk tiers.
A scoring system, developed with patient and public input, underpins the risk categorization.
The tool was validated using real and synthesized project proposals to confirm its efficacy in triaging health data access requests.

Main Results:

The GOSLING tool successfully differentiated between fifteen low, medium, and high-risk projects during validation, aligning with expert assessments.
An interactive, open-access interface encourages proactive risk evaluation and mitigation by researchers before formal data governance review.
Initial testing indicated that GOSLING can potentially expedite the review process by identifying projects requiring less scrutiny or those with significant risks.

Conclusions:

GOSLING establishes a novel quantitative approach to study risk assessment, addressing the need for standardized methods in health data research.
Implementing GOSLING can advance ethical data utilization, improve research transparency, and foster public confidence.
Future research will focus on broadening GOSLING's application and evaluating its impact on research efficiency and data governance.