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Data Governance Playbook
  • Health data governance: a playbook for non-technical leaders
    • Why data governance is important in healthcare
    • Who is this playbook for?
    • How to use this playbook
    • Other related resources
  • Index
  • Play one: Implementing data governance in healthcare
    • The value of data governance for data-informed healthcare projects
    • How to implement a data governance framework for a healthcare organisation or project
      • 1. Data assets
      • 2. People
      • 3. Policies and processes
      • 4. Standards and technologies
    • Resources relating to this play
  • Play two: Understanding and mapping health data ecosystems
    • Data ecosystems in healthcare
    • Data governance and trustworthy data ecosystems
    • Mapping the data ecosystem
      • Use case 1: Mapping the ecosystem of a Covid-19 symptom tracker in the UK
      • Use case 2: Identifying current stakeholders to reduce snakebite mortality and morbidity in India
    • Resources related to this play
  • Play three: Roles and responsibilities in health data governance
    • Roles involved in health data governance
      • Senior data leader
      • Health system leader
      • Policy leader
      • Health project partner
      • Governmental body
      • Senior executive leader
    • How to enlist support from stakeholders
    • Resources relating to this play
  • Play four: Making data interoperable
    • What is interoperability and how is it relevant to healthcare?
    • Standards for data and interoperability
    • Existing standards for data
    • Data adaptors
    • When to use an adaptor
    • Resources relating to this play
  • Play five: Demonstrating the value of health data governance: case studies
    • Primary care data use: MedMij platform
    • Using research data: INSIGHT Health Data Research Hub
    • Using healthcare data for other purposes: Infectious Diseases Data Observatory
  • Play six: Emerging uses of data and technology in the health sector
    • Emerging uses of health data
    • Emerging technologies to support health data management
    • Resources relating to this play
  • Play seven: Assessing the legal, regulatory and policy context for sharing health data
    • Data protection laws and policies
    • Intellectual property
    • Other regulations and laws impacting use of health data
    • Socio-cultural norms
    • Resources relating to this play
  • Play eight: Managing risks when handling personal data
    • Managing personal data responsibly and ethically in healthcare projects
    • What is personal data?
    • Data protection regulations
    • Recognising personal data in healthcare projects
    • Impacts from use of healthcare data
    • Minimising risk - practical approaches
    • Appendix: Risks from personal data exposure and how harms can be mitigated
  • Play nine: How to set up successful data sharing partnerships
    • Understanding how data sharing occurs in the health sector
    • A step-by-step guide to setting up successful data sharing partnerships
      • Step 1. Understand the purpose of sharing data, and with whom
      • Step 2. Define the principles that will guide how data is shared
      • Step 3. Build and maintain relationships with your data sharing partners
    • Appendix: International frameworks for data sharing principles
    • Resources relating to this play
  • Play ten: Sharing health data: data agreements and technologies
    • Common types of data sharing agreements
    • How to choose the best method of sharing data
      • Step 1: Decide how widely you need or want to share data
      • Step 2: Decide on the type of agreement required for sharing data
      • Step 3. Consider how technology can facilitate data sharing and access
    • Appendix: Choosing technology to support data sharing and access
    • Resources relating to this play
  • Play eleven: Cross-border data sharing
    • What is cross-border data sharing?
    • Current trends and global discussions on cross-border data sharing
    • Overcoming challenges with cross-border data sharing
  • How to support trustworthy data sharing: Checklist
  • Slides to communicate the benefits of data governance to key health stakeholders
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On this page
  • Why do we need data governance frameworks for research data?
  • Accessing, using and sharing eye health data
  • What can we learn from this case study?
  • Using data to improve health outcomes

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  1. Play five: Demonstrating the value of health data governance: case studies

Using research data: INSIGHT Health Data Research Hub

PreviousPrimary care data use: MedMij platformNextUsing healthcare data for other purposes: Infectious Diseases Data Observatory

Last updated 3 years ago

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Key findings

  • Increased participation by patient groups in how data should be used by health and research teams.

  • Improved healthcare service planning and reduced risk of long-term eye deterioration through better use of data to identify early diagnosis opportunities.

Why do we need data governance frameworks for research data?

Data use in research can help improve health outcomes. As researchers look to make use of data acquired from multiple sources, as well as make better use of their own research, there is a need to build trustworthy data ecosystems where patients are aware of how their data is used, and are partners in encouraging data use to improve health outcomes. This requires researchers and patient organisations to work together in new ways.

Accessing, using and sharing eye health data

In the UK, – a Health Data Research Hub for eye health – is involving the public, patients and other stakeholders in deciding how health data is used and shared. INSIGHT brings together and makes available anonymised data from eye scans and images, and advanced analytics, to the UK’s National Health Service (NHS), and academic and industry researchers.

INSIGHT aims to unlock new insights in disease detection, diagnosis, treatment and personalised healthcare through the following data governance processes:

  • The INSIGHT Data Trust Advisory Board – or ‘INSIGHT DataTab’ – provides meaningful oversight and scrutiny of the hub’s use and sharing of data.

  • A process was developed where the hub receives an application from a researcher to access data, and then processes the request to check that it is technically and legally feasible.

  • Applications are reviewed by the INSIGHT DataTab.

INSIGHT is learning from existing work to standardise data such as NHS Digital’s , the Health Research Authority , and the for assessing data. A Data Hub platform is provided to project partners which aligns with these data formats.

What can we learn from this case study?

This project was instrumental in ensuring participation from patient representative groups. Developing a data governance framework is vital to enabling the public, patients and other stakeholders to be involved in deciding what data is made available by INSIGHT and how this is shared and used. By involving patient representative groups in deciding how health data will be used, greater trust is built among stakeholders and the community around consenting to future health data sharing.

Using data to improve health outcomes

During the COVID-19 pandemic, there was a sharp decline in appointments for eye tests at hospitals, which can lead to late-stage diagnoses which cannot be treated in time to prevent sight loss. The use of meant there was better service planning and outreach to ensure that patients had timely access to services, reducing the risk of irreversible eyesight loss. Data has also been used to create new tools that can detect up to 50 different retinal diseases.

INSIGHT
Data Access Request Service
Confidentiality Advisory Group
Personal Information and Access Sub-Committee
data collected through INSIGHT