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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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  1. Play ten: Sharing health data: data agreements and technologies
  2. How to choose the best method of sharing data

Step 3. Consider how technology can facilitate data sharing and access

PreviousStep 2: Decide on the type of agreement required for sharing dataNextAppendix: Choosing technology to support data sharing and access

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Alongside , technology can help curate and manage in accordance with the conditions set out in the licence or contracts. See Box 5 for an example of this.

When considering whether a new system or interface can help in a project, it is important to be driven by the needs of the users and the principles set out in the data governance framework, rather than the features of the technology product.

There are common uses of technology to facilitate secure data sharing and access:

  • To help users find data. When combined with open licences, data repositories can be used to publish key details about datasets (known as metadata) and make these discoverable through search engines.

  • To improve access to data. Access to data can be improved through application programming interfaces (APIs), which are often used as a way to access datasets. An API is a connector that can link two or more systems together. Datasets that are made available via an API have the advantages of large downloads as they can remain constantly in sync with the original dataset, and check for any updates of the data as queries are being conducted and the data is being analysed. This avoids the risk of working with an outdated version of the dataset. Also, given the large size of many health datasets, an API allows opportunities to query or filter the part of the dataset that is needed, rather than requiring all users to download the complete dataset. Access to data can also be improved through a data repository, data library or data archive where data can be stored and conditions set to control access.

  • To facilitate use of data. Data platforms provide a greater range of features for sharing and using data, including sandboxes (see below). Platforms tend to be more resource-intensive to set up, and often require some internal engineering expertise to help build the components. Data providers making use of platforms may need to devote resources to maintaining the data platform, including ensuring it remains performant on an ongoing basis.

  • To support innovation. Data sandboxes are emerging technologies that allow access to data within a secure, controlled environment, where data can be analysed or used within the sandbox but cannot be downloaded or removed from the secure environment.

We are seeing emerging models that give individuals greater control over their personal data, the ability to use data about them for their primary healthcare and to contribute altruistically to healthcare research or initiatives. These models include technological and regulatory approaches such as:

  • Platforms where individuals can .

  • Platforms where about them so that they can unlock it for individual use cases as they decide is necessary.

  • Technical architecture that , including checking for consent.

  • aimed at establishing data intermediaries that will enable ‘data altruism’, where people can share data about them on a secure platform and 'donate' it to specific projects.

The to this play includes some helpful tips on choosing technologies such as data repositories and data platforms.

Key questions to consider:

  • What are the user needs for access, use and sharing of data?

  • Is technology needed to support these needs?

  • Does something suitable already exist (for example through project partners)?

  • What budget and resource do you have, and does this include ongoing maintenance/ access?

Box 5: How technology can facilitate data sharing

GISAID is a non-profit, public-private partnership. The platform, which focuses on genome sequencing, allows variant tracking, and is used by governments to inform outbreak disease response, and by the healthcare industry to shape vaccine design and therapeutic interventions.

The platform uses standards to ensure data can be compared and aggregated, and provides a range of data visualisation and dissemination tools. One critical element to the success of the GISAID initiative is its ability to provide for a fair and transparent, as well as a verifiable and unbiased mechanism not only to govern, but to take measures and guard against bias in decision-making, preserving scientific independence.

Over 190 countries have shared genomic sequences of COVID-19 variants on the GISAID platform. To date, two million genomes have been catalogued. The , with African and South American scientific contributions to the platform more than doubling between January and April 2021.

agreements for sharing data
access
freely share data about them for use for social good
individuals can store health data
automatically determines whether data use is within regulatory requirements
Emerging regulations
Appendix
GISAID
platform is the most trusted for data sharing of COVID-19 genome data