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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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  • Data repositories to store and share data
  • Tools to support data governance
  • Tools to support standardisation
  • Tools to support data integration
  • Privacy enhancing technologies

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  1. Play six: Emerging uses of data and technology in the health sector

Emerging technologies to support health data management

PreviousEmerging uses of health dataNextResources relating to this play

Last updated 3 years ago

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Digital technologies can support a range of data management activities, including data storage, standardisation, interoperability and privacy. The five technologies described below are increasingly being used to help manage health data.

Data repositories to store and share data

Data repositories are large online data stores that enable stakeholders to upload, share and access data. They ensure data is preserved, can help track the provenance of data, and can help standardise the way data is described (known as metadata). Repositories can provide flexibility in access permissions, enabling data to be shared across the spectrum; from internal use only, to sharing with partners and specific groups, to public or open access. Examples of data repositories used in the health sector include:

  • : A catalogue of available health data repositories used for general data uploads or for specific subjects and geographies.

  • : A widely accepted data repository for all types of data including qualitative social science data, as well as biomedical data. It is recognised by PLOS One journals.

  • : Another widely accepted data repository, also commonly used by scientific journals as an accepted data repository for the data attached to published research studies.

  • : A global data research sharing platform specialising in sharing clinical trials data. This allows the results of multiple studies to be combined to create large enough datasets to perform analysis that have meaningful conclusions.

Tools to support data governance

There are an increasing number of software systems to support enterprises, health departments and others to govern data. Using methods, such as labelling and tagging to indicate important things like permissions for reuse (for example consents for personal data), standards to capture key information about each dataset to inform the way it is managed, and data dictionaries to ensure common terms, these technologies can help to implement the agreed policies and processes set out by a data governance framework. Examples of these tools include:

  • : This blog post describes some open source projects released by leading tech organisations that are sharing their internal data governance infrastructure and tooling. Select the GitHub repository link provided for each example.

  • : A non-profit organisation specialising in working with the pharmaceutical industry to manage and share research data.

  • : The European Commission's experimental tooling platform, with privacy and data governance pilot tools available.

Tools to support standardisation

and data models detail agreed ways to collect, use and share data, for example language, concepts, rules and guidance.These might be embedded in software, or described in documents. There are a number of standards for data that help ensure health data is interoperable, enabling use by a variety of stakeholders in a consistent, comparable manner. These include:

Tools to support data integration

Data can be made accessible to others via APIs. APIs enable datasets to be integrated directly into systems so that users can access data that is relevant for their purpose, rather than having to download and upload entire datasets. APIs give users access to up-to-date data, mitigating the risk that they might use an outdated version. They can also contain built-in contract conditions to manage access permissions. These controls can be set at a granular level so that different users can be given different levels of access, as determined by a unique identifier (also referred to as an API key).

Health-specific API standards include:

Privacy enhancing technologies

creates a digital object identifier (DOI) for each dataset which enables it to be shared with a consistent, globally standardised internet link.

Metadata standards help ensure datasets are described with standardised metadata descriptions. Models like and , and dataset descriptions like , enable standardised descriptions of datasets.

There are also standardised data models for how health data should be organised, including , , , , , and others.

Industry-based solutions like the seek to transform datasets into a format readable by the FHIR (Fast Healthcare Interoperability Resources) application programme interface (API) standard as they are ingested, in order to make them interoperable.

APIs can be developed as needed by individuals or organisations, but there are also a number of open API standards such as the . API standards make sure that different systems using the same API can access, modify or create data items in a way that is consistent between each system.

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(PETs) enable use of data while protecting the identity of the individuals reflected in the data. There are emerging good practices . PETs include:

Platforms that enable data to be accessed in a secure way that does not allow removal from the platform, often referred to as "trusted execution environments". For example, EUCANConnect's aims to enable analysis of research data without removal from the data storage source.

Personal data stores allow individuals to store their data in a platform and set agreements on who can access the data. This can include proprietary offerings built on blockchain technologies, like , or health authority designed platforms like in Finland.

Global Health Data Repository Finder
figshare
Zenodo
Vivli
Open source data catalogs
Transcelerate
EUCANConnect
Standards for data
Datacite
schema.org
DCAT
HCLS
DHSI2
OMOP
LOINC
PCORnet
DICOM
Google Cloud Healthcare Engine
OpenAPI specification
FHIR API standard
OpenEHR (technologies for e-health) APIs
Privacy enhancing technologies
to consider when adopting PETs
DATAShield project
meeco.me
MyKanta