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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 four: Making data interoperable

What is interoperability and how is it relevant to healthcare?

PreviousPlay four: Making data interoperableNextStandards for data and interoperability

Last updated 3 years ago

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Health datasets can be incredibly complex and are often fragmented. There are often multiple data assets in a tracking the process of delivering healthcare or a specfic use case, and these data assets are often stored across multiple systems.

  • Dataset complexity: When analysing health outcomes it is often necessary to look at a wide range of data assets including electronic health records of individual patients, population health data, health system usage and performance, clinical trials data and increasingly data collected from wearable technologies. Each of these datasets may describe common characteristics like dates and locations using slightly different formats, but those small differences increase complexity, making it harder to combine or compare one dataset with another.

  • Dataset fragmentation: Multiple datasets, held in multiple systems, are often used to track one process of healthcare delivery or a health intervention journey for a patient. If each of these datasets records key elements differently, for example dates, locations and names of patients, these elements must be standardised between the datasets first, in order for the data to be useful for analysis.

To help reduce complexity and fragmentation, and to enable greater reuse and sharing of data, it is important to ensure that data is interoperable.

Path.org's defines interoperability as:

‘(...) the ability of different applications to access, exchange, integrate, and use data in a coordinated manner through the use of shared application interfaces and standards, within and across organizational, regional, and national boundaries, to provide timely and seamless portability of information and optimize health outcomes.’

In digital health systems, interoperability is essential for ensuring complete oversight of care, for example, to track outcomes post-surgery or to inform chronic disease management strategies.

Interoperability enables innovative use of data and information, and facilitates sharing across different digital platforms. This allows data collected in one system to be accessed through different systems; reducing the time and cost of data entry, increasing the availability of data and helping to assure data quality. This creates new opportunities for analytics, visualization and data-informed decisions.

There are two tools that can be used to help build interoperability of health data systems:

  1. Standards

  2. Adaptors

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