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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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  • Health data to improve diagnosis
  • Health data to reduce inequalities
  • Health data to encourage patient participation
  • Health data to save money
  • Health data to drive innovation

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

Emerging uses of health data

PreviousPlay six: Emerging uses of data and technology in the health sectorNextEmerging technologies to support health data management

Last updated 3 years ago

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Health data is increasingly being used to help drive better outcomes, including improving diagnosis, reducing health inequalities, increasing patient participation, saving money and resources, and driving innovation. Some real-world examples for each of these follow:

Health data to improve diagnosis

  • Big data analytics has the potential to by assisting the diagnosis of certain diseases and improving the management of chronic diseases.

  • In Boston, US, health data to increase the accuracy and speed of identification of bacteria that is known to cause bloodstream infections.

Health data to reduce inequalities

  • Health data by integrating information systems to better address health inequalities, analyse health outcomes disaggregated by specific factors (socio-economic status, gender, ethnicity and education) and potentially reduce risk factor exposure of vulnerable groups.

  • In Brazil, to increase resource allocation, bringing more doctors to remote and low-income areas. When high rates of maternal mortality were seen during the COVID-19 pandemic, some states used this data to prioritise pregnant women for vaccinations, which is now a national policy.

Health data to encourage patient participation

  • The online research platform, , allows patients with life-changing illnesses to share their experience using patient-reported outcomes. Responses to a survey from 1,323 participants (19% of the almost 7,000 members) showed that users perceived the greatest benefit as learning about their symptoms (72%) and rated the site as “moderately helpful” or “very helpful”. By enabling patients to make use of data about them, they were able to participate more actively in understanding their health and wellbeing.

  • Increasing the availability and sharing of data among stakeholders enabled greater patient participation in activities focused on reducing risks to eye health through the .

Health data to save money

  • In Minneapolis, US, has saved the health system more than $60 million over five years, while also improving clinical outcomes for patients.

  • Working with publicly available prescription data in the UK, a data tech startup was able to of up to ÂŁ200 million per year for the NHS in England.

Health data to drive innovation

Accelerating the use of data to enable artificial intelligence tools to make analyses in Ontario, Canada. These partnerships focus on developing new health technologies such as enhanced radiology, remote patient monitoring and early diagnosis of life-threatening illnesses in premature babies.

The availability of health data has enabled the development of . These new collaborations are designing innovative health solutions, such as diagnostic tools and cancer treatments.

transform healthcare
has been used in artificial intelligence systems
is being used across EU member states
data has been used
PatientsLikeMe
INSIGHT Health Data Hub
the use of health data and machine learning algorithms
identify potential savings
has fostered new industry and public research partnerships
international partnerships between public health researchers, pharmaceutical companies and Finnish health tech startups