📊
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
Powered by GitBook
On this page

Was this helpful?

  1. Play four: Making data interoperable

Data adaptors

PreviousExisting standards for dataNextWhen to use an adaptor

Last updated 3 years ago

Was this helpful?

A data adapter is any method, tool or process used to exchange data between two different systems. It can be very simple or very complex, but its function is to extract or read some data, and write an amended version after performing some transformation on it (sometimes known as ‘’). This might be:

  • a calculation, for example expressing an absolute value as a percentage of the total, or changing date of birth to current age

  • a conversion, for example converting inches to centimeters, date to datetime format, or easting-northing to longitude-latitude.

  • a lookup, for example assigning towns to healthcare resourced catchment areas.

Box 1 illustrates how a data adapter can be used to standardise data.

Box 1: Using a data adapter to standardise date formats

Scenario: A dataset has been created from several sources that combine test results including the date the test was administered. However, the date format used by the datasets varies from row to row. If part of your project involves plotting these test results over time, this variation will make that difficult to do.

Start by choosing an appropriate standard date format and applying it to each value in the ‘test_date’ column.

Now we have a dataset with a consistent date format, but that’s only half the job done. If we pass this data to someone else to work on, can they easily tell whether we’ve used DD/MM/YYYY or is it in fact MM/DD/YYYY? From the three rows shown here, it could be either. While this is consistent, it is not necessarily a good standard to use. A better choice is one of the ISO date formats, for example YYYY-MM-DD.

Document an explanation of the data field and the standard applied, for example: test-date: Date of self-administered nasal swab; described in the format: datetime ISO 8601.

Extract, Transform and Load
Data as supplied
Data with the date in DD/MM/YYYY format
Data with the date in ISO 8601 format