Data and clinical informatics

To continue to deliver healthcare within limited resources we must mobilise the power that data gives us to target expertise efficiently, automate common processes and augment decision making with artificial intelligence. NHSGGC aspires to be a true Learning Healthcare System where the care we give is informed and improved by the data that we collect.

Challenges we face include:

  • The exponential rise in scale of data from the “–omics revolution” and citizen-data contribution
  • Maintaining confidence and trust of our population
  • Meeting ever increasing expectations from practitioners for data views and from citizens to interact with their health data
  • The move to precision, individualised medicine
  • Data needs to support the new National Care Service
  • Maintaining the confidence and trust of our population in how we manage their data

To address these, we will:

  • Build easy, high-quality structured data-capture into our clinical workflows, ensuring new developments use agreed standards-based data terminology such as SNOMED-CT
  • Ensure these data are modelled and stored in robust data warehousing and as much as possible made available via our Safe Haven Trusted Research Environment
  • Enhance our health data science and information governance capability, building on our academic partnerships
  • Give our staff access to high quality data tools and visualisations: make the data come alive and become actionable
  • Continue to monitor and improve data quality
  • Provide training and support to staff in the use of data and reports

Strategic aims

Citizens will

  • Tell us once about things that are important to them, and will see this making a difference to their care
  • Be able to share their personally collected health data with us and see meaningful views of their statutory health data
  • Trust that deterioration in their health markers will be noticed pro-actively
  • Be engaged in decisions over data use

Clinicians will

  • Be able to assimilate key information about a person they are caring for no matter the original data source
  • Appreciate computer-assisted decision making, letting the ‘computer take the strain’, with less reliance on routine review and more on management of exceptions
  • Be comfortable using modern data tools to help quality improvement

Researchers will

  • Use our safe and secure Trusted Research Environments to generate new knowledge to benefit the health of our population

NHSGGC will

  • Attract health data scientists and technology developers
  • Foster strong academic and industry partnerships contributing to the ‘quadruple helix’

Aim to be a highly performing, digitally mature global health exemplar

Infographic showing high-level logical data warehouse architecture

Data standards

We must maintain interoperability with a large installed base of historic systems.

Our data and development team will remain familiar with legacy standards such as SOAP, Scottish XML, HL7 v2.x, READ codes and ICD-10.

For newer developments we will look to use:

  • RESTful interfaces, offering greater security than SOAP
  • JSON and YAML which is quicker than XML
  • HL7 FHIR

We will drive adoption of SNOMED-CT for clinical terminology and explore storing selected data in OpenEHR format.

With one of the largest hospital prescribing systems in the UK we are heavily dependent on the dm+d dictionary.

NHSGGC will contribute to development of these data standards.

Data protection by design

As data controller we will implement appropriate technical and organisational measures to ensure that we comply with Article 25 of the UKGDPR and to ensure we process only the data that is necessary to achieve the specific purpose. This means we will integrate data protection into our processing activities and business practices, from the design stage right through the lifecycle of the work we do.

We will implement the data protection principles effectively and safeguard individual rights. A proactive approach will be taken to data protection and identifying privacy risks before they happen by carrying out appropriate Data Protection Impact Assessments and implementing technical controls and policy controls to mitigate any risks identified.

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