Data & Analytics Principles

Approved

These principles have been approved by the DHCW TDA

Data is captured once and reused

Data is captured once and reused refers to the practice of collecting patient information (such as medical history, test results, and treatment plans) during an initial encounter and then using that data throughout the patient’s care journey. This approach aims to improve efficiency, reduce duplication, and enhance continuity of care.

Data is semantically interoperable

Data is not only syntactically consistent (i.e. formatted correctly) but also semantically consistent. Different systems can understand and interpret the data in the same way because the data carries the same meaning across systems. For example, a diagnosis code or a medication name will be interpreted correctly regardless of the system accessing it.

Data for analytical use is not an after thought

Secondary uses of data is integral to the design of any new applications. Publishing data to analytical stores must be included prior to deployment.

Data is secured

Data is compliant with security, regulatory and privacy requirements. Appropriate measures have been implemented to protect it from unauthorised access, disclosure, alteration, or destruction. Data security involves safeguarding sensitive information and ensuring its confidentiality, integrity, and availability.

This can be achieved through various technical, organisational, and procedural controls, such as encryption, access controls, authentication mechanisms, data backups, and security monitoring. Effective data security practices aim to mitigate risks associated with data breaches, cyber attacks, insider threats, and other security vulnerabilities, thereby safeguarding the privacy, trust, and reputation of individuals and organisations.

Data is findable, accessible and well described

Data should be easy to find for both humans and computers. These data should be well described through comprehensive metadata and should include metrics relating to data quality and coverage. Data should be easily discoverable, available for authorised users, and accompanied by detailed documentation that enhances its understanding and usability.

Data is high quality

Data quality is monitored and reported. High-quality data serves as a reliable foundation for analysis, decision-making, and insights generation. It instils confidence in the results derived from data-driven processes and provides the opportunity to derive maximum value from data assets.