4#9 - Marte Kjelvik & Jørgen Brenne - Healthcare Data Management: Towards Standardization and Integration (Nor)

MetaDAMA - Data Management in the Nordics

"Den største utfordringen, det viktigste å ta tak i, det er å standardisere på nasjonalt nivå. / The biggest challenge, the most important thing to address, is standardizing at the national level."

The healthcare industry is undergoing a significant transformation, driven by the need to modernize health registries and create a cohesive approach to data governance. At the heart of this transformation is the ambition to harness the power of data to improve decision-making, streamline processes, and enhance patient outcomes. Jørgen Brenne, as a technical project manager, and Marte Kjelvik’s team, have been instrumental in navigating the complexities of this change. Their insights shed light on the challenges and opportunities inherent in healthcare data modernization.
Here are my key takeaways:
Healthcare data and registry

• Its important to navigate different requirements from different sources of authority.
• To maintain comprehensive, secure, and well-managed data registries is a challenging task.
• We need a national standardized language to create a common understanding of health data, what services we offer within healthcare and how they align.
• Authorities need also to standardize requirements for code and systems.
• National healthcare data registry needs to be more connected to the healthcare services, to understand data availability and data needs.

Competency

• Data Governance and Data Management are the foundational needs the registry has recognized.
• Dimensional Modeling was one of the first classes, they trained their data team on, to ensure this foundational competency.
• If the technology you choose supports your methodology, your recruitment of new resources becomes easier, since you don’t need to get experts on that very methodology.

Models

• User stories are a focus point and prioritized. Data Lineage (How data changed through different systems) is not the same as Data Provenience (Where is the datas origin). You need both to understand business logic and intent of collection) - User stories can help establish that link.
• Understanding basic concepts and entities accounts for 80% of the work.
• Conceptual models ensured to not reflect technical elements.
• These models should be shareable to be a way to explain your services externally.
• Could first provides an open basis to work from that can be seen as an opportunity.
• There are many possibilities to ensure security, availability, and discoverability.
• Digitalization in Norwegian public services has brought forth a set of common components, that agencies are encouraged to use across public administration.
• Work based on experiences and exchange with others, while ensuring good documentation of processes.
• Find standardized ways of building logical models, based on Data Contracts.
• By using global business keys, you can ensure that you gain structured insight into the data that is transmitted.
• Low Code tools generate generic code, based on the model to ensure effective distribution and storage of that data in the registry.
• The logical model needs to capture the data needs of the users.
• Data Vault 2.0 as a modeling tool to process new dats sources and adhering to a logical structure.
• There is a discipline reference group established to ensure business alignment and verification of the models.
• Data should be catalogued as soon as it enters the system to capture the accompanying logic.

Data Vault

• Adaptable to change and able to coordinated different sources and methods.
• It supports change of formats without the need to change code.
• It makes parallel data processing possible at scale.
• Yet due to the heterogeneity of data vault, you need some tool to mange.