HIP Insights
Turning data into insight
HIP Insights is the analytical component of the Health Intelligence Platform. It makes health data analysable beyond the individual case for key metrics and reporting, data quality, research cohorts and operational forecasts. The analysis takes place on the basis of the platform, on the leading clinical data repository, without first having to build and operate a dedicated data warehouse.
Benefits
Turning existing data into robust decision-making foundations
Administrative and health data in a single platform
A 360° view of your organisation's data
HIP Insights brings together clinical and non-clinical data. Unlike other analytics platforms, health data is captured in detail and with all the necessary contextual information. This means the data complies with the FAIR principles (Findable, Accessible, Interoperable, Reusable). As a result, analyses and prognostic models become possible not only on billing data, but on clinical documentation.
Challenge
Data fragmentation and the limits of a CDR
In most healthcare organisations, valuable data exists digitally but cannot be analysed. It is distributed across primary systems, coded inconsistently, and optimised for accessing individual cases rather than for aggregation across thousands of cases. As a result, every analysis has to start again with export, cleansing and harmonisation.
Traditionally, this is where a separate data warehouse project begins alongside the CDR build: its own data modelling, its own load processes, its own team, its own operations. Often, the economic key figures are then available, but the complex, hierarchical clinical documents do not fit into the data warehouse architecture and end up forming, once again, a technical representation that clinical users cannot understand on their own.
Solution
A unified data platform for research and care
HIP Insights builds on the platform's leading clinical data repository, rather than establishing a second data world alongside it. Cohort exploration, data quality assurance, a terminology service for semantically comparable analyses, and modern ML methods for operational forecasting all access the same repository. The module is separately licensable and works on HIP CDR or another openEHR- and FHIR-compatible platform.
How HIP Insights works
Purpose-built for analyses across large case numbers.
HIP Insights complements the CDR with an Analytics Store optimised for queries across large case numbers. The Analytics Store is used exclusively for read access and is updated automatically from the leading dataset. Views of the clinical and administrative data can be flexibly defined to make them available to third-party analytics applications via an SQL interface. This allows, for example, classic business intelligence tools to be connected for dashboards and predictive models.
A cohort explorer also enables data-protection-compliant requests for data access for clinical analyses on a "self-service" basis. Thanks to the Health Intelligence Platform's data models based on open standards, which are comprehensible to clinicians, they can interact independently with the platform's data and form cohorts based on inclusion and exclusion criteria. An integrated consent management system ensures that only data for which patients have given their consent is used for research.
Your benefits
- Self-service for requesting data access via the cohort explorer
- Use of rich metadata based on FAIR principles instead of losing clinical context
- Integrated system for administrative and clinical data: no need to build, model and operate a separate data warehouse
- Use of existing BI tools
A second data silo for analysis? Not necessary.
A copy for speed. A single source of truth for accuracy.
Analytical queries across millions of records require a different type of storage to a transactional CDR, which is designed for high-speed access to individual patient records and for write consistency. Many hospitals address this with a dedicated data warehouse: a second system, a second data model, separate operations, and a second location where patient data must reside and be governed.
HIP Insights takes a different approach. The analytics store is a deliberately chosen, controlled redundancy within the platform: read-only, automatically updated from the leading dataset, operating under the same operational and permissions model, and under the same contract. The authorisation and deletion concepts of the leading dataset also apply here, and project-specific requirements can be accommodated.
Your key features
Analytics Store
Column-oriented storage optimised for analysis across large case numbers, covering not only administrative data but also the representation of hierarchical clinical documents and data. Used exclusively for reading, updated automatically from the leading system of record.
Cohort Explorer
Define, review, refine and reproducibly document cohorts on the leading clinical dataset in self-service mode, without upfront SQL modelling.
Integration of existing BI tools
Read access to clinical and administrative data via standard SQL. Suitable for creating dashboards and reporting.
Operational forecasting
Provision of data for modern ML methods addressing operational questions, such as predicting length of stay and complications based on clinical risk factors.
Analytics partner ecosystem
Applications from the partner ecosystem offer readily deployable use cases for predictive models, observational studies, billing dashboards and more, built on the HIP.
Data protection and regulatory compliance
Clinical environments demand more than functionality – they demand compliance. HIP Insights meets both requirements:
- The Analytics Store resides within the platform, under the same operating and permissions model as the leading system of record. Authorisation and deletion concepts apply unchanged, and project-specific requirements can be accommodated.
- Read and write access is audited according to IHE ATNA. Analyses remain traceable to their data origin and thus reproducible.
- EU cloud operation or on-premises: data does not leave the infrastructure you have chosen.
- Role-based access control and purpose limitation apply to analyses just as they do to access to individual cases.
- Project-specific implementation of consent management to ensure patient wishes are taken into account.
HIP Insights: insights into the solution
A controlled, traceable and scalable route to analyses across large case numbers.
Cohort Explorer: Define a cohort in self-service using criteria from diagnoses, laboratory results, procedures and demographic data.
Cohort Explorer: Export data from an approved cohort as CSV or JSON, with time-limited access and a fully traceable record.
Current use cases
Metrics for management and reporting obligations
Based on the clinical dataset
Determine case numbers, structural metrics and quality analyses from the existing clinical dataset, instead of compiling them manually from multiple systems.Research and secondary use
Research-grade cohorts
Define cohorts for studies, registries and health services research on standardised data, with documented data provenance and reproducible results.Operational forecasting
For predictable operations
Predict operational issues, such as short-notice cancellations of procedures, to improve resource planning and utilisation.Developed for
Hospitals and hospital groups
Gain metrics and quality analyses from existing clinical data without building a dedicated data warehouse team, and evaluate a cohort definition comparably across multiple sites.
Solutions for hospitals and hospital groupsResearch and secondary use
Access research-grade cohorts on standardised data, with documented provenance, reproducible results and a connection to EHDS secondary use.
Solutions for research and secondary useHealth regions and data spaces
Standardise analyses across organisational boundaries, with clear roles and permissions for each participating organisation.
Solutions for health regions and data spaces