The Challenge
The Challenge
Public health data is rarely neat. The client faced a familiar set of issues:
- Data spread across clinical and non-clinical systems, each with different formats and refresh patterns.
- A growing need for repeatable pipelines that could support both batch and streaming workloads.
- Increasing demand for consistent, trusted reporting for programme delivery, research, and stakeholders.
- The need to bring governance, security, and collaboration into the centre, not bolt it on afterwards.
The Solution
We helped the organisation adopt a Lakehouse approach on Databricks, designed around real public health and clinical use cases.
1) A Databricks Lakehouse for clinical + operational data
We established a Lakehouse pattern that supports structured and semi-structured clinical data, operational records, and programme datasets, with clear curation layers and repeatable pipeline standards.
2) Direct clinical integration into Databricks
A key step was enabling direct integration from a clinical system API into Databricks across development, staging, and production environments. This reduced reliance on brittle manual extracts and created a consistent foundation for downstream analytics.
3) Use-case delivery: from outbreaks to clinical protocol audits
Rather than treating the platform as an end in itself, we anchored it in practical, high-value scenarios, including:
- Outbreak prediction and epidemiological modelling
Building predictive workflows using environmental, historical, and clinical surveillance data to improve readiness and response
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Geospatial and programme analytics
Using spatial analytics patterns on Databricks to support planning and delivery of health campaigns.
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Clinical data transformation and audit for chronic diseases
Implementing transformations and audit checks focused on chronic disease pathways (including hypertension, diabetes and sickle cell disease), surfacing potential protocol gaps and supporting improvements in data quality and programme assurance.
4) Governance with Unity Catalog
Datasets and pipelines were designed to be discoverable, governed, and reusable. Unity Catalog provided the structure for controlled access and consistent management of shared data assets.
Tools & Technologies
This solution was designed for a modern Databricks stack:
- Databricks Lakehouse platform for unified engineering, analytics, and ML
- Unity Catalog for governed datasets and shared access patterns
- Medallion-style curation patterns for clinical transformation workloads
The Results
This work created a step change in how the organisation could use data across programmes:
- Faster, more reliable clinical and operational reporting through automated pipelines
- A scalable foundation for predictive modelling and public health insight
- Better data quality and assurance for chronic disease programme management through audit checks and curated transformations
- A governed, reusable approach to sharing curated datasets internally, with clearer control and visibility
Public health organisations do not need “more dashboards”. They need faster decisions, better data trust, and practical AI that supports clinicians and programme leads. This project shows how Databricks can be used to do exactly that, without turning the data platform into a never-ending science project.
If your health data is spread across clinical apps, surveys, spreadsheets and operational systems, we can help you unify it on Databricks and deliver real, use-case-led outcomes in weeks, not years.
