From records to a clear question
Understand the data before interpreting the result.
Medical datasets can combine measurements, images and records collected in different systems. We help organise those sources, identify missing information and define how a result will be calculated and reviewed. The work begins with your question, the available data and the people who understand its context.
Describe the data types and intended use first. Agree a suitable way to share samples before sending any records.
Medical data services
Prepare, analyse and explain.
A project can focus on one dataset, an imaging workflow or the reporting tools that connect several sources.
Talk through your requirementsHealthcare data preparation
Bring agreed files, exports and database records into a consistent structure. Check formats, units, duplicate records and missing values, keeping the preparation steps visible for review.
- Source and field mapping
- Cleaning and quality checks
- Documented transformations
Statistical analysis
Explore distributions, trends and differences using methods suited to the question and dataset. Document assumptions and limitations so the team can understand what the findings support.
- Exploratory analysis
- Group and trend comparisons
- Methods and limitations
Medical imaging workflows
Develop interfaces for organising, viewing and working with agreed imaging data. Define how images, measurements and annotations relate, and where specialist review belongs in the workflow.
- Image viewing interfaces
- Measurement and annotation workflows
- Links to associated records
Research reporting
Turn the agreed analysis into tables, visualisations and reports that retain the context of the source data. Make definitions, selection rules and version information available alongside the results.
- Research tables and figures
- Reports and dashboards
- Traceable analysis outputs
Imaging tools
A clear workspace for reviewing images.
This ultrasound viewer example illustrates how images and controls can share a focused workspace. A project defines the supported data, measurements and review steps around its intended users.
- Agree supported images and associated information.
- Keep viewing controls and review actions clear.
Scan visualisation
Explore a view without losing its context.
This compact scan example introduces a possible visualisation component. Its role, source information and interaction would be defined within the wider imaging workflow.
- Retain the link to source information.
- Define what a user should be able to inspect.
Research reporting
Keep the method beside the finding.
Reports are more useful when readers can trace how they were prepared. We structure research outputs around agreed variables, calculations and review questions, with assumptions and limitations made clear.
- Document selection and calculation rules.
- Present tables and figures with analytical context.
Example: an exploratory dataset review
Make the comparison understandable.
A research team might need to compare measurements across groups or time periods. A workflow could first check identifiers and units, record the selection rules, and then present the agreed comparisons alongside missing-data summaries. Subject specialists would review the context and interpretation of the findings.
- Define which records belong in each comparison.
- Keep missing values and exclusions visible.
- Record the methods used to produce each output.
- DefineAgree the question, dataset and intended use.
- PrepareCheck records, formats and selection rules.
- AnalyseApply the agreed methods and inspect the outputs.
- ReviewPresent findings with assumptions and limitations.
Illustrative research workflow. Available data, permissions, methods and review responsibilities determine the scope of a real project.
Working with your data
Build a clear path from source to output.
Data owners, researchers and subject specialists help define the requirements and review the work.
Map the question and sources
Define the intended use, available records and access arrangements. Review formats, relevant variables and the evidence needed to assess an initial result.
What you leave withData scope and analysis planPrepare and check the analysis
Develop the transformations, calculations or viewing workflow. Reconcile samples against their sources and review unexpected results with the people who know the data.
What you leave withReviewed preparation and analysis workflowDeliver outputs with context
Prepare the agreed reports or application views. Document methods, known limitations and the steps needed to reproduce or update the work.
What you leave withReporting outputs and handover notes
Can you start with spreadsheets or existing exports?
Yes. We first review the available formats, field definitions and access permissions. A small agreed sample can help establish what needs cleaning, linking or clarifying before analysis begins.
How do you define the intended use of an imaging tool?
We agree whether the workflow supports exploratory analysis, research or another defined task, and identify who reviews its outputs. Any intended clinical use brings additional domain, evaluation and regulatory requirements that must be assessed within that project.
How should we share sample data?
Start with a description of the data and the question you want to answer. We can then agree an appropriate transfer method, access controls and whether synthetic or de-identified examples can support the initial review.
Your next step
What should your medical data help you understand?
Tell us about the data types, the question and the outputs your team needs. We can help define a practical starting scope.