Clinical operations, patient flow and capacity analytics for hospitals and healthcare providers — integrated directly with HL7 and FHIR sources rather than manual extracts.
Clinical data sits behind HL7 interfaces that reporting tools cannot read directly. Bed state is tracked on a whiteboard. Discharge delays are discussed anecdotally because nobody has the numbers. Duplicate and inconsistent records quietly corrupt every downstream report.
We build HL7 and FHIR integration into a governed clinical data layer, apply data quality rules that catch duplicates and missing values automatically, then deliver the operational dashboards clinical and administrative leadership need.
Bed state and patient flow visible in real time. Discharge bottlenecks identified by ward and cause. Clinical analytics built on records that have been validated rather than assumed correct.
What We Build
Live occupancy, admissions, discharges and transfers by ward, with projected capacity.
Direct interface integration into a governed clinical data store — no manual exports.
Automated duplicate detection, missing-value handling and format normalisation.
Scheduling efficiency, list utilisation and turnaround analytics.
LOS by specialty, consultant and pathway, with outlier identification.
Board and departmental reporting automated on a single validated source.
Proven Results
Proof
Technical Stack
Context
Healthcare analytics carries a constraint no other sector has to the same degree: the data is clinical, the interfaces are standardised in theory and inconsistent in practice, and the cost of a wrong number is not a bad quarter. HL7 and FHIR solved interoperability on paper, but the reporting layer on top is frequently still a nightly export into a spreadsheet.
Data quality is the recurring failure. Duplicate patient records, inconsistent date formats, missing discharge timestamps and free-text fields that should have been coded quietly corrupt every downstream metric. Length of stay calculated over records with missing discharge times is not slightly wrong, it is wrong in a direction that flatters the number.
So we build the quality framework before the dashboard. Automated duplicate detection, missing-value handling and format normalisation run in the pipeline, and the exceptions surface rather than being silently dropped. Only then is a clinical dashboard worth putting in front of a board.
How It Runs
FAQ
Yes. We integrate at the interface level rather than relying on nightly exports, which is what makes live bed state and patient flow possible. We have worked with both HL7 v2 messaging and FHIR resources.
Data stays within your infrastructure and governance boundary. We work inside your environment, apply role-based access, and design so that identifiable data is only present where it is clinically necessary. We do not extract patient data to our own systems.
That is the normal starting condition and it is the first thing we address. We profile the data, quantify the problems, and build automated rules that resolve what can be resolved and flag what cannot. A dashboard built on unvalidated clinical data is worse than no dashboard.
We integrate with the interfaces your EHR exposes. The EHR remains the clinical system of record; we build the analytical layer that sits alongside it and combines it with administrative and operational sources.