Platform
One system from fragmented records to a reviewable clinical picture
The Medical Intelligence Agent (MIA) reconstructs the available longitudinal record, uses PULSE as its prediction engine, connects findings to medical evidence, and prepares a structured view for physician review.
How it works
One continuous system
Clinical data moves through the five connected parts of MIA, from patient history to physician review.
- 00
Clinical data
Laboratory results, diagnoses, medications, encounters, and notes from the connected sources.
- 01
Patient timeline
The available longitudinal record is normalised into one ordered trajectory.
- 02
PULSE prediction engine
Change is modelled and biomarkers are projected with uncertainty intervals.
- 03
Clinical orchestration
Specialized medical reasoning and retrieval are coordinated across the case.
- 04
Evidence graph
Findings are linked to supporting observations and medical evidence.
- 05
Physician review loop
Output reaches the physician as a structured, reviewable trajectory.
Walkthrough
One patient, end to end
The same illustrative case as it moves through MIA, from the available record to a reviewed finding.
01
Input
What is available in the connected record for one illustrative patient across eight years.
- Laboratory results
- HbA1c, eGFR, LDL-C, ACR
- Diagnoses
- Type 2 diabetes 2019 · hypertension 2017
- Medications
- Metformin · ramipril · atorvastatin
- Encounters
- 24 across 2018–2026
- Notes
- Annual review and referral correspondence
- Missing-data intervals
- Aug 2022 – Jul 2023, no laboratory data
02
Reconstructed trajectory
The record is resolved into one ordered trajectory and measured against this patient's own baseline.
- Patient-specific baseline
- HbA1c 6.2% · eGFR 92 (2018)
- Longitudinal change
- HbA1c +1.2 points · eGFR −16 over 8 years
- Change point
- Slope steepens after the 2022–2023 data gap
- Data completeness
- Laboratory 92% · notes 71%
03
Forecast
Recurring biomarkers are projected forward with uncertainty intervals.
- Projected direction
- HbA1c rising · eGFR declining
- Quantified uncertainty
- HbA1c ±0.4 points · eGFR ±5
- Contributing observations
- Listed per biomarker with dates
- Horizon
- Set per biomarker from observed history
04
Clinical reasoning
Specialized medical reasoning is coordinated across the case and the output is prioritized.
- Prioritized finding
- Declining renal function alongside rising HbA1c
- Second finding
- Lipid panel not repeated after a dose change
- Missing information
- Retinal screening absent from connected sources
- Specialist reasoning
- Diabetology and nephrology context applied
05
Evidence
Each finding carries what it was derived from and what supports it.
- Guideline
- Diabetic kidney disease monitoring interval
- Publication
- Supplied with the finding, with source link
- Record observations
- eGFR series 2018–2026, ACR 2024
- Provenance
- Traceable from finding back to record entry
06
Review
The physician decides, and the decision is retained in structured form.
- Confirm
- Finding accepted as clinically relevant
- Edit
- Wording or emphasis corrected by the clinician
- Dismiss
- Finding rejected, with reason recorded
- Add context
- Information not present in the connected record
- Follow-up
- Action and interval recorded per finding
Illustrative patient data
Architecture
Five connected parts of MIA, built around clinical time
MIA combines longitudinal patient representation, the PULSE prediction engine, medical reasoning, evidence infrastructure, and physician review.
Patient timelineLongitudinal record reconstruction
Reconstructs the available clinical history across laboratory results, diagnoses, medications, encounters, and notes into one ordered patient trajectory.
PULSE prediction engineTemporal modeling and forecasting
Models temporal change and forecasts biomarker development with quantified uncertainty, measured against the patient's own history.
Clinical orchestrationTrained clinical orchestration
An internally trained orchestration model coordinates specialized medical reasoning, predictive models, evidence retrieval, and structured output across the patient context.
Evidence graphEvidence and provenance
Connects findings to supporting clinical observations, medical literature, guidelines, and structured medical knowledge.
Physician review loopPhysician review
Captures physician review, corrections, decisions, follow-up actions, and structured feedback.
Output reaching a clinician is physician-reviewed output: findings include traceable supporting observations and relevant medical evidence.
Interface
Designed for clinical review
Patient context, the reconstructed trajectory with its forecast, and the inspector where a finding is confirmed, corrected, or dismissed.
MIA · patient trajectory
Interactive · Case 04-118
Select a view, then trace the prioritised finding to its source or physician-review step.
HbA1c %
observed 2018–2026 · projected to 2028
- 2019DiagnosisType 2 diabetes
- 2024DiagnosisMicroalbuminuria
- 2025MedicationRamipril increased
- 2022–23Gap11 months without laboratory data
HbA1c %
observed 2018–2026 · projected to 2028
- 2019DiagnosisType 2 diabetes
- 2024DiagnosisMicroalbuminuria
- 2025MedicationRamipril increased
- 2022–23Gap11 months without laboratory data
Illustrative patient data
Integration
How MIA fits into care
- Data exchange
- FHIR-compatible clinical data exchange and structured ingestion for laboratory and record exports.
- Deployment
- Deployment is configured around the organization's clinical, technical, and data-governance requirements.
- Workflow
- MIA is designed to run alongside existing clinical systems and deliver output into an agreed physician-review workflow.
Pilot path
From use case to controlled pilot
- 01
Define the clinical use case
Agree the clinical question, the patient population, and the review step where output is used.
- 02
Connect historical data
Establish the agreed data exchange and validate ingestion against real record structures.
- 03
Run retrospective analysis
Apply MIA to historical cases from the organization's own record.
- 04
Conduct physician review
Clinicians review the output against the cases they already know.
- 05
Launch a controlled pilot
Operate in a defined clinical workflow with agreed success criteria.
Walk through MIA with your clinical team
MIA is designed to support qualified healthcare professionals and does not replace clinical judgment.