AI-powered platform
AI Solutions for Connected Healthcare
Intelligence built into the pipes, not bolted on top. AI runs where data already flows — at intake, matching, coding and adjudication — so every hospital and payer on the exchange benefits without building its own models.
Nine AI services
Clinical, operational and population intelligence
Every suggestion is explainable, and every clinical decision stays with a human.
Patient matching
Learns spelling, transliteration and nickname variation across Indian names to cut duplicate records.
Clinical NLP
Turns discharge summaries, scanned prescriptions and free-text notes into coded FHIR conditions, medications and allergies.
Auto-coding
Suggests ICD-10, SNOMED CT and procedure codes with evidence highlighted for the coder to confirm.
Denial prediction
Scores claims before submission for likely denial reasons — missing auth, code mismatch, eligibility gaps.
Fraud & anomaly detection
Flags unusual billing patterns, duplicate claims and upcoding across the whole network, not one payer’s slice.
Interface mapping assistant
Proposes field mappings for new HL7, CSV or proprietary feeds, shrinking facility onboarding from weeks to days.
Longitudinal summary
Generates a one-screen patient summary across facilities for the treating clinician, citing source documents.
Data-quality scoring
Scores each source on completeness, timeliness and code validity, and shows facilities exactly what to fix.
Population insights
Disease burden, readmission and utilisation analytics for programme managers on de-identified data.
From note to data
What clinical NLP produces
A discharge summary goes in; coded, reviewable FHIR data comes out — with each code linked to the sentence it came from.
Input · discharge summary
Pt admitted with fever and productive cough x5 days. CXR: right lower lobe consolidation. Dx: community-acquired pneumonia. Known T2DM on metformin 500 mg BD. Allergic to penicillin (rash).
Output · for coder review
| Resource | Code | Confidence |
|---|---|---|
| Condition | Community-acquired pneumonia · SNOMED 385093006 | 0.97 |
| Condition | Type 2 diabetes mellitus · SNOMED 44054006 | 0.95 |
| Medication | Metformin 500 mg, twice daily | 0.98 |
| Allergy | Penicillin · reaction: rash | 0.91 |
Illustrative example.
FAQ · AI solutions
How the intelligence works and is governed.
5 common questions.
Does the AI make clinical decisions?
No. AI suggests matches, codes, summaries and risk scores; clinicians, coders and data stewards review and decide.
What data are the models trained on?
De-identified or federated data only, under the data-governance rules agreed with the programme owner. Identifiable patient data is not used to train shared models.
Can users see why the AI made a suggestion?
Yes. Each output carries its supporting evidence — the highlighted text span for a code, the field-level weights for a patient match, or the contributing factors for a denial-risk score.
How is model quality monitored over time?
Each model has a model card and is monitored for accuracy, drift and bias across groups, with scheduled reviews and a rollback path.
Can we switch individual AI features off?
Yes. Each AI service is enabled per tenant and per workflow, so programmes can adopt them gradually.
Put AI to work on your data
We’ll help you prioritise the AI use cases with the fastest, safest payback.