Every patient. Every bed. Every margin. One intelligent hospital.
Medisys-X brings EHR, labs, imaging, pharmacy, workforce and revenue into one real-time command center. Explainable AI flags risk early, scores performance fairly and shows exactly where your margin goes.
- HL7 FHIR R4
- HL7 v2
- DICOM
- LOINC
- SNOMED CT
- ICD-10 / 11
Connects to the systems you already run
Your entire hospital, live on one screen.
Occupancy, ED flow, clinical risk, theatres, staffing and revenue stream in second by second. Every number drills down to the patient, the bed or the invoice behind it.
Patient flow · last 24 h
AI insights
LiveDepartment load
Census vs staffed capacityRisk stratification
Deterioration watchlist
NEWS2 + ML model| Bed | Patient | Score | Trend | Main signals |
|---|
Bed board · Tower A
Theatre utilisation · today
Nurse coverage vs acuity
Revenue vs cost by department · ₹ lakh MTD
Claims pipeline · this month
One patient, one record, from ambulance to home.
Follow Ravi through a cardiac emergency. At every hand-off Medisys-X carries the full picture forward, so nobody re-asks, re-orders or re-types.
Pre-arrival
10:42 · AMB-1412-lead ECG streamed from the ambulance. Cath lab pre-alerted.
ED triage
10:58 · ESI 2AI triage score and history ready before the doctor walks in.
Diagnostics
11:20 · TROP ↑Troponin, echo and CT correlated into one clinical picture.
Cath lab and ICU
12:05 · CCU-02Continuous vitals with deterioration alerts every second.
Ward
Day 2 · 4B-12Care plan, eMAR and dose checks follow the patient automatically.
Discharge
Day 4 · 10:30Summary, pharmacy and insurance claim closed before noon.
Home
Day 5+ · wearableRemote monitoring flags early warning signs after discharge.
The whole patient, not fragments of one.
Notes, labs, imaging, medications, devices and history come together in one living profile. Risk models watch quietly in the background and explain every flag they raise.
Intelligent EHR and EMR
Structured notes, problem lists, orders and a clinical timeline that reads like a story, not a database.
Risk stratification
Deterioration, sepsis, readmission, falls and length-of-stay risk scored continuously for every inpatient.
Explainable AI
Each score shows the signals that pushed it up or down, so clinicians can check the reasoning in seconds.
Ravi Kumar, 58 · M
MRN MX-204118 · Ward 4B · Bed 12
Early warning sent to the rapid response team
Why the model raised this flagcontribution to risk
- 06:10Admitted from ED with community-acquired pneumonia
- 08:45CBC, CRP and lactate ordered by Dr. Iyer
- 11:20Lactate 2.9 mmol/L, respiratory rate trending up
- 11:22AI early warning, rapid response team paged
- 11:31Acknowledged at bedside, sepsis bundle started
Performance KPIs that are fair, adjusted and useful.
Every clinician is compared with peers treating similar patients. Case mix, acuity and shift patterns are built in, so the numbers start coaching conversations instead of arguments.
Doctor KPI index
Case-mix adjusted · demoCardiology · consultant · 214 patients this quarter
Nurse rostering · next 7 days
Acuity forecast- Covered
- Tight
- Short
Results, diagnoses and drugs, connected and checked.
The LIS, RIS and PACS feed one correlation graph. Medisys-X links each result to the clinical picture and every prescription, then catches interactions before the order is signed.
Diagnostic correlation · Bed 21
Hover or tab through a node to trace it- Supports
- Dose review
- Interaction alert
Contrast and metformin
CT angiogram is booked for 14:00 and eGFR is 38. Local protocol suggests holding metformin for 48 hours after contrast.
Renal dose review
Enoxaparin ordered with eGFR 38 and weight 92 kg. Pharmacist review requested before the second dose.
Medications reconciled
Home medications checked at admission. 7 of 7 matched, 1 duplicate statin removed.
LIS integration
Analyser interfaces, sample tracking, delta checks and critical-result escalation.
RIS, PACS and DICOM
Worklists, viewer launch in context and structured reports linked to the chart.
Every device streams. Alarms never wait for the cloud.
Bedside monitors, pumps, ventilators and wearables connect to an on-premise edge gateway. Critical alerts fire locally in under a second, even if the internet drops, while the cloud runs the heavier prediction models.
Streaming now
HealthySee where every rupee is earned and lost.
Costs are traced from procurement and payroll down to each department, each procedure and each patient-day. Revenue cycle, billing and insurance claims sit in the same model, so margin is never a month-end surprise.
Operating margin by department
Bar = actual · line = targetRevenue forecast · ₹ Cr per month
Figures on this page are illustrative demo data.
Ask your hospital a question. Get an answer with sources.
Natural-language analytics over live clinical, operational and financial data. Every answer shows where it came from and respects the asker's role and permissions.
One system for the whole hospital.
Start with the modules you need and switch on the rest when you are ready. They all share one patient record, one data model and one login.
The same truth, shaped for each seat.
CEO and managing director
Occupancy, quality and margin on one board, with forecasts for the next quarter.
- Operating margin
- Occupancy
- Quality index
CFO and finance
Revenue cycle, payer mix, cost per patient-day and leakage, down to the line item.
- Days in A/R
- Denials
- Cost per case
Medical director
Case-mix adjusted outcomes, doctor KPIs, readmissions and guideline adherence.
- Mortality index
- Readmissions
- LOS vs expected
Nursing leadership
Acuity-based rostering, early warnings and workload that stays fair across wards.
- Coverage
- Overtime
- Falls
Lab and radiology heads
Turnaround times, analyser uptime, critical-result escalation and report backlog.
- TAT
- Critical calls
- Repeat rate
CISO and IT
Zero-trust access, audit trails, integrations health and uptime in one console.
- Access reviews
- Uptime
- Interface errors
Zero trust by default. Privacy by design.
Medical records are the most sensitive data a hospital holds. Every request is verified, every record is encrypted and every access is written to a tamper-evident log.
Role and context-based access
RBAC plus context rules: a nurse sees her ward, a consultant sees his patients, break-glass is logged.
Encryption everywhere
AES-256 at rest and TLS 1.3 in transit, with keys held in a managed KMS.
Immutable audit trails
Who opened which record, when and why. Searchable, exportable, append-only.
Resilient by design
Multi-zone deployment, point-in-time backups, health checks and graceful failover.
- WebHTML5 · Canvas · CSP-locked JavaScript
- APINode.js 22 · Express 5 · zod
- DataMySQL 8 · InnoDB transactions
- OpsDocker · nginx · Prometheus
Built to support HIPAA, GDPR and India's DPDP Act 2023 requirements.
From kickoff to command center in weeks, not years.
Discover
Weeks 1 to 2Map workflows, data sources and the KPIs your board actually reads.
Integrate
Weeks 3 to 8HL7 and FHIR interfaces, LIS, PACS, devices, billing and data migration.
Go live
Ward by wardRole-based training, hypercare on the floor and a short parallel run.
Optimise
OngoingModel monitoring, monthly KPI reviews and new modules as you grow.
See Medisys-X running on a hospital like yours.
A 45-minute walkthrough with a clinical and a technical specialist, tailored to your bed count, specialties and current systems.
- Live command center with your departments
- Integration plan for your HIS, LIS and PACS
- Margin and ROI model for your hospital