Dialyst turns a single technician's session note into the medical record, the progress report, and a clean, claim-ready PM-JAY/CMHIS submission — automatically, offline-first, and without a resident nephrologist in the room.
Technicians carry the full clinical and administrative weight, by hand, session after session.
The same session gets written into a register, then re-typed for the claim portal, then re-typed again for the monthly report — each copy a chance to lose accuracy.
Weight swings, complication patterns, and drifting lab values live in paper registers no one has time to review across months — until a crisis makes them obvious.
A missing field or mismatched value on a PM-JAY/CMHIS claim comes back weeks later as a rejection — with no system flagging the error before submission.
Official figures from Parliament, the National Health Mission, and peer-reviewed research using government-linked data show the size of the burden this problem creates.
Of the 16.5–22 lakh people who develop end-stage renal disease in India every year, only about 10% receive treatment — the rest die. That is an estimated 1.5–2 lakh preventable deaths annually, driven largely by lack of access to timely, well-managed dialysis care rather than the disease itself.
Cross-sectional study, South India — PMC / NCBI, National Library of MedicinePMNDP has done the hard infrastructure work — 1,403 centres, 9,477 machines, in 686 districts nationwide (Lok Sabha reply, July 2023). The gap Dialyst addresses sits one layer above the hardware: most of these centres run on technician-only staffing with no resident nephrologist, exactly the operating model government studies flag as a recurring implementation challenge. A patient trend that goes unnoticed for months on paper, or a claim rejected for a paperwork error, both translate directly into delayed or interrupted care — and interrupted dialysis care is the single largest driver of the mortality gap above. Digitizing session recording, automating progress tracking, and building AI-assisted review into every unit is how the last-mile care gap between "a machine exists" and "a patient survives" gets closed.
All figures above are drawn from government sources (Lok Sabha replies, National Health Mission) and peer-reviewed research citing government-linked data (Global Burden of Disease study, PMC/NCBI). Figures reflect the most recent officially published data available at time of writing and should be re-verified against current PMNDP/NHM releases before use in a formal submission.
A technician's day is fixed-length. Every minute spent re-writing a session by hand is a minute not spent watching a patient, catching a complication early, or turning the chair over for the next session.
Session data hand-written into a register, then re-typed into Excel, then re-typed again for the claims portal — the same numbers, three times
Hourly vitals and complications tracked on paper, easy to misplace or forget under pressure with multiple patients running at once
End-of-day claims prep is a separate, dedicated task — often done after hours, cutting into technicians' own time
A new or relief technician has no fast way to see a patient's history — dry weight, past complications, tolerances — before starting a session
Settings pre-filled from the patient's last session; technician confirms or adjusts instead of re-entering everything from scratch
Hourly vitals logged in seconds each; the system — not memory — tracks what's been recorded and flags gaps
The claim is already assembled and validated the moment the session is saved — no separate end-of-day task
Any technician opens a patient's full history instantly — dry weight, past complications, what settings worked — before touching the machine
A technician fills one structured session form — the same handful of fields already on the paper register. Dialyst does the rest: records, reports, claims, and AI-assisted analytics, automatically.
Pre/post weight, vitals, complications, dialysate — entered once, at the chair, in under two minutes.
A structured, dated clinical entry attached to the patient's history — no re-typing into a separate register.
Trends across sessions — dry weight drift, recurring complications — visible on one screen for the next doctor visit.
A PM-JAY/CMHIS-ready submission, pre-checked against required fields before it ever reaches the portal.
Every session feeds a growing dataset — the AI layer reads across thousands of records to flag drifting trends and write plain-language summaries for doctor review.
Dialysis-specific software exists in India and abroad. Almost all of it is built for urban private chains with admin staff, stable connectivity, and enterprise budgets.
| Capability | Dialyst | General Hospital HMIS | Private dialysis-chain software | Manual claim portal |
|---|---|---|---|---|
| Works fully offline, no internet needed | Yes | No | Rarely | No |
| Dialysis-specific session recording (settings, complications, hourly vitals) | Yes | No | Yes | No |
| PM-JAY / CMHIS claim pre-validation built in | Yes | No | No — built for US/private insurers | Only catches errors after submission |
| Designed for a unit with no resident nephrologist | Yes | No | No | No |
| Runs on one ordinary laptop, no server or IT team needed | Yes | No | No | n/a |
| AI-generated doctor summaries, on-device, free per use | Yes | No | Cloud-based, per-use cost | No |
| Enterprise pricing / IT dependency | None | Often high | High — built for chains, not single units | n/a |
Everyone touching a dialysis session sees the benefit differently — Dialyst is built around all three at once.
Digitizing dialysis session management pays for itself through recovered claims and freed staff time — not through a new fee line.
Every hospital-caused rejection — a wrong code, a missing document, a mismatched date — is a claim resubmitted late or never resubmitted at all. Pre-validation before submission recovers that revenue instead of losing it to paperwork.
Time technicians currently spend re-writing the same session three times over goes back to patient monitoring and turning over the chair for the next session — without hiring anyone new.
Runs offline on hardware the unit likely already has. No server, no IT department, no recurring cloud infrastructure bill — a real constraint removed for a resource-limited district hospital.
A visiting nephrologist covering a unit without a resident specialist can't manually review months of paper registers for every patient. Dialyst's on-device AI reads the structured data and writes what a human would take hours to compile.
Steady adequacy (URR 68–71%) through the quarter. Interdialytic weight gain has risen from ~2.1kg to ~3.4kg over the last 5 sessions. Two hypotensive episodes in the past 2 weeks, both mid-session, both resolved with saline. No missed sessions this month.
⚑ Flagged for review: rising weight-gain trendAcross thousands of records, the system surfaces the patients whose trends are drifting — not just the ones someone happened to notice.
A month of numbers becomes five readable lines, so a doctor's limited time on-site goes to the patients who actually need attention.
The AI describes and flags patterns — it never decides doses or treatment. Every flag is a prompt for a clinician's judgment, not a replacement for it.
The Ayushman Bharat Digital Mission defines four milestones for a compliant health-record system. Dialyst is being built to move through them in order — starting with patient identity, ending with claims exchange.
Every patient registration can create or link a 14-digit ABHA number — India's national health ID — during check-in.
Dialysis session records, prescriptions, and lab results structured as shareable FHIR bundles — the format the national health stack expects.
Patient-authorized sharing of records with other providers through ABDM's Consent Manager — nothing moves without explicit permission.
Direct, consent-based claims exchange with PM-JAY/CMHIS and other payers through the National Health Claims Exchange.
Dialyst starts where the paper register already lives — one form, one entry, per session — and builds outward into records, reports, analytics, and compliant claims from there. Every hour saved is patient care hours gained; every claim recovered is money the hospital already earned.