MVP · Early Accessby ITLOX

Healthcare intelligence that
predicts, reasons, and proves itself.

MahCare predicts what is coming, reasons about why, challenges its own conclusions before they ever reach a clinician, and leaves evidence anyone can verify — across ambulatory, community, home, pharmacy-linked, and hybrid care. Monitoring is a feature. This is the category above it.

Referral01Care Plan02Visit03Medication04AI Worker05Evidence06PatientCare Graph · liveEvidence Ledger ◇ on
Causal
Reasoning, not correlation
Self-checking
AI that challenges itself
12
Governed AI workers
Europe · USA
Market-ready

What is MahCare

A healthcare intelligence platform. Not a monitoring widget or an EHR wrapper.

MahCare sits between clinical record systems and the frontline teams who deliver care, and turns the stream of events into reasoning, foresight, and action. In organisations with multiple source systems it becomes the intelligence and coordination layer. In lighter-weight settings it is the primary system of record for care operations while integrating with prescribing, diagnostics, finance, and external records.

The incumbents in digital care are monitoring companies with dashboards and risk scores. MahCare treats monitoring as one feature of a larger system — one that predicts, explains cause, checks its own work, and proves every decision. Not a workflow tool. A reasoning platform.

Care execution layer

Turns referrals, plans, alerts, and visits into managed work — prioritised by predicted risk, not just queue order. Reduces missed handoffs, overdue work, and operational chaos.

Patient engagement layer

Runs patient, proxy, and caregiver messaging across app, SMS, email, voice, and letter, personalised to each patient's state. Improves adherence and attendance.

AI workforce layer

A governed roster of AI workers for intake, triage, drafting, outreach, and coding — every consequential output challenged and risk-checked before it is shown or acted on.

Reasoning & prediction layer

Causal models, calibrated forecasts, and what-if simulation that explain why something is likely and what the next-best action is — with honest uncertainty on every number.

Evidence & compliance layer

Verifiable provenance for every write, decision, and AI output. Audit trails, DSAR packages, access reviews, and investigation packs that an external auditor can check.

Developer & marketplace layer

APIs, SDKs, event streams, configuration packs, and partner apps. Expands distribution and product stickiness without forking the core.

What makes it different

Six things monitoring platforms cannot do.

These are not add-ons bolted onto a dashboard. They are the reasoning substrate of the platform — the reason a recommendation is trustworthy enough to act on.

01

Causal reasoning

Recommendations are grounded in an estimable cause-and-effect relationship, not a correlation. When an effect cannot be established, MahCare says so instead of guessing.

02

Honest uncertainty

Every risk score arrives with a calibrated confidence band, validated against real outcomes. No bare probability is ever shown on its own.

03

Self-checking AI

Every consequential AI output is argued against by independent critics and passed through a risk gate before it is shown or executed. Weak or unsafe outputs are blocked or sent to review.

04

Care simulation

Roll the future forward before you act — "what happens if we escalate now versus wait" — and compare the chosen action against the next-best alternatives in real time.

05

Adaptive autonomy

An AI worker earns more independence as it proves itself, and loses it the moment calibration drifts or incidents rise. Autonomy is a dial that responds to trust, never a fixed setting.

06

Verifiable evidence

Every decision carries cryptographically anchored, externally verifiable provenance — the inputs, model and prompt versions, and reviewer decisions behind it. Proof, not assertion.

A governed AI workforce

Twelve AI workers.
Every one checks its own work.

Every AI worker records model, prompt version, sources, reviewer, and outcome. Every consequential output is argued against and risk-checked before it is shown. Clinically influential outputs require human approval. Medication plans are untouchable. Evidence is the default, not the exception.

01

Referral Intake Agent

Reads inbound referrals, extracts facts, flags missing items, suggests triage, creates tasks.

02

Patient Concierge Agent

Handles booking guidance, reminders, and low-risk administrative interactions.

03

Documentation Agent

Drafts notes, visit summaries, transfer summaries, and discharge packs.

04

Care Coordination Agent

Watches open pathways, overdue tasks, and breach risks. Proposes actions.

05

Medication Adherence Agent

Monitors refill gaps, missed doses, reported issues, and outreach effectiveness.

06

Prior-Auth Agent

Assembles documents, maps checklists, drafts rationale, tracks submission status.

07

Quality Auditor Agent

Reviews documentation completeness, missing evidence, policy deviations, audit gaps.

08

Coding & Revenue Agent

Suggests codes, completeness improvements, package mapping, leakage flags.

09

Inbox Triage Agent

Clusters, prioritises, and routes tasks, messages, and alerts.

10

Executive Analyst Agent

Generates weekly performance narratives, anomaly explanations, board commentary.

Hard boundary

No AI autonomously prescribes, discontinues, or silently writes to the legal clinical record. Clinically influential outputs require human review. This is a hard product safety boundary, not a toggle.

Who uses MahCare

Built for operators with coordination burden and compliance pressure.

UK · Community & home care

Community and home-care operators

High coordination burden. Medication follow-up complexity. Multi-site visibility need. Compliance pressure from CQC, DTAC, DSPT.

UK · Private care

Private clinic groups

Growth pressure. Patient communication at scale. No-show reduction. Pathway standardisation. Private billing handoff and quote management.

US · Ambulatory

Ambulatory specialty groups

Scheduling friction. Prior authorisation burden. Documentation overhead. Patient follow-up leakage. Value-based care reporting needs.

US · Value-based care

Care management organisations

Longitudinal coordination. Risk stratification with calibrated uncertainty. Outreach burden. ROI sensitivity. Strong fit for Care Graph and predictive outreach.

Pharmacy-linked

Pharmacy-linked services

Adherence monitoring. Refill coordination. Patient communications. Task routing. Evidence and audit for dispensing workflows.

Regulated innovators

Digital-first healthtech

Operators building new models that need clinical safety governance, audit infrastructure, and multi-channel patient engagement out of the box.

Measurable journeys

Eight journeys. Each with a hard outcome metric.

MahCare is judged by hard metrics. Every journey traces to source events. Every source event is on the ledger. Every metric is exportable and independently verifiable.

01

Referral to first contact

Intake, eligibility, triage, scheduling, reminders, handoff, escalation.

Reduced time-to-first-contact
02

Care-plan execution

Versioned plans, task emission, reminders, observations, reviews, closure summaries.

Higher pathway adherence
03

Medication coordination

Reconciliation, refill reminders, omission capture, pharmacy follow-up, adherence interventions.

Fewer medication-related misses
04

Observation escalation

Threshold checks, alert routing, acknowledgement, action tasks, closure evidence.

Faster time-to-action
05

Visit execution

Scheduling, mobile offline checklist, capture, sync, follow-up tasks, documents.

Lower admin time per visit
06

Patient engagement loop

Templates, channel routing, reminder sequences, reply triage, proxy handling.

Higher digital response rates
07

Prior auth & revenue prep

Evidence assembly, checklist completion, tasking, payer communication, outcome tracking.

Faster submission turnaround
08

Compliance response

DSAR, access review, incident pack, legal hold, export approval.

Hours, not days

Deployment-ready

One core. Two country packs. Zero forks.

🇬🇧

United Kingdom

Built for NHS-adjacent reality

NHS login and NHS Notify adapters. DTAC and DSPT workflow support. Clinical safety workflow support (requires customer-side Clinical Safety Officer engagement). dm+d medication terminology. GDPR and DPA 2018 operating workflows.

NHS loginNHS NotifyDTACDSPTdm+dGDPR
🇺🇸

United States

Ambulatory & value-based care

Designed to HIPAA Security Rule principles. US Core and SMART on FHIR adapter patterns. Prior-authorization workflow design. NPI provider identifier support. RxNorm and NDC medication terminology.

US CoreSMART on FHIRRxNormNDCNPI

Engineering principles

Evidence over assertion. Always.

01

Causal and evidenced by default

Recommendations are tied to an estimable cause, and every write, decision, and AI output is committed to the Evidence Ledger — independently reviewable, cryptographically anchored, exportable as verifiable evidence on demand.

02

Self-checking, human where it matters

Every consequential AI output is challenged and risk-gated before it acts. Clinically influential outputs require review. Medication plans are immutable without confirmation. Break-glass access creates visible review items. No silent mutations.

03

Configurable without forking

Tenants configure agent autonomy, review thresholds, model providers, retention, and market-specific policies through Studio. Enterprise control without engineering intervention.

Design partner programme open

See care that predicts, reasons, and proves itself.

We are onboarding design partners across Europe and the USA. Clinicians, operators, and founders welcome. Bring the hardest workflow you have — we want to see it run on MahCare.