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Applied research 06 · Healthcare

Give time back to care. Keep judgment with clinicians.

Healthcare AI earns trust when it makes the record clearer, the care gap earlier, and the administrative path shorter—while showing its evidence and knowing when only a clinician can decide.

Decision domainClinical workflow, population health, revenue cycle, and diagnostics
Evidence surfaceEHR, notes, imaging/lab, guidelines, claims, coverage, and patient context
Control boundaryDocument and surface; clinicians and authorized reviewers decide

Our point of view

The record should support the encounter, not compete with it.

A fluent clinical answer can still be unsafe if it confuses a historical diagnosis with an active one, misses negation, ignores a contraindication, or cites guidance that does not apply to this patient. We model temporal clinical facts, terminology, provenance, and care-team authority before adding generation.

Speech and language models capture; clinical ontologies normalize; rules and retrieval check context; clinicians validate; outcome monitoring closes the loop.

Design principleThe system may reduce the distance between evidence and attention. It may not blur the distance between assistance and clinical judgment.

These are applied research patterns, not medical advice or descriptions of completed customer engagements. Public provider and regulator results below are not Zustis outcomes.

Applied research outputs

Three workflows where clarity changes capacity.

Every pattern includes clinical or administrative sign-off, privacy controls, and a defined response when evidence is incomplete.

01

Clinical documentation & coding

Draft the record from the encounter, then make verification easy.

This research pattern structures multilingual conversation, chart context, orders, and results into a draft note and coding cues that preserve uncertainty, attribution, and clinician edits.

The decision

Does the note accurately represent the encounter, and what requires clarification before signature or coding?

The evidence

Patient consent, encounter audio, chart, medications, problems, allergies, orders, results, templates, terminology, and coding guidance.

System behavior

Separates speakers, captures clinical concepts and negation, reconciles chart context, drafts the note, flags unsupported content, and learns from accepted edits.

Human boundary

The clinician reviews and signs; certified coding/revenue roles validate codes. The system cannot create a diagnosis not supported in the record.

02

Population health & care gaps

Find the patient who needs attention—and explain why now.

The reference design connects longitudinal records, guidelines, risk trajectories, access barriers, and prior outreach in a ranked, clinician-reviewable care-gap queue.

The decision

Which cohort or patient should be reviewed for outreach, test, medication review, appointment, or escalation?

The evidence

Diagnoses, labs, vitals, medications, encounters, referrals, claims, guidelines, social/access factors, device data, and outreach history.

System behavior

Builds temporal features, applies eligibility rules, estimates risk, explains drivers and exclusions, and routes the case to the appropriate care team.

Human boundary

Clinicians determine diagnosis and care. High-risk symptoms, uncertainty, and guideline conflict escalate; no autonomous clinical message is sent.

03

Claims & prior authorization

Turn an opaque queue into a cited clinical-administrative review.

This research pattern aligns clinical evidence, coding, coverage, medical-necessity criteria, authorization history, and contract rules so reviewers could see what supports or blocks the request.

The decision

Is the submission complete, covered, medically supported, and ready for approval, query, specialist review, or denial?

The evidence

Claim/request, notes, orders, results, codes, policy and benefit, fee schedule, authorization, clinical criteria, provider contract, and history.

System behavior

Extracts and reconciles evidence, tests rules, identifies missing support, drafts a cited summary, and provides precise query or reason language.

Human boundary

Authorized clinical and payer reviewers own adverse and exceptional decisions, medical necessity, appeals, and overrides.

Reference architecture

A clinically governed evidence layer.

The patient timeline, consent, provenance, terminology, and intended use travel together.

01 · Protect

Consent & identity

Purpose, consent, role, minimum-necessary access, residency, retention, and audit are enforced before retrieval.

02 · Normalize

Longitudinal health graph

Patient, encounter, condition, observation, medication, procedure, provider, coverage, and guideline share temporal meaning.

03 · Assist

Clinical retrieval + rules

Models summarize and predict; terminology, applicability, contraindication, coverage, and escalation rules constrain.

04 · Review

Role-specific workflows

Clinician, coder, care manager, lab, and payer experiences expose sources, uncertainty, edits, and approval state.

The line we do not cross.

  • No autonomous diagnosisModels do not independently diagnose, prescribe, discharge, triage emergencies, or determine medical necessity.
  • Minimum necessaryProtected health information is purpose-bound, access-controlled, logged, and excluded from model improvement without authority.
  • Patient specificityGuidance is checked for applicability, date, comorbidity, medication, allergy, age, pregnancy, and available evidence.
  • Visible uncertaintyContradiction, missing context, out-of-domain input, and low confidence route to qualified review.

Validation before clinical use

Validate with the people who carry the consequence.

Clinical, operational, privacy, safety, and equity review happen before workflow release—not after a model benchmark.

  1. 01

    Curate

    Build representative, de-identified evaluation sets with clinical adjudication, difficult negation, chronology, dialect, and rare edge cases.

  2. 02

    Safety test

    Probe omission, hallucination, contraindication, wrong-patient context, prompt injection, leakage, and unsafe reliance.

  3. 03

    Silent trial

    Measure against real workflow without affecting care; review every material miss and subgroup difference.

  4. 04

    Controlled rollout

    Release by role and site with training, monitoring, override, incident response, rollback, and periodic revalidation.

What earns the right to scale.

Clinical faithfulness
Material statements are supported by the correct patient, encounter, time, and source evidence.
Safety-weighted recall
Critical conditions, contraindications, results, and escalation triggers receive consequence-weighted testing.
Equity
Performance is examined across language, dialect, sex, age, condition, care setting, and other relevant cohorts.
Capacity returned
Verified reduction in documentation, search, and avoidable queue time without shifting hidden work to another role.

Public sector signals

UAE healthcare is pairing AI ambition with governance.

  1. PureHealth 2025 integrated annual report

    Public context spanning active AI pilots, document intelligence, clinical assistance, diagnostics, and consumer health.

  2. PureHealth ambient clinical documentation pilot

    A regional signal for multi-phase testing and clinician time returned through ambient documentation.

  3. Abu Dhabi DoH healthcare AI governance initiative

    Primary regulator signal for safety, transparency, accountability, privacy, and ethical deployment.

  4. Abu Dhabi DoH Responsible AI Standard

    Formal local standard informing lifecycle controls and validation for health AI.

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