Product Overview
Who Atlas is built for, and the problem it exists to solve.
The problem. Large healthcare payers and provider networks run on drug policy — formulary tiers, pricing agreements, reimbursement rules, prior-authorisation logic — that carries real financial and clinical weight. That policy usually lives in spreadsheets, PDFs, and the memory of a handful of people. Changing a rule takes weeks; finding out whether a rule was even followed takes even longer.
What Atlas does. Atlas turns that policy into a governed, testable system — a browsable formulary and pricing catalog for everyday use, with the underlying rule logic one click away for anyone who needs to see why a drug is priced or gated the way it is. It gives cost teams an actual workflow to size and action savings, not just a report, and gives everyone a place to ask "what does policy actually say here?" and get a sourced answer.
Why Abu Dhabi. This concept is scoped around the structure of the Abu Dhabi healthcare market — a Department of Health (DOH)–regulated environment, Daman/Thiqa as the dominant payers, Malaffi as the health information exchange, and ADHICS and PDPL governing how health and pricing data can be stored and shared.
Where these personas come from
The brief for this kind of platform describes it as "large-scale... managing complex drug policies, pricing, reimbursement and cost optimisation," "backend-heavy, rules and data-driven," with "significant financial impact," built by someone who can translate "business rules into scalable product capabilities." That points to three people who touch the rules directly — a policy owner writing them, an ops lead acting on cost, and a reviewer handling what the rules can't resolve — plus the leadership layer who has to trust and report the numbers those rules produce.
Who it's built for
Commercial & Formulary Policy Owner
As a Formulary Policy Owner, I want to browse and filter the current formulary by class, diagnosis, tier, and institution, so that I can answer "what's covered, and at what price" without digging through spreadsheets.
As a Formulary Policy Owner, I want to simulate a proposed rule change against historical claims before it goes live, so that I don't introduce a costly or non-compliant policy by accident.
Cost & Reimbursement Operations Lead
As a Cost & Reimbursement Lead, I want to size a savings opportunity with real cost and volume inputs, so that I can quantify impact before proposing it.
As a Cost & Reimbursement Lead, I want to track an opportunity from flagged through to actioned, so that there's accountability for outcomes, not just identified potential.
Clinical Policy Reviewer
As a Clinical Policy Reviewer, I want to see a queue of cases the rules engine couldn't auto-resolve, with the full rule trace attached, so that I can make a fast, informed judgment call instead of starting from scratch.
As a Clinical Policy Reviewer, I want to ask a plain-language question and get a sourced policy answer, so that I don't have to search PDFs mid-conversation.
DOH / Payer Leadership
As a DOH/Payer leader, I want top-line KPIs with a clear, defensible calculation behind each one, so that I can present these numbers in governance reporting without a follow-up "how was this calculated?"
As a DOH/Payer leader, I want to see savings actually delivered, not just identified, so that I can hold teams accountable for real outcomes.
Product Validation does Atlas actually fulfil these stories?
Checked each user story against what's actually built, rather than assuming the feature list covers it. Two gaps were found and closed; one is flagged as an honest next-iteration item rather than faked.
| User story (persona) | Fulfilled by | Status |
|---|---|---|
| Policy Owner — browse & filter formulary | Formulary & Pricing Catalog | ✓ Supported |
| Policy Owner — simulate a rule change pre-launch | Rule Change Impact Simulator | ✓ Now interactive |
| Cost Lead — size an opportunity | Savings Calculator | ✓ Supported |
| Cost Lead — track flagged → actioned | Optimisation Pipeline | ✓ Supported |
| Clinical Reviewer — exception queue with context | Policy Exception Queue | ✓ Now actionable |
| Clinical Reviewer — plain-language sourced answers | Policy Assistant | ✓ Supported |
| Leadership — defensible KPI calculations | Dictionary & KPI Definitions | ✓ Supported |
| Leadership — savings delivered, not just identified | Pipeline "Actioned" total vs. Dashboard KPI | ⚠ Not yet linked — noted below |
The last row is a real gap, left visible rather than hidden: the pipeline's "Actioned" total and the Dashboard's "Savings Identified" KPI run independently in this prototype. In a live build, an actioned pipeline item would post directly to that KPI — that data pipeline just isn't wired up here.
Scope at a glance
Policy Assistant
A knowledge bank built on formularies, pricing schedules, and regulatory documents — answers are grounded only in what's uploaded, with every response traceable to a source.
Knowledge Bank
Sample source library shown for demonstration. Swap in real DOH Abu Dhabi formulary, Daman/Thiqa reimbursement, and pricing documents to activate live sourced answers.
Guided Drug Finder structured intake → formulary options
Matched Options ranked by member cost-share
Formulary & Pricing Catalog
Browse the active formulary by class, diagnosis, and price — filtered by institution type. Click "View rule logic" on any listed drug to see the underlying policy conditions.
| Drug | Class | Diagnosis / Indication | Purpose | Tier | Price (AED) | Policy |
|---|
Prices shown reflect the selected institution rate. Sample data — see Dictionary & KPIs for methodology.
Rule Logic
Policy Exception Queue rules that can't auto-resolve
Not every case fits the ruleset — clinical judgment calls are routed to a reviewer with full rule trace attached, rather than silently approved or denied.
| Case | Trigger | Status |
|---|---|---|
| EXC_2291 | Off-label biologic use, oncology | Pending clinical review |
| EXC_2288 | Step therapy waiver requested | Pending clinical review |
| EXC_2276 | Dual-eligible cross-plan conflict | Resolved |
Rule Change Impact Simulator before committing a rule to production
Pick a live rule and a proposed change — Atlas projects the effect against the prior 90 days of claims before anything goes live.
Rule Version & Audit History what changed, when, and who approved it
Policy doesn't stand still. This traces how a rule has evolved over time — and makes explicit that a claim is evaluated against the rule version that was in effect on the date of service, not whatever version is live today.
Cost Optimisation
Size a savings opportunity, then move it through an actual pipeline — from flagged to actioned. Charts and historical spend live on the Dashboard; this is where the work happens.
Savings Calculator size an opportunity
Optimisation Pipeline flagged → review → approved → actioned
Flagged
Under Review
Approved
Actioned
Dashboard
Consolidated analytics — top-line KPIs, formulary mix, reimbursement performance, and spend concentration in one place. All figures are sample data; see Dictionary & KPIs for definitions and calculations.
Formulary Mix by Tier
Claim Volume & Approval Rate last 6 months
Denial Reasons this quarter
Spend by Therapeutic Category
Claims Aging
| Bucket | Claim Count | Value (AED) | Status |
|---|---|---|---|
| 0–3 days | 18,240 | 41.2M | On target |
| 4–7 days | 4,110 | 12.6M | On target |
| 8–14 days | 1,380 | 5.4M | Watch |
| 15+ days | 296 | 2.1M | Escalated |
Dictionary & KPI Definitions
Shared vocabulary for policy owners, claims teams, and reviewers — so a rule, a metric, or a regulatory term means the same thing to everyone using Atlas.
A note on the numbers read this first
Every figure on this platform — spend, compliance rate, turnaround, savings — is sample data, generated to be internally consistent and realistic in scale, not pulled from any real payer, provider, or Abu Dhabi regulatory source. Nothing here represents Falcon Health Group, Cleveland Clinic Abu Dhabi, Burjeel Hospital, DOH, Daman, or any actual organisation's real performance.
Glossary
Top-Line KPI Definitions
| KPI | Definition | Calculation | Why it matters |
|---|---|---|---|
| Covered Annual Spend | Total plan spend on covered drugs across all formulary tiers, trailing 12 months | Σ (unit price × quantity dispensed) across all approved claims, Jan–Dec | The baseline every other commercial metric is measured against |
| Formulary Compliance | % of claims dispensed within current formulary rules, no manual override | (claims matching active rule set ÷ total claims) × 100 | Measures whether policy is actually being followed at the point of dispensing |
| Avg. Claim Turnaround | Mean days from claim submission to adjudication decision | Σ (adjudication date − submission date) ÷ total claims | Core service-level metric for members and providers |
| Denial Rate | % of submitted claims denied, tracked by reason code | (denied claims ÷ submitted claims) × 100, segmented by denial reason | Surfaces policy gaps and documentation issues early |
| PA Approval Rate | % of prior-authorisation requests approved without manual escalation | (auto-approved PA requests ÷ total PA requests) × 100 | Indicates whether PA criteria are clear and well-calibrated |
| Savings Identified (YTD) | Annualised AED value of cost-optimisation opportunities actioned | Σ (baseline cost − optimised cost) × affected claim volume, for each actioned opportunity | Direct measure of the platform's commercial impact |
| Claims Aging >14 Days | Count and value of claims exceeding the target adjudication window | COUNT / SUM(claim value) where (today − submission date) > 14 | Early-warning signal for operational backlog |
Reading the deltas
| Shown as | Meaning | Calculation |
|---|---|---|
| ↑ 6.2% YoY | Year-over-year change vs. the same 12-month period last year | ((current period − prior year period) ÷ prior year period) × 100 |
| ↑ 3.8 pts | Percentage-point change for a rate/ratio metric (not a % change) | current rate − prior period rate, in percentage points |
| ↓ 1.4 days | Absolute change in a time-based metric, arrow shows direction of improvement | current period value − prior period value |
| ↑ vs AED 6.4M target | Actual-to-date compared against a governance-set target, not a prior period | YTD actual − target, shown as over/under |
Cost Optimisation Methodology
Each opportunity on the Cost Optimisation pipeline uses the same underlying formula as the Savings Calculator: (current unit cost − optimised unit cost) × affected claim volume × expected uptake. In a live build, uptake and volume assumptions would be configurable and reviewed with the clinical policy team before an opportunity is reported as committed savings.
| Opportunity | How the estimate is calculated | Value |
|---|---|---|
| Biosimilar switch | (originator cost − biosimilar cost) × eligible Tier 3 claim volume × assumed switch rate | +3.4M |
| Step-therapy expansion | (specialty drug cost − first-line generic cost) × non-compliant claim volume × expected uptake | +2.1M |
| Prior-auth threshold tuning | reduction in over-authorised specialty claims × average specialty claim cost | +1.8M |
| Duplicate therapy flag | value of concurrent overlapping prescriptions identified × expected avoidance rate | +1.2M |