Most diagnostic networks reach only a fraction of clinics. AI-DNO models where facilities, devices, and sample routes should go—so accurate diagnostics reach more people, faster and at lower cost.
DNO is a geospatial analytics approach used to:
Analyze the current diagnostic network
Recommend the optimal type, number, and location of diagnostics and the sample referral network needed to hit national health goals
Minimize overall network costs, subject to applied access constraints
In practice, DNO decides where testing facilities and devices should be placed, and how samples and results should move between them — to get accurate diagnostics to more people, faster and at lower cost.
Most diagnostic networks aren't held back by a lack of effort, they're held back by a lack of data-driven decisions. Facilities, devices, and referral routes get built up over time without the ability to model trade-offs, so networks end up expensive to run and still fail to reach the people who need them most.
| Characteristic | Without AI-DNO | With AI-DNO |
|---|---|---|
| Access to testing | Low and inequitable — hard-to-reach populations wait longest | Testing placed where underserved populations actually live |
| Collection-to-lab distance | Long sample journeys delay results and raise transport cost | Optimized routes so samples travel less and return faster |
| Referral model | Patients travel to testing sites | Samples move; patients stay in their community |
| Testing capacity | Clustered at a few central sites | Decentralized toward where demand actually is |
| Device placement | Idle in some sites, queues in others | Placed by utilization and demand, not history |
| Trade-off analysis | No cost vs coverage scenarios to compare | Baseline vs optimized, side by side, before you commit |
AI-DNO makes placement, routes, and budget trade-offs visible—so you redesign diagnostic networks with evidence, not guesswork.
Legacy networks leave machines idle in one place and queues elsewhere. Scenario modeling puts capacity where demand and equity gaps actually are, before you commit spend.
Compare baseline vs optimized placements before you move a single device.
See coverage, turnaround, and budget impact together—not in separate sheets.
Model sample flow so hard-to-reach sites stop waiting the longest.
AI-DNO turns facilities, devices, and routes into scenarios you can compare before redesign.
Put capacity where demand and equity gaps actually are—shorten queues where it matters.
Work solo on Pro or Ultra, or with your org on Enterprise—the same optimization engine, the right collaboration model.
Ask for recommendations tied to your project network—not generic templates.
AI-DNO makes placement, routes, and budget trade-offs visible before you redesign diagnostic capacity.
01
Capture facilities, devices, transport routes, demand, and existing diagnostic capacity.
02
Compare baseline vs optimized designs for various future scenarios including cost, turnaround time, and access.
03
Surface impact of decisions like moving idle devices, testing backlogs, and redesign of sample referral and transport systems, increasing capacity by adding new devices.
04
Share plans in individual or enterprise workspaces so stakeholders align before changing and spend.
AI-DNO is built to remove the friction that usually slows diagnostic network planning down — so you spend less time wrangling data and more time making decisions.
Bring in facility, device, demand, and transport data without wrestling with rigid formats or manual cleanup.
Automatically surfaces gaps, bottlenecks, and inefficiencies in your current network before you start modeling changes.
See the cost implications of each scenario as you build it, not as an afterthought.
Designed for planners, not just data scientists — no deep technical background required to get meaningful results.
Get modeled scenarios and recommendations in a fraction of the time traditional network analysis takes.
Chat with the DNO agent to ask questions, explore trade-offs, and get recommendations grounded in your own project data.
AI-DNO supports individual and enterprise workspaces so facility and device data stays under your control.
Pro, Ultra, and Enterprise plans stay separated so organizational data is not mixed by default.
Share scenarios with the right stakeholders—without exposing raw network details broadly.
Device, route, and demand inputs are treated as operational assets, not public artifacts.
Scenario comparisons stay transparent so budget and placement decisions can be audited later.
A side-by-side view of common DNO analysis tools — license, skills, timeline, and data history requirements.
| Characteristic | AccessMod | OptiDx | BI (Power BI / Tableau) | AI DNO |
|---|---|---|---|---|
| License fee | Open source | Open access | License required | Subscription (hosted platform) |
| Skillset required | GIS analyst | Supply chain analyst | Data analyst | Programme manager (self-serve UI) |
| Typical timeline | 2–3 months | 3–6 months | Depends on analysis | Minutes to hours per scenario |
| DNO history | Encouraged | Required | Depends on analysis | Not required |
| Further resources | UNIGE AccessMod docs | OptiDx.org guides & SOPs | Vendor training & dashboards | In-app guide + AI agent support |
Characteristics for AccessMod, OptiDx, and BI (Power BI / Tableau) align with the Stop TB Partnership DNO Implementation Guide (Annexure C). AI DNO reflects the Froncort platform.
AI-DNO supports solo planners on Pro or Ultra, and Enterprise teams — same optimization engine, the right workspace model.
Design and compare diagnostic network scenarios on your own—without organization setup.
Higher usage caps and a larger network footprint for advanced solo diagnostic planning.
Collaborate across your organization with shared projects, members, and invites.
Pro includes 20 scenario optimizations per month. Need more? Purchase add-on packs anytime from your billing dashboard.
Create your AI-DNO workspace, or reach out to explore enterprise collaboration.
How scenario modeling helps you redesign diagnostic networks before you move devices.
