Diagnostic Network Optimization for efficient networks supporting accessible and equitable testing
Diagnostic networks in many regions are underfunded and inefficient, and that gap limits access to quality healthcare. Basic diagnostic capacity often reaches only a small share of primary care clinics, leaving planners to balance access, efficiency, and cost, usually without the tools to model the trade-offs.
What is Diagnostic Network Optimization?
Diagnostic Network Optimization (DNO) is a geospatial analytics approach used to analyze the current diagnostic network, recommend the optimal type, number, and location of diagnostics and the associated sample referral network needed to achieve national health goals, and minimize overall network costs, subject to applied access constraints. In practice, DNO is the process of deciding 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.
Map your network
Review current facility and device coverage and pinpoint gaps.
Optimize placement
Compare scenarios for where to add, move, or consolidate testing capacity.
Optimize routes
Find the most efficient frequency, mode, and route for moving samples and results.
Plan with confidence
Model multiple changes at once and see their combined impact before you commit.
High costs and system inefficiencies are keeping testing out of reach
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.
| Aspect | Without AI-DNO | With AI-DNO |
|---|---|---|
| Network design | Static, opaque networks | Modeled, equitable networks |
| Scenario planning | No way to compare cost versus coverage trade-offs | Compare baseline versus optimized networks before redesign |
| Visibility | Coverage, turnaround, and budget in separate spreadsheets | Cost, access, and turnaround impact side by side |
| Decisions | Guesswork and generic advice | Agent recommendations tied to your project data |
| Access | Low, inequitable access; hard-to-reach sites wait longest | Capacity placed where demand and equity gaps actually are |
Networks designed for access & cost
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 and optimized designs before you commit to a redesign.
See cost, coverage, and turnaround together, not in separate sheets.
Surface the sites that wait longest and close those gaps in the design.
Plan solo on Pro or Ultra, or collaborate with your org on Enterprise.
Design networks that balance cost, speed, and access
AI-DNO turns facilities, devices, and routes into scenarios you can compare before redesign.
Network scenarios
Compare baseline versus optimized placements before you move a single device.
Cost & access clarity
See coverage, turnaround, and budget impact together, not in separate sheets.
Route & hub design
Model sample flow so hard-to-reach sites stop waiting the longest.
Fewer idle machines
Put capacity where demand and equity gaps actually are, and shorten queues where it matters.
Pro, Ultra & Enterprise
Work solo on Pro or Ultra, or with your org on Enterprise, the same optimization engine, the right collaboration model.
Grounded DNO advice
Ask for recommendations tied to your project network, not generic templates.
From network data to evidence-led design
AI-DNO makes placement, routes, and budget trade-offs visible before you redesign diagnostic capacity.
- 01
Map the baseline network
Capture facilities, devices, transport routes, demand, and existing diagnostic capacity.
- 02
Run scenarios
Compare baseline versus optimized designs for various future scenarios, including cost, turnaround time, and access.
- 03
Compare trade-offs
Surface the impact of decisions like moving idle devices, clearing testing backlogs, and redesigning sample referral and transport systems, or adding new devices to increase capacity.
- 04
Commit with clarity
Share plans in individual or enterprise workspaces so stakeholders align before changing and spend.
From data to decisions, faster
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.
Easier data input & handling
Bring in facility, device, demand, and transport data without wrestling with rigid formats or manual cleanup.
Identifies challenges in your baseline
Automatically surfaces gaps, bottlenecks, and inefficiencies in your current network before you start modeling changes.
Built-in costing
See the cost implications of each scenario as you build it, not as an afterthought.
Simple and intuitive
Designed for planners, not just data scientists, no deep technical background required to get meaningful results.
Faster results
Get modeled scenarios and recommendations in a fraction of the time traditional network analysis takes.
Conversational, NLP-powered interface
Chat with the DNO agent to ask questions, explore trade-offs, and get recommendations grounded in your own project data.
Start free locally, scale in the cloud
AI-DNO open-source core covers baseline analysis and Open Referral optimization. The AI-NETRA platform adds collaboration, AI assistance, and enterprise controls.
Open-source AI-DNO
FreeSelf-hosted
Run the optimization engine on your machine with JSON/CSV inputs, ideal for analysts validating networks before programme rollout.
Baseline network metrics
Headline KPIs and validation reports.
Open Referral MILP
Sample referral redesign optimization.
JSON + CSV I/O
CLI and local no-code UI (CSV upload in Prepare inputs).
No license fee
Apache-style OSS core; solver download on first install.
AI-NETRA platform
PaidHosted premium
Multi-user workspace with agentic assistance, richer ingestion, and billing-backed feature limits with add-on packs.
Everything in open source, plus
Hosted workspace
Projects, scenarios, and team collaboration.
AI agent chat & voice
Grounded recommendations on your network data.
Excel ingestion
Paid ingest jobs for programme workbooks.
Usage limits + add-ons
Monthly scenario caps with purchasable top-ups.
Enterprise support
Per-seat metrics, sales-assisted plans.
Why teams choose AI-DNO
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 and SOPs | Vendor training and dashboards | In-app guide and 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.
Design networks with evidence, not guesswork
Request a demo to explore AI-DNO for your programme or enterprise team.
Questions about AI-DNO
How scenario modeling helps you redesign diagnostic networks before you move devices.
What does AI-DNO optimize?
AI-DNO models facilities, devices, transport routes, and demand so you can compare network scenarios for coverage, turnaround time, cost, and equity before redesigning the system.
Who can use AI-DNO, individuals or organizations?
Both. Pro and Ultra solo planners and Enterprise teams use the same optimization engine, with collaboration and project models that fit how your organization works.
Can we compare baseline versus optimized networks?
Yes. You can run scenarios side by side, baseline placements versus optimized options, so stakeholders see trade-offs clearly before committing budget or moving machines.
Does AI-DNO include sample transport and hubs?
Yes. Route and hub design is part of the workflow, helping you model sample flow so hard-to-reach sites are not left waiting the longest.
How do recommendations stay grounded in our network?
The DNO agent answers against your project network data, facilities, capacity, and routes, so guidance reflects your geography, not generic templates.
How do we get started with AI-DNO?
Create an AI-DNO account, set up your organization or individual workspace, and start a project with the facilities and demand data you already manage.