health workers per 1,000 population in sub-Saharan Africa
Global Health · Open Source · Clinical AI
Evidence-based
clinical
guidance
for
Evidence-based
clinical
guidance
for
under-resourced
care
settings.
ClinicDx develops offline AI clinical decision support tools for nurses, clinical officers, and physicians in facilities without specialist access — where diagnostic uncertainty is a daily reality, and connectivity cannot be assumed.
4 billion
people without access to a specialist
1 in 50,000
clinician-to-patient ratio in rural settings
25%
of global disease burden managed by 3% of the clinical workforce
The Scale of the Crisis
radiologists available — vs. 120 per million in Europe
in retrospective EMR data entry in low-resource settings
The Problem
Four gaps that cost lives every day
Clinicians in rural facilities face compounding failures at every point of care delivery.
Documentation Gap
Manual EMR entry is slow, error-prone, and often happens hours after care — losing clinical nuance.
Voice Scribe
Knowledge Gap
Clinical officers make life-or-death decisions alone — no specialist, no protocol library.
Clinical Decision Support
Lab Gap
Paper lab results don't get digitized, interpreted in context, or flagged for critical values.
Lab Digitization
Imaging Gap
DICOM files go unread. 3.6 radiologists per million means most images are never reviewed.
Imaging Analysis
Runs locally on commodity hardware
OpenMRS native integration
5 clinical languages
One system. Four modules.
Every critical point of care covered.
ClinicDx plugs directly into the OpenMRS workflow clinicians already use — adding AI intelligence at the moment of decision, without internet, without cloud dependency.
Modules
Four integrated intelligence modules
Click any module to see its capabilities and a live simulation.
Clinical Decision Support
Evidence-cited differential diagnoses, dosing guidance, urgency stratification, and safety alerts at the point of care.
Lab Digitization
Photograph paper lab results. ClinicDx digitizes, flags critical abnormals, and interprets in full patient context.
Imaging Analysis
On-device DICOM analysis and paper imaging capture. Structured findings for referral decisions, fully offline.
Multilingual Scribe
Short phrases in Swahili, Amharic, French, Hausa, or English → complete structured SOAP note in seconds.
How It Works
From patient chart to evidence-cited output in seconds
Open the patient chart
Clinician opens an existing OpenMRS patient encounter — no new workflow or app switching required.
Activate ClinicDx
Choose voice input, photo capture, or DICOM upload. ClinicDx reads the full patient context automatically.
Receive structured output
Evidence-cited differential, dosing, SOAP note, or imaging findings appear in seconds — fully offline.
Outcomes
Measured impact for clinicians and health systems
0%
Reduction in diagnostic uncertainty
from structured differentials and discriminating features guiding each clinical assessment.
0%
Data sovereignty maintained
Patient data never leaves the facility. No cloud. No external API. No privacy exposure.
0%
Reduction in documentation time
with multilingual phrase-based scribe converting spoken input to structured SOAP notes.
Who It’s For
Purpose-built for every layer of care delivery
Clinical Officers & Nurses
- Second opinion at point of care
- Differential diagnosis support
- Drug dosing & safety guidance
- Documentation in native language
Physicians & Medical Officers
- Complex case decision support
- Lab & imaging interpretation
- Referral decision support
- Multilingual scribe workflow
NGOs & Aid Organizations
- Rapid deploy on commodity hardware
- No internet dependency
- Consistent evidence-based protocols
- Multi-site rollout ready
Ministries of Health
- National OpenMRS integration
- Anonymized population analytics
- WHO guideline alignment
- Scalable, modular rollout
Patient data never leaves the facility.
No cloud. No external API. No privacy exposure. ClinicDx operates entirely within the four walls of your facility — clinical intelligence without surveillance.
On-device inference
All AI computation happens locally on facility hardware. No data ever leaves the machine.
OpenMRS native
Patient data stays inside the national health system’s own infrastructure, not a third-party cloud.
WHO alignment
Structured outputs and audit trails support national digital health governance frameworks.
FAQ
Common questions from deployment teams
No. ClinicDx is designed from the ground up to operate fully offline. All AI inference runs on local hardware at the facility. There is no dependency on external servers, cloud APIs, or internet connectivity of any kind.
ClinicDx runs on commodity hardware — a standard workstation or rugged laptop with a modern CPU. No GPU is required for CDS and scribe functionality. Imaging analysis benefits from a mid-range discrete GPU but can operate in CPU-only mode.
ClinicDx integrates natively with OpenMRS 3.x as a module or companion application. It reads structured patient data directly from OpenMRS and writes structured outputs back — maintaining all data within the national health record system.
The scribe module supports Swahili, Amharic, French, Hausa, and English out of the box. CDS outputs are available in English and French, with localization roadmap prioritized by deployment region.
Completely. Patient data never leaves the facility. ClinicDx performs all AI computation locally — no external API calls, no cloud uploads, no telemetry. Outputs are structured and auditable, meeting WHO and national health data governance standards.
Yes. The CDS knowledge base is curated and aligned to WHO and MSF clinical protocols, including WHO Essential Medicines List, WHO Malaria Guidelines, and IMCI protocols. Every CDS output includes citations so clinicians can verify the evidence basis.
Help us bring specialist-level care to facilities that have never had one.
Pilot ClinicDx in your facilities to reduce diagnostic uncertainty, preserve data sovereignty, and return clinician time to patient care.