AI-powered health decision support

Decision support tools for improved health outcomes.

We build decision support that helps healthcare providers find what’s likely wrong, see why, and take the right next step. Built with clinicians, research and data from East Africa, so it fits the patients in front of you.

Try a case Interactive demo
Child, 3 years · fever for 3 days Findings: tap to change
Likely conditions
Pneumonia0.81
Malaria0.11
Common cold0.08

Why: Chest indrawing (danger sign); Cough, Fast breathing

Next step: urgent referral, danger sign present

Illustrative probabilities from a scripted example. Not the Elsa model, and not medical advice.

Working with government & partners

  • UNICEF Innovation Fund
  • Fondation Botnar · Afya-Tek
  • Ifakara Health Institute
  • D-tree
  • Tanzania Data Lab
  • Pharmacy Council
  • Meru District Council
Why it matters

Most health decisions are made far from a specialist, a lab or a second opinion.

In community clinics, drug shops and rural facilities, one provider often sees dozens of patients a day with little diagnostic equipment. The few minutes they spend with each patient shape everything that happens next.

Elsa brings specialist-level decision support into that moment, so the right patients are treated, referred or followed up, and fewer go home with the wrong medicine.

How Elsa supports a decision

From symptoms to next step, always showing why.

Explore the platform
Elsa assessmentIllustrative
1 · Assess
Fever 3 daysCoughFast breathingChest indrawing
Kiswahili: homa siku 3, kikohozi, kupumua haraka, kifua kuvutika ndani
2 · Reason
Severe pneumonia0.64
Malaria0.19
Common cold0.09
Why: chest indrawing and fast breathing for age.
3 · Act
Urgent referral: danger sign present
The provider confirms and decides.
01 · Assess

Capture what the provider sees

You capture symptoms, history, vitals and local context in English or Kiswahili, guided by the questions that matter for this patient.

02 · Reason

Rank the likely conditions

Elsa combines expert knowledge with local disease patterns to rank the likely causes, and shows you which findings drove each one.

03 · Act

Guide the next step

Clear guidance on triage, referral, treatment and follow-up, while you stay in charge of the decision.

Capabilities

Elsa’s health intelligence, for four kinds of decisions.

Disease identification

Uses a patient’s history and how they present today to find the most likely causes of illness.

Triage

Sorts patients by urgency, so the sickest are seen first and everyone gets care at the right level.

Adherence prediction

Flags patients likely to miss their HIV treatment, so clinicians can step in before they’re lost to follow-up.

Outbreak insight

Tracks how common diseases are over time, sharpening diagnosis and giving an early signal of outbreaks.

Explore the platform →
Built to be trusted

A safety net for clinicians, not a black box.

Elsa stays in the background until it can help: a missed danger sign, a likely alternative, a safer next step. The provider always makes the call.

How we keep Elsa safe →

Explainable

Every suggestion shows the findings behind it, so providers quickly understand why, and know when to disagree.

Context-specific

Built with expert knowledge, research and data from East Africa, so models reflect the patients providers actually see.

English & Kiswahili

Available in both English and Kiswahili, to support providers across every level of care.

Open by default

Open source and open algorithms, reviewed by a community of clinicians, researchers and engineers.

How we keep Elsa safe →
A provider at a Care and Treatment Center uses Elsa on a tablet
From the field
“It offers more than just tracking my patients. It helps me assess my patients’ symptoms and recommends a treatment plan.”
Sabato MichaelClinician, Care and Treatment Center · Arusha
See Elsa in the field

Let’s change healthcare together.

Work with us