Navigate DHIS2 for health data entry, reporting, and dashboard interpretation
DHIS2 is the world's largest health management information system platform, used by 100+ countries — including 40+ in Africa — to collect, analyze, and visualize health data. This skill teaches you to enter data, navigate dashboards, and understand how facility-level data feeds national health intelligence. For AI practitioners, DHIS2 data is often the training signal for population health models.
In most African health systems, DHIS2 is where routine health data lives. Malaria cases, vaccination coverage, maternal mortality — all flow through DHIS2. If you're building AI for African health, you need to understand this system. If you're a medical student, you'll encounter DHIS2 throughout your career.
Go to https://play.dhis2.org/ and log into the demo instance. Explore:
| Concept | What It Means | Example |
|---|---|---|
| Organisation Unit | A location in the health system hierarchy | Country → Region → District → Facility |
| Data Element | A specific thing being measured | "Malaria cases confirmed", "BCG doses given" |
| Period | When the data was collected | Monthly, quarterly, yearly |
| Data Set | A collection of data elements for a form | "Monthly Disease Surveillance Report" |
| Indicator | A calculated value from data elements | "Malaria positivity rate = confirmed / tested" |
In the Data Entry app:
Open the "Disease Surveillance" dashboard. For each visualization, answer:
Use the Data Visualizer to create a chart, then export as:
Note the structure of the exported data — this is what AI models consume.
| Criterion | Meets Standard | Below Standard |
|---|---|---|
| Data entry completed | All required fields filled, form submitted | Incomplete or not submitted |
| Dashboard interpretation | Identifies trends, gaps, and implications | Surface-level description only |
| Data quality awareness | Notes missing data and discusses impact on AI | Ignores data quality issues |
| Export format correct | Valid CSV/JSON with metadata | Corrupted or incomplete export |
fhir-resource-basics — How DHIS2 data maps to FHIR resourcesdigitalize-paper-records — What happens before data reaches DHIS2ai-readiness-scorecard — DHIS2 usage is a key dimension of institutional AI readiness