dhis2-data-entry

name: dhis2-data-entry description: Navigate DHIS2 for health data entry, reporting, and dashboard interpretation category: digital-health-foundations raigh_tier: MOOC difficulty: beginner estimated_time: "2 hours" prerequisites: [] tags: [dhis2, hmis, data-entry, dashboards, surveillance] evidence_basis: "https://dhis2.org/" version: "1.0"

DHIS2 Data Entry & Dashboard Interpretation

Purpose

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.

Learning Objectives

1. Navigate the DHIS2 web interface and understand its data model (Organisation Units, Data Elements, Periods)
2. Enter health facility data using data entry forms
3. Interpret a DHIS2 dashboard with maps, charts, and pivot tables
4. Export data in standard formats (CSV, JSON, ADX)
5. Understand data quality indicators and how missing data affects AI models

Context

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.

Steps

Step 1: Access the DHIS2 Demo Instance

Go to https://play.dhis2.org/ and log into the demo instance. Explore:

• Data Entry app: Where facility-level data is entered
• Dashboard app: Where visualizations live
• Data Visualizer: Build your own charts
• Maps: Geographic visualization of health data

Step 2: Understand the Data Model

Step 3: Enter Data

In the Data Entry app:

1. Select an Organisation Unit (pick a health facility)
2. Select a Data Set (e.g., "Disease Surveillance")
3. Select a Period (e.g., January 2026)
4. Enter values for 5+ data elements
5. Click "Complete" to submit

Step 4: Interpret a Dashboard

Open the "Disease Surveillance" dashboard. For each visualization, answer:

• What data elements are displayed?
• What time period is shown?
• What geographic level (national, regional, facility)?
• What trends do you see?
• Where is data missing, and why does that matter for AI?

Step 5: Export Data

Use the Data Visualizer to create a chart, then export as:

1. CSV (for spreadsheet analysis)
2. JSON (for programmatic use / AI pipelines)

Note the structure of the exported data — this is what AI models consume.

Artifacts

1. Data Entry Screenshot — Evidence of completed data entry for one facility/period
2. Dashboard Analysis — 1-page write-up interpreting 3 dashboard visualizations (what the data shows, what's missing, implications)
3. Exported Dataset — CSV or JSON export with brief description of contents and quality assessment

Assessment Criteria

Common Mistakes

• Entering data for the wrong period (check the period selector carefully)
• Confusing data elements with indicators (elements are raw, indicators are calculated)
• Ignoring "0" vs blank — zero means "none observed", blank means "not reported" — critical difference for AI
• Assuming dashboard data is complete — always check reporting rates

Related Skills

• fhir-resource-basics — How DHIS2 data maps to FHIR resources
• digitalize-paper-records — What happens before data reaches DHIS2
• ai-readiness-scorecard — DHIS2 usage is a key dimension of institutional AI readiness

References

• DHIS2 Documentation: https://docs.dhis2.org/
• DHIS2 Academy: https://www.dhis2.org/academy
• HISP Centre: https://www.mn.uio.no/ifi/english/research/networks/hisp/