1. Understand service delivery
A hospital can examine how many patients attended its clinics and how attendance changed over time.
Next lesson1.2 From Raw Data to Actionable InformationNext moduleModule 2. Excel for Health Data Management and Analysis
MODULE 1 · LESSON 1.1
Welcome to the World of Health Data
Estimated time: 10–15 minutes
ON THE BOARD · Module 1 summary
Health data is collected every day. Collecting it is only the start.
- 1. Health data is collected every day. Collecting it is only the start.
- 2. Learn the difference between raw data and information, and where health data comes from.
- 3. Look for missing values, late reports, and figures that cannot be true.
- 4. Say what a number describes, and what it cannot decide.
MODULE SUMMARY
Module 1 summary
Health data is collected every day. Collecting it is only the start.
Learn the difference between raw data and information, and where health data comes from.
Look for missing values, late reports, and figures that cannot be true.
Say what a number describes, and what it cannot decide.
The board writes the point she is saying. It changes as the lesson moves on. Keep the sound on.
READ COMPLETE NOTES
1.1.1 Imagine you run a small shop
Let's start with something simple.
Imagine you sell oranges in a small shop.
On Monday, you sell 10 oranges. On Tuesday, you sell 15 oranges. On Wednesday, you sell 8 oranges.
You write the numbers in a notebook because you want to remember how many oranges you sell each day.
| Day | Oranges sold |
|---|---|
| Monday | 10 |
| Tuesday | 15 |
| Wednesday | 8 |
Congratulations! You have started collecting data.
But why would you collect these numbers?
- Which day did you sell the most oranges?
- Which day did you sell the fewest?
- Should you buy more oranges next week?
- Why did sales decrease on Wednesday?
Your numbers become useful when you start asking questions and using the answers to make decisions.
That is the basic idea behind data analytics.
1.1.2 Now imagine you work in a hospital
A hospital also collects information every day.
For example, a fictional primary healthcare centre records the following patient attendance:
| Day | Patients attended |
|---|---|
| Monday | 45 |
| Tuesday | 60 |
| Wednesday | 38 |
These figures are health data.
A health information professional, administrator or data analyst might ask:
- Why did attendance increase on Tuesday?
- Why did fewer patients attend on Wednesday?
- How many patients visited the facility during the three days?
- Does the facility have enough staff to serve its patients?
- Do we need more information before changing the staffing schedule?
Notice something important.
The figures tell us how many patients attended. They do not, by themselves, explain why attendance changed or whether the quality of care improved.
We need to examine the information carefully before making decisions.
FRONTLINE TO DECISION
- 1Daily clinic counts
- 2Monthly NHMIS summary
- 3DHIS2
- 4Policy and resource decisions
1.1.3 What is health data?
Health data is information collected about health, healthcare services, patients, diseases, health outcomes and health programmes.
Examples include:
- Number of patients attending a clinic.
- Number of babies delivered in a facility.
- Number of children vaccinated.
- Number of laboratory tests conducted.
- Number of health facilities submitting monthly reports.
- Patient waiting times.
- Reasons people give for missing appointments.
- Results of a community health survey.
Some health data consists of numbers. Other health data consists of words, descriptions or observations.
We will learn more about these differences in Lesson 1.2.
1.1.4 What is health data analytics?
Health data analytics is the process of examining health data to identify patterns, answer questions, understand performance and support informed decisions.
Let's use our fictional healthcare centre again.
Suppose the facility records the following monthly attendance:
| Month | Patients attended |
|---|---|
| January | 800 |
| February | 950 |
| March | 700 |
An analyst can calculate totals, compare the months and identify changes.
However, the analyst should not immediately conclude that the facility performed better in February.
Why?
Because patient attendance alone does not tell us everything about healthcare performance.
We may need additional information about staffing, available services, patient needs, reporting completeness and other relevant factors.
Good analysis combines calculations with careful interpretation.
1.1.5 Why does health data analytics matter?
Health data analytics can help organisations:
2. Identify potential problems
An unusual decline in reported vaccinations may require investigation.
3. Improve resource planning
Reliable information can help managers assess staffing, equipment and supply needs.
4. Monitor health programmes
An NGO can examine whether its activities are reaching the intended population.
5. Support policy decisions
Health authorities can use reliable evidence to help determine priorities and allocate resources.
6. Improve accountability
Organisations can use documented results to explain what they achieved, where challenges remain and what needs to change.
The quality of the decisions depends partly on the quality, relevance and interpretation of the data.
1.1.6 Who uses health data analytics?
Health data analytics is relevant to several professional groups.
Health Information Management professionals
Check record completeness and prepare health information reports.
Hospital administrators
Review service activity and support operational planning.
Public health professionals
Monitor health indicators and investigate emerging concerns.
M&E officers
Track programme indicators and assess progress against targets.
NGO programme officers
Review service coverage and identify implementation gaps.
Health researchers
Examine datasets to answer research questions.
Data analysts
Prepare datasets, calculate indicators and communicate findings.
Policymakers
Use evidence to inform health planning and policy decisions.
You do not need to become an expert in every area. The aim is to develop practical skills that help you perform relevant tasks more effectively.
1.1.7 Your first knowledge check
Answer these questions before looking at the suggested answers.
Lesson 1.1 knowledge check
Choose an answer for each question, then check. The suggested answers stay hidden until you do.
1. A clinic records that 120 patients attended on Monday. What does 120 represent?
2. What is the main purpose of health data analytics?
3. A clinic reports fewer patients this month than last month. Can we immediately conclude that the quality of care has declined?
4. Which of the following is an example of health data?
1.1.8 Lesson summary
In this lesson, you learned that:
- Data consists of recorded facts, values or observations.
- Health data concerns health, healthcare services and health programmes.
- Analytics involves examining data to answer questions and support decisions.
- Numbers alone do not always explain why something happened.
- Good decisions require reliable data and careful interpretation.