Ensuring Accuracy in Public Health: Why Data Validation is Important in High-Stakes Health Projects

By Adeola Joseph

Data is the foundation of public health, but not all data is reliable. When decisions about patient care, resource allocation, and disease control depend on accurate information, errors aren’t just inconvenient— they can have life-or-death consequences.

In large-scale health programs that rely on real-time reporting, data validation is critical to ensuring sound decision-making. Mistakes in reporting, incomplete data, or inconsistencies across sources can mean the difference between an effective health program and one that wastes resources or overlooks critical cases.

Adeola Joseph, a public health data specialist with extensive experience in large-scale disease control programs, understands this challenge firsthand. During a phone conversation, he discussed the realities of data validation in public health, especially in resource-constrained contexts where errors often go unnoticed until they become systemic problems.

When people hear the term “data validation,” they believe it refers to another technical procedure. Why is it so important in public health?

Most people don’t realize how much public health relies on accurate data – until they see what happens when the data is wrong. Imagine a national HIV treatment program where patient adherence records are inconsistent. If the system incorrectly shows that a patient is taking their medication when they are not, the patient might miss crucial follow-up care.

If a reporting mechanism underestimates the number of new infections in a region, resources and interventions could be misdirected, allowing an outbreak to spread unchecked.

Data validation isn’t just about making reports look neat – it’s about ensuring that every statistic represents real people with real health needs. Reliable data allows public health professionals to respond appropriately, allocate resources effectively, and ultimately save lives.

Q: What are some typical data validation problems you’ve encountereded, and what impact do they have?

There are several ways data errors occur. One of the most common issues is duplication, where multiple records are created for the same patient due to slight variations in name spelling or formatting. Without a strong validation system, someone might appear to be receiving treatment at multiple clinics when, in reality, they are not receiving care at all..

Another major issue is missing values. When health workers fail to complete records-such as documenting a diagnosis without linking it to a treatment outcome – the system has no way of tracking whether the patient received cre or was lost to follow-up.

Simple human errors, such as typos or incorrect dates, may seem minor but can distort entire datasets. These errors affect more than just the quality of the data – they have real-world consequences.

Poor data leads to flawed policy decisions, misallocated funding, and inaccurate assessments of public health programs.

For example, if 20% of reported malaria cases in a dataset are duplicates or misclassified, the estimated burden of disease could be significantly inflated or underestimated, leading governments and donors to allocate resources inefficiently.

How do you ensure that data validation methods keep up with large-scale health projects?

You need systems that do more than just collect data – they must actively validate it at several levels. This involves combining automation with human oversight.

● Automated validation detects Basic errors such as duplicate entries, missing fields, nonsensical dates. .
● Cross-referencing data sources can flag inconsistencies, such as a patient’s visit being recorded in one system but no corresponding medication pickup in pharmacy records.
● Training and awareness among health workers are just as important as technical solutions. If frontline staff see validation as just another bureaucratic step, mistakes will persist. However, when they understand that an inaccurate report could result in fewer test kits for their clinic next month, they become more invested in getting the data right.

Given the difficulties of implementing strong data validation in low-resource environments, what has worked in your experience?

The key is designing systems that work for the people using them. . Too often, validation is considered as an afterthought—something that happens after data has been entered into the system. However, by integrating validation at the point of entry, many errors can be prevented before they occur..

Some effective strategies include:

● Using drop-down menus instead of free-text to standardize data entry.
● Implementing real-time prompts that remind users to complete missing information.
● Adding logic checks to detect outliers, such as a patient’s age being recorded as 200 years old.
Another option that has been effective is decentralized review. Instead of waiting until data reaches a central level to execute validation checks, you allow field workers to review and amend data as the point of entry. If a disparity is discovered, it is addressed promptly rather than months later, when it is too late to rectify.

What is the long-term impact of rigorous data validation on public health programs?

Simply put, better data leads to better health outcomes.

For healthcare providers: Accurate data ensures they can track patient progress and deliver the right interventions.

For policymakers: Reliable information allows them to allocate resources efficiently, directing funds and medical supplies where they are needed most.

For entire health programs: Decisions are based on actual impact rather than flawed assumptions.

For example, in HIV programs, data validation ensures that viral load suppression rates reflect real treatment success rather than reporting gaps. It guarantees that coverage estimates in vaccination programs are correct, so that no community falls behind owing to inaccurate data.

More than anything, robust data validation fosters confidence. When governments, donors, and healthcare providers know that the figures they are dealing with are reliable, decision-making becomes more confident, financing decisions become more strategic, and patient care improves. It’s not only about cleaning up the data; it’s also about making public health operate properly.

In an era where data drives decision-making, one thing is clear: good data saves lives.

Adeola Joseph is an experienced Technical Officer with over six years of expertise in transforming public health data systems. He currently manages a database for more than 300,000 patients under Nigeria’s CDC HIV program.

Adeola specializes in data analysis, visualization, and project management. He is certified in Power BI, SQL, and R, and his innovative, data-driven approaches to digital health have enhanced decision-making that positively impacts case-finding efforts and streamlines data processes.

Additionally, Adeola has presented his insights at international forums, such as the OpenMRS conference and the national NDR boot camp, demonstrating his proficiency in data-driven public health strategies.