When we discuss digital transformation in healthcare, we focus on shiny apps, dashboards, and AI promises. But at the heart of the tools powering this era is a quieter truth: digital health is only as strong as its underlying data. Right now, there may be a problem with the data powering some of our systems, especially those that are weak or underdeveloped.
Across Africa and other emerging markets, the rush to digitise care has outpaced efforts to ensure data is clean, consistent, and trustworthy. The consequences are real and growing. Behind each misreported fever case, missing antenatal record, duplicated patient file, or “adjusted” metric is a decision made on porous foundations.
The Hidden Cost Of Wrong Numbers
- A community clinic uploads malaria test results a day late.
- A crowded outpatient unit enters vitals in a hurry, resulting in two patients being mixed up.
- An under-resourced facility guesses numbers because the internet is down again.
Individually, these moments seem minor. Together, they distort the truth of what’s happening. Algorithms, policymakers, funders, and health leaders end up acting on a warped picture.
In systems increasingly driven by data, even small errors carry outsized consequences.
Why Bad Data Happens?
The problem doesn’t begin with technology; it begins with the ecosystem around it and is compounded by culture and a lack of discipline among those tasked with handling, reporting, and managing data.
Bad data shows up when:
- Connectivity is weak, and platforms fail to sync.
- Healthcare professionals are overwhelmed, rushing through manual entries.
- Systems don’t speak to one another, leading to duplication and copy-paste errors.
- Devices are outdated, producing unreliable readings.
- Incentives reward good-looking dashboards instead of accurate reporting.
- Training is inconsistent. This is especially true for frontline staff new to digital tools.
Digital health was meant to simplify care. In many places, it has just added work, and data quality suffers first.
The Domino Effect: When Bad Data Shapes Big Decisions
The real danger isn’t the individual mistake — it’s the chain reaction that follows.
- AI triage tools offer inaccurate risk scores because their training data is flawed.
- Public health surveillance systems miss early signals of outbreaks due to patchy reporting.
- Procurement teams overspend or undersupply because demand data isn’t clean.
- Hospital rankings reward facilities based on numbers that don’t reflect reality.
- Policy reforms are shaped by data gaps that policymakers may not even realise exist.
And in global health funding—where metrics and outcomes drive investment decisions —bad data can cost countries (and big investors) millions.
Africa’s Dual Reality: A Challenge And An Advantage
Africa faces a unique paradox.
Many health systems are digitising rapidly but unevenly, creating gaps where old paper-based systems collide with new digital layers. In some states, facilities still rely on manual registers that are later transcribed into digital systems. Errors multiply in transition.
Yet this moment of transformation also presents an extraordinary opportunity: Countries should build clean, modern data pipelines without the legacy complications that burden Western systems. To do so, they must prioritise investment in data quality standards, robust training for data handlers, and the implementation of clear data governance policies from the outset. By making these priorities central now, they can leapfrog the mess.
Recognising The Stakes, Some Groups Are Tackling The Problem Head-On
A new wave of innovators, ministries, and health workers is quietly stepping up:
- Data validation layers that automatically flag impossible entries.
- Offline-first platforms that prevent guesswork during network downtime.
- Integrated systems that reduce duplicate data entry.
- AI-powered anomaly detectors show when numbers don’t make sense.
- Training programmes, turning nurses and frontline staff into “data stewards” who treat clean data as part of quality care.
To strengthen digital health systems, stakeholders should direct attention and resources toward these foundational solutions. Prioritise implementing validation tools, offline-capable platforms, integration between systems, AI-based anomaly detection, and widespread frontline staff training in data quality. By making these solutions central, health programs can sustain improvements and build trust in health data.
The Future: Clean Data As A Public Health Currency
Digital health innovations are now judged by the reliability of the data building blocks, not how advanced they look. Clean data is becoming a national power—shaping responses, financing, innovation, and public trust.
To ensure success, prioritise data quality as an integral part of clinical operations. Establish clear standards for data management, routinely measure and report on data quality, and create accountability mechanisms.
Countries and innovators that prioritise data quality will build more reliable, impactful digital health systems.
Because in the end, digital health isn’t about software.
Act now: make data quality a non-negotiable standard across all levels of digital health interventions.
Further Reading
The Impact of Data Quality Problems in Healthcare
