Information Systems Researcher and Risk Analyst, Femi Esan, has stressed the need to address a very crucial challenge: what happens to the vast amount of information the healthcare system generates.
Esan, in a recent research work, noted that while Healthcare information is generated every day by hospitals, primary healthcare centres, laboratories, pharmacies, insurance platforms and public-health agencies, these sources, he argues, do not always communicate effectively with one another.
“A hospital may understand what is happening within its walls while a health authority struggles to see patterns developing across multiple facilities or communities,” he stated.
For Esan, collecting more data is therefore not enough; the bigger opportunity, he believes, lies in connecting relevant information and turning it into intelligence that decision-makers can actually use.
That, he added, could mean identifying a facility where rising patient numbers, staff shortages and recurring medicine stockouts are creating an increased risk of service disruption.
Using maternal healthcare, as an example, the Risk Analyst stated, it could mean recognising a combination of clinical and access-related factors that suggest a woman requires closer attention; while in disease surveillance, it could mean detecting an unusual pattern early enough for public-health officials to investigate.
“The important shift is from asking only “What happened?” to also asking “What is happening now, what could happen next, and what should we do about it?” he added.
Esan’s background in risk analysis shapes much of this thinking. Risk management rarely offers certainty about what will happen next. Instead, it looks for signals, considers the likelihood and potential impact of an event, and helps organisations decide how to respond.
Applied to healthcare, he stated, that approach could be particularly useful in an environment where resources are limited and difficult choices must constantly be made.
“If several hospitals need additional personnel, for example, simply knowing that they are understaffed does not resolve the problem. Decision-makers still have to determine where the shortage presents the greatest risk,” he argued.
Esan, therefore, sees predictive intelligence as becoming increasingly useful, not in terms of simply forecasting problems, but helping decision-makers understand which risks require attention first.
On Artificial Intelligence, Esan argues there are limits. According to him, as enthusiasm around AI grows, healthcare systems must avoid treating algorithms as unquestionable decision-makers. A model may identify an unusual pattern or classify a population as high-risk, but healthcare professionals and administrators still need to understand why, he stated.
“A system that flags a hospital as high-risk, for instance, should ideally help decision-makers understand the factors behind that assessment. Is demand increasing? Are staffing levels falling? Are medicine shortages becoming more frequent? How reliable is the available information? he stated.
Esan, therefore, argued that responsible predictive intelligence cannot be built around algorithms alone, adding that such will also require governance, appropriate data, local context and people who remain accountable for the decisions ultimately made.
The most important part of his research direction, however, comes after prediction.
A system may correctly identify that a problem is likely to occur, but the prediction has little value if nothing happens as a result, he stated.
He is therefore interested in how information systems can support intervention prioritisation and resource allocation.
Esan’s research reflects a broader argument about Nigeria’s digital transformation: arguing that digitising existing processes should not be the final destination.
He believes the greater opportunity lies in building information systems that help institutions recognise risk earlier, understand what is driving it and make better-informed decisions about what to do next.
For Nigeria’s healthcare system, that could mean moving gradually from systems that primarily document yesterday’s problems, towards systems that also help decision-makers anticipate tomorrow’s.
The future of healthcare information systems may therefore depend not on how much data Nigeria can collect, but on how intelligently and responsibly it can use what it already has.