If there’s anything the last few years have made clear, it’s that resilience in healthcare isn’t just about having enough hospital beds or the right stockpile of supplies. It’s about the entire architecture—how resources, infrastructure, and personnel come together in the face of crisis. But when policymakers and researchers set out to measure this resilience, the conversation often gets tangled in aggregate numbers and anecdote. What’s needed is a sharper way to see how the system is built, and where its weak spots lie. The International Standard Industrial Classification (ISIC) system, with its dry logic and detailed codes, might just offer that lens.
Take ISIC 8610, which covers hospital activities, and ISIC 8621, general medical practice. On paper, they’re categories like any other. In practice, they let us see the skeleton of a health system—how many hospitals exist, how they’re distributed, where clusters of clinics might offset gaps in acute care. Local and national authorities that keep their registries up to date with ISIC codes are sitting on a map of potential strengths and vulnerabilities, whether or not they realize it.
This mapping isn’t just academic. Imagine a region with a high number of ISIC 8610 facilities—but all packed into one urban center, leaving rural areas thinly covered. Or a place where small general practices (ISIC 8621) are well-distributed, but hospitals are few and far between. The picture that emerges from ISIC-coded data isn’t just a snapshot; it’s the beginning of a diagnosis. Are healthcare facilities being built where people actually live? Are resources spread in a way that can handle both everyday demand and sudden surges?
When the pandemic hit, these questions became painfully real. Some areas weathered the storm, able to surge capacity or redirect patients from overloaded hospitals to nearby clinics. Others faltered, not because they lacked buildings, but because the distribution and mix of care options were out of sync with need. Analysts who were able to overlay ISIC-coded facility data with pandemic outcome metrics—cases, mortality, recovery rates—had a distinct advantage. They could spot correlations between facility density and health outcomes, even controlling for population and workforce.
Workforce density adds another dimension. Having a hospital or clinic on the map is one thing; staffing it with trained professionals is another. When ISIC-coded facility data is layered with statistics on medical and nursing personnel, the analysis deepens. Are the hospitals that appear numerous on paper actually staffed to handle a crisis? Do general practices have enough practitioners to absorb overflow during a public health emergency? By correlating ISIC data with workforce counts and deployment, planners can identify not just where facilities exist, but where they function as true nodes of resilience—or where they are, in effect, hollow shells.
Of course, the data is never perfect. Facilities can be misclassified, workforce figures may lag behind reality, and not all types of care are equally visible in the coding system. But the act of mapping—of putting the ISIC framework to work—brings a new clarity. Suddenly, policymakers can see beyond the headlines to the actual structure of the system, and plan accordingly.
It’s also a process that invites ongoing conversation. When maps and facility counts are shared with practitioners and the public, gaps get pointed out, misclassifications are corrected, and new needs come to light. The data becomes less a static product and more a tool for collective problem-solving.
In the end, the power of ISIC in health system resilience isn’t in the codes themselves. It’s in the way they make the invisible visible, turning the complexity of hospitals, clinics, and personnel into a landscape that can be navigated, critiqued, and—crucially—strengthened before the next crisis hits.