Health systems around the world are wrestling with rising attrition, persistent skill shortages, and structural gaps that no amount of quick hiring seems to fix. In the GCC, where healthcare demand keeps climbing and workforces are increasingly global, the pressure is even sharper. TERN steps into this space with a different view, treating workforce management as critical infrastructure rather than a staffing problem to patch on the fly.
Drawing on experience inside highly regulated European systems, the company has built an AI-driven model that ties recruitment, licensing, deployment, and long-term retention into one governed workflow.
Here, Avinav Nigam, founder and CEO, TERN Group, we explore why that distinction matters, where traditional systems break down, and how smarter workforce intelligence can strengthen care continuity while keeping costs under control.
TERN describes itself as a healthcare workforce infrastructure company rather than a staffing or recruitment platform. How did the company’s origins, operating model, and European standards-based approach shape this positioning, and why is that distinction important for healthcare systems today?
TERN was built working inside some of Europe’s most regulated healthcare systems, particularly Germany and the UK. In those environments, workforce decisions aren’t just HR issues. They’re patient safety issues. You can’t separate hiring from licencing, or deployment from compliance, or retention from care continuity. It’s all connected.
That shaped how we think about the problem. Most workforce failures we saw weren’t because of candidate shortages. They were because systems were fragmented – treating hiring, credentialing, and deployment as separate problems managed by different teams with different tools.
In those markets, you have to build for governance and auditability from day one, not just speed. That became our foundation. That’s why we position ourselves as AI workforce infrastructure rather than a staffing platform. Staffing platforms help you move people into roles faster. We help healthcare systems understand who’s actually ready to work, where capacity really exists, and how to deploy talent safely over time and retain them long term – and with AI at the core.
The distinction matters because – healthcare workforce is one of the largest cost and risk areas for any health system. If you treat it like a staffing function you only think about during a crisis, you’re always going to be in firefighting mode. Infrastructure means you’re building for stability and long-term resilience.
Many healthcare systems frame workforce shortages as a hiring problem. From your perspective, where do system design and workforce deployment break down, and why does adding headcount alone fail to improve care continuity or outcomes?
Headcount is visible and easy to measure, so it becomes the default answer. But more people doesn’t automatically mean better care. Sometimes it just means more chaos.
The breakdown happens in how people get deployed and utilised. Systems hire skilled professionals and then drop them into roles without proper preparation or context. Teams stay stretched not because there aren’t enough bodies, but because the right skills aren’t consistently available where care actually happens.
Adding headcount into a badly designed system can actually make things worse. You increase the burden on existing staff who have to onboard and train new people while doing their own jobs.
International studies show it takes healthcare systems an average of 83 days to hire a registered nurse, and global data indicates each nursing turnover costs upwards of $60,000 when you account for recruiting, onboarding, lost productivity, and the burden on existing staff who have to train newcomers while doing their own jobs. That accelerates burnout. And if the underlying coordination problems aren’t fixed, you just end up with higher turnover at a larger scale.
The core issue is this: continuity of care needs continuity of the workforce. Globally, over the past five years, hospitals have turned over 107 per cent of their workforce – meaning they’ve replaced their entire staff and then some. That requires more than hiring. You need to understand who’s ready for what roles, how they fit, how they’ll develop over time. Without that, you’re just running on a treadmill, hiring constantly while outcomes stay flat or get worse.
This is where TERN’s approach differs fundamentally. The platform doesn’t just accelerate hiring – it helps healthcare systems deploy smarter and retain longer. You get visibility into candidate readiness before making the hire, not six months after when it’s too late.
Healthcare leaders can see which candidates actually match specific role requirements, where their skills fit in the system, and how likely they are to stay based on competency alignment and career trajectory. Instead of the typical pattern – hire someone in 83 days, discover six months later they’re in the wrong role or leaving due to poor fit – you’re making evidence-based deployment decisions from day one.
That’s how you break the turnover cycle. Not by adding more headcount faster, but by getting the right people into the right roles with the right support from the start.
Healthcare workforce management is often fragmented across sourcing, training, licensing, deployment, and operations. How does this fragmentation drive reactive staffing decisions, burnout, and rising attrition across health systems?
Fragmentation creates blind spots. And blind spots force people into reactive mode. When your sourcing system doesn’t talk to licensing, when training programmes aren’t aligned with actual deployment needs; when credential tracking sits in spreadsheets rather than integrated platforms, you lose the ability to plan ahead. You end up with professionals overtrained in areas that don’t matter for their roles, or undertrained for the work they’re actually doing. Either way, it’s frustrating.
And when deployment happens without visibility into who’s credentialed, who’s been trained for what, or how workload is distributed, you can only react after gaps appear and start affecting care.
What happens then is predictable. Last-minute redeployments. Heavy reliance on expensive temporary staff. Uneven workloads. Constant disruption to care teams. Global healthcare data shows that 44 per cent of healthcare turnover is preventable through improvements in work environment and better deployment decisions, yet most organisations still operate reactively.
Over time, professionals lose predictability and control. They can’t plan their schedules or their development. They feel interchangeable rather than valued.
This is especially true in places like the GCC, where you have international workforces dealing with complex credentialing across different regulatory systems. When processes are fragmented, even highly motivated professionals burn out, not from the work, but from the system chaos around it.
People don’t leave healthcare because they stopped caring. They leave because fragmented systems make it impossible to sustain contribution over time. International workforce studies indicate that 95 per cent of hospital separations are voluntary – meaning these are preventable losses, not retirements or involuntary terminations.
TERN positions recruitment as the entry point, not the solution. How does workforce intelligence change how healthcare organisations plan, deploy, and sustain talent at scale?
Recruitment answers the question: “Who can start Monday?”
Workforce intelligence answers: “Who’s still going to be effective and engaged six months from now and how do we get them there?”
When you have actual visibility into skills, readiness, credentials, deployment history, development needs, planning changes. You move from reactive to anticipatory.
Instead of filling gaps one role at a time when someone quits, you start seeing patterns earlier. You can identify where expertise is sitting underutilised in one area while another area is desperate for it. You can align training with what’s actually coming, not just what broke yesterday.
At scale, this becomes essential. In large healthcare workforces, you can’t rely on individual relationships and people remembering things. You need systems that give the right information to whoever’s making deployment decisions, whether that’s a frontline manager or executive leadership.
Without intelligence, scale just means more complexity. With it, you can actually build continuity.
AI-led workforce intelligence is becoming more prominent across healthcare operations. What practical role does AI play in turning talent mobility into a governed, auditable, and efficient system rather than a transactional staffing model?
AI’s value isn’t automating decisions. It’s eliminating blind spots that make decisions risky or slow.
Practically, AI connects data that usually lives in separate places. Who’s credentialed? Who’s ready today? Which roles match their actual competencies? Where are compliance issues emerging before they become crises? Instead of spending weeks coordinating all this manually, AI makes it accessible in real-time.
This matters especially for cross-border talent mobility, which is reality for most GCC healthcare. OECD data shows that international healthcare hiring can take three–six months longer than domestic hiring, largely due to fragmented credentialing and compliance workflows. When applied correctly, AI doesn’t disrupt the system. It stabilises it by giving leaders faster, safer, and more defensible workforce decisions at scale.
We use AI used to pre-verify credentials, standardise readiness assessments, and create auditable workflows across sourcing, licensing, and deployment. In live European deployments, this has reduced work-readiness timelines by 40–60 per cent, while maintaining full audit trails required by regulators.
When applied correctly, AI doesn’t disrupt healthcare systems. It stabilises them by giving leaders faster, safer and more defensible workforce decisions at scale.
How does combining ethical talent access with operational insight help healthcare providers optimise costs while maintaining quality, safety, and continuity of care?
Cost pressure is real. But cutting costs without insight usually creates bigger problems down the line.
What most people don’t see are the hidden workforce costs. Attrition from unethical recruitment practices where people leave because of debt burdens. Dependency on expensive temporary staffing to fill recurring gaps. Compliance failures that take whole teams offline. None of this shows up clearly in budgets, but it’s expensive.
Ethical talent access removes those costs at the source. WHO estimates that attrition and inefficiency account for up to 20 per cent of total healthcare workforce expenditure in some systems. Much of this is driven by poor role fit, unethical recruitment practices that lead to early exits, and over-reliance on premium temporary staffing.
Ethical talent access removes these risks at the source. Operational insight means once people join, they’re deployed well and supported properly, so they stay longer and contribute more.
In practice, healthcare providers using governed, zero-fee recruitment pathways and workforce intelligence report lower early attrition and reduced agency dependency. At TERN, healthcare systems see more predictable staffing costs and fewer emergency hires because readiness, deployment, and continuity are planned together.
The result is straightforward. Fewer emergency hires. Less reliance on premium temporary staff. More stable teams delivering consistent, safer care. Lower turnover. All of that drives cost down, while quality goes up.
When the workforce is stable and properly matched to roles, you see fewer clinical errors, better patient outcomes, and stronger team coordination. That’s the safety dividend. When people aren’t constantly onboarding or covering for gaps, care quality improves naturally.
The key insight is that quality, safety, and cost don’t have to compete. When workforce systems are designed with transparency and continuity built in, they reinforce each other instead of pulling in different directions.
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