Operational challenges of large-scale emergency call systems

How Artificial Intelligence can strengthen, not replace, human decision-making

Emergency call systems represent one of the most critical layers of public safety infrastructure. While citizens experience these systems as a single phone call, the reality behind the scenes is far more complex. Each call initiates a time-critical decision chain involving multiple agencies, human judgment under stress, and technical systems that must operate without failure.

Turkey’s nationwide 112 Emergency Call System provides a unique case for observing these challenges at scale. Serving a population of more than 85 million with a unified emergency number, the system integrates police, health services, fire brigades, gendarmerie, coast guard, disaster management, and other public safety units under one operational roof. This scale creates both operational strength and structural stress points that are increasingly relevant for NG112 and NG911 ecosystems worldwide.

Cognitive load at the first point of contact

The most critical moment in any emergency response is the first contact between the caller and the system. Call takers must simultaneously calm the caller, extract reliable information, determine the nature and urgency of the incident, confirm location, and decide which institutions should be involved. In practice, this process often unfolds under severe time pressure, emotional distress, and incomplete or contradictory information.

From a technical perspective, many legacy systems still rely on manual or semi-manual classification models. Calls are interpreted primarily through human experience rather than structured semantic analysis. Background noise, panic, language barriers, or fragmented narratives frequently reduce information quality, increasing the risk of delayed or incorrect routing.

This challenge is not unique to Turkey. As call volumes rise across Europe, the limitations of purely human-centric decision flows become increasingly visible.

The limits of static routing models

Another structural issue lies in static routing logic. Most emergency calls are routed based on geographic proximity. While location remains essential, it is no longer sufficient in isolation. Certain incidents require specialized handling: domestic violence, suicide risk, child-related cases, chemical hazards, or foreign-language calls all demand different competencies.

NG112 architecture already enables richer data flows. However, without an intelligent decision layer, these capabilities remain underutilized. The key question is not only where a call comes from, but what it represents.

Artificial Intelligence as a decision support layer

Artificial intelligence should not be viewed as a replacement for emergency call professionals. Its real value lies in functioning as a cognitive support layer that enhances human performance.

Applied correctly, AI can analyze incoming voice or text streams in real time, identifying semantic indicators of risk, distress, violence, or medical urgency. These indicators can be presented to the call taker as contextual cues rather than automated decisions. The result is not automation, but consistency and support.

AI-assisted prioritization can help distinguish high-risk incidents from low-priority or repetitive calls earlier in the process. Importantly, this does not mean blocking access to emergency services. Instead, it allows systems to preserve human attention for cases where seconds truly matter.

Dynamic routing is another critical area. In an NG112-aligned environment, AI can support routing decisions based on multiple parameters: language compatibility, operator expertise, current workload, and incident type. This approach complements the existing geographic logic rather than replacing it.

From reactive to anticipatory operations

Beyond individual calls, anonymized historical data enables a shift toward anticipatory operations. Patterns related to time, location, and incident type can support resource planning and staffing decisions. For large-scale systems, this predictive insight can significantly improve readiness without increasing personnel pressure.

Such capabilities become particularly valuable during peak periods, mass events, or crisis situations where human capacity alone is insufficient.

Why large systems matter

Turkey’s 112 system, due to its scale and institutional integration, functions as a high-stress test environment for emergency response concepts. Solutions that operate effectively under these conditions are inherently resilient and transferable.

As NG112 evolves across Europe, lessons from large, unified systems can contribute meaningfully to the broader public safety ecosystem. The challenges faced in Turkey today mirror those emerging elsewhere: higher call volumes, multi-channel communication, and the need to protect human decision-making under pressure.

Conclusion

The future of emergency call systems is not defined by replacing people with technology, but by strengthening human judgment through intelligent support. Artificial intelligence, when designed as a modular and transparent decision aid, can reduce cognitive overload, improve consistency, and help emergency services respond faster and more effectively.

In public safety, complexity is unavoidable. The goal is not to eliminate it, but to manage it intelligently — always keeping the human operator at the center of the system.

Because in the end, emergency response is not about algorithms or infrastructure.It is about ensuring that the right help reaches the right place at the right time.

About the Author

Fahri Gürcan is a technical specialist working within Turkey’s national 112 Emergency Call Center system. His work focuses on large-scale emergency operations, decision-support processes, and the application of artificial intelligence to public safety infrastructures. He is also the founder of an independent initiative exploring AI-driven solutions for emergency and crisis management systems.