# ODIN IVR-Interactive Solution for Emergency Calls Handling
Additionally, the location of the caller is crucial ([[http://www.eena.org/view/en/About112/legislation.html--last](http://www.eena.org/view/en/About112/legislation.html--last)]([http://www.eena.org/view/en/About112/legislation.html--last](http://www.eena.org/view/en/About112/legislation.html--last)) accessed on 23 August 2022), which has led to the implementation of certain techniques for locating callers of 112 in the telephone network. Emergency call centers are often flooded with calls. The nature of these events could be real emergencies in a short period, related, for instance, to natural phenomena; pocket calls; or malicious calls. AI tools could help analyze calls and messages, which would reduce waiting times and save more lives. Waiting times can be reduced by more effective filtering of abusive calls and by providing suggestions to operators, allowing them to make decisions more quickly. There are additional ways in which AI can assist operators. According to studies in Denmark [7], emergency operators fail to identify approximately 25% of cardiac arrest cases and therefore miss the opportunity to provide resuscitation instructions. Each year, more than 350,000 cases of cardiac arrest occur outside the hospital. If resuscitation maneuvers are applied in the first minutes, the survival rate increases substantially. Artificial intelligence could fill in this gap and support the early detection of cardiac arrest. Based on these assumptions, in 2020, Belgium implemented an IVR solution called the Options Menu ([[https://www.sos112.be/en/menu/--last](https://www.sos112.be/en/menu/--last)]([https://www.sos112.be/en/menu/--last](https://www.sos112.be/en/menu/--last)) accessed on 5 October 2022) for the national 112 SOS Emergency System. The Belgium approach includes only two options that redirect the citizens to either the police department or fire brigade and ambulance. The Options Menu system was first tested as a pilot project between 1 October 2018 and 17 January 2019 in the provinces of Namur and West Flanders. The populations of these two provinces used the new system without any problem, and the authorities did not receive any complaints, which led to the expansion of the project nationwide. Moreover, a similar IVR solution was implemented in the United Kingdom for the Devon and Cornwall Police Department ([[https://www.devon-cornwall.police.uk/contact/non-emergency-101/what-is-interactive-voice-response-ivr/--last](https://www.devon-cornwall.police.uk/contact/non-emergency-101/what-is-interactive-voice-response-ivr/--last)]([https://www.devon-cornwall.police.uk/contact/non-emergency-101/what-is-interactive-voice-response-ivr/--last](https://www.devon-cornwall.police.uk/contact/non-emergency-101/what-is-interactive-voice-response-ivr/--last)) accessed on 7 October 2022). The IVR system helps callers reach each person or department that is best placed to resolve their inquiries using a two-level decision tree. At first glance, there are no significant issues in this use case, but the most crucial aspect of these systems is the time constraint. Therefore, the implementation of a chatbot/IVR solution is a challenging task, especially in the area of real-time task scheduling. Moreover, the system must guarantee 100% acknowledgments and resolution of calls in a fixed period of time, usually less than a minute. Missed calls can lead to disastrous situations or even death. Another aspect that needs to be taken into consideration is user satisfaction and compliance with the system. For instance, when a person is facing a critical situation or a medical emergency, his/her patience is very low, and he/she is likely to drop the call if faced with endless call redirects. As such, our solution must be implemented with a maximum depth of three layers. Moreover, the implementation of such a system faces multiple problems related to scheduling theory. First, as mentioned in the previous paragraph, we are dealing with a deadline-oriented system.