Precision Emergency Call Routing With ANI Validation
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Solution Overview
Problem
North American public safety answering points (PSAPs) face an overwhelming shortage of call takers, leading to increased wait times and insufficient treatment of urgent calls due to a high volume of nuisance calls from deactivated mobile phones with invalid automatic number identifications (ANI), such as 000-911-0000 or 911-000-0000, which lack location information in automatic location identification (ALI) databases.
Innovation Solution
Implementing a precision routing system that includes a first agent to determine the validity of ANI and route calls to a second agent for emergency confirmation, followed by a second agent determining the legitimacy and location of the emergency, and routing the call to an appropriate PSAP based on proximity, jurisdiction, and emergency type, using machine-learning algorithms to filter out nuisance calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all incoming calls are routed to PSAP without filtering, then no legitimate emergency calls are missed, but wait times increase and call takers become overwhelmed due to nuisance calls
Solution Approach 1:
The system performs preliminary actions by validating ANI and requesting emergency confirmations before routing calls to PSAP. This advance filtering of nuisance calls reduces the burden on call takers and decreases wait times for legitimate emergencies without missing any actual emergency calls.
Solution Approach 2:
The system introduces an intermediary filtering layer between the incoming call and the PSAP. This intermediary validates ANI, requests emergency confirmations, and determines call legitimacy before routing, thereby protecting PSAP resources from nuisance calls while ensuring legitimate emergencies are delivered.
2Productivity
If ANI validation and emergency confirmation requests are implemented, then nuisance calls are filtered out, but processing time for legitimate calls increases
Solution Approach 1:
The system applies partial filtering by validating ANI format and requesting emergency confirmations only when needed. By not over-filtering or applying excessive validation steps to all calls, the system maintains high processing efficiency for legitimate calls while still effectively reducing nuisance calls.
Solution Approach 2:
The system uses automated ANI validation and emergency confirmation request processes that operate without human intervention. This self-service approach filters nuisance calls efficiently while minimizing additional processing time for legitimate calls, as the automated system quickly validates and routes appropriate calls.
3Ease of operation
If automated filtering systems are deployed, then call taker workload is reduced, but system complexity increases
Solution Approach 1:
The system replaces manual call taker assessment with automated electronic validation of ANI and emergency confirmations. This substitution of mechanical/human processes with automated electronic systems reduces call taker workload while managing system complexity through standardized validation algorithms.
Solution Approach 2:
The system changes key parameters for call evaluation by using automated ANI validation rules and emergency confirmation request protocols. These parameter changes enable objective, consistent filtering of nuisance calls while simplifying call taker decisions, balancing automation complexity with operational ease.
Data Source
AI summary
A method implemented by a system includes receiving a call; determining whether the call is legitimate; and routing the call to a public safety answering point (PSAP).