AI Quality Alerts for Missed CAD Records in Emergency Dispatch
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Solution Overview
Problem
PSAPs face challenges in providing effective quality assurance due to resource constraints, leading to inadequate auditing of emergency calls, which can result in improper handling and delayed dispatch of emergency services.
Innovation Solution
Implementing an AI-driven quality assurance system that generates alerts when call takers fail to create CAD records within a threshold time and when emergency service units are not sufficiently dispatched, using AI models trained on historical data to analyze call transcripts and CAD records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quality assurance auditing is performed by supervisors, then quality control can be maintained, but the system becomes resource-intensive and cannot scale effectively
Solution Approach 1:
The system enables self-service quality assurance by having the CAD system itself automatically audit its own operations through AI models that monitor call handling, CAD record creation timing, and emergency service dispatch accuracy, eliminating the need for separate manual auditing resources
Solution Approach 2:
The patent replaces manual mechanical quality assurance processes with automated AI-based systems that use machine learning models to analyze call transcripts, determine call attributes, and detect quality issues, substituting human supervisor auditing with intelligent automation
2Reliability
If more call audits are performed to improve quality control, then dispatch accuracy improves, but the time required for auditing increases
Solution Approach 1:
The system performs preliminary quality checks automatically during the call handling process itself, with AI models continuously monitoring for quality issues in real-time, so that audits are conducted as calls occur rather than requiring time-consuming retrospective reviews
Solution Approach 2:
The quality assurance process operates continuously and automatically throughout operational hours through AI models that constantly analyze call data, eliminating the need for periodic manual audit cycles and providing uninterrupted quality monitoring
3Reliability
If AI models continuously monitor all calls, then quality assurance coverage increases, but system complexity increases
Solution Approach 1:
The quality assurance system is segmented into multiple specialized AI models, each responsible for specific monitoring functions such as call attribute determination, CAD record timing verification, and emergency service dispatch validation, making the overall complex system manageable through functional decomposition
Solution Approach 2:
The AI models are designed with multi-functionality to handle various quality assurance tasks within a unified framework, where a single model can determine call attributes, assess timing compliance, and identify dispatch issues, reducing the need for separate specialized systems
Data Source
AI summary
Examples provide a public safety answering point server including a processor configured to determines that a call taker of the PSAP has received a call from a caller regarding an incident and generate a transcript of the call. Based on the transcript, the processor determines a set of call attributes associated with the call, and determines whether a computer automated dispatch (“CAD”) record associated with the call is created within a threshold time period from the start time of the call. When the CAD record associated with the call is not created within the threshold time period, the processor infers, using a first artificial intelligence model, whether a CAD record should be created. In response to inferring that a CAD record should be created based on the set of call attributes, the processor generates and transmits a quality alert to an electronic device associated with a user of the PSAP.