PSAP Scripted Intake for Emergency Communication Triage
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Solution Overview
Problem
PSAPs face challenges in managing non-emergency and irrelevant communications, which divert operator attention from genuine emergencies, due to understaffing and increasing volumes of machine-generated messages.
Innovation Solution
A communication analysis engine intercepts incoming communications, classifies them as emergency, non-emergency, or irrelevant, and engages in scripted dialogues to gather additional details, using AI and machine learning to prioritize and triage messages effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If PSAP operators manually handle all incoming communications, then complete information gathering is achieved, but operator workload increases and response time to genuine emergencies decreases
Solution Approach 1:
The patent introduces an automated communication analysis engine as an intermediary between incoming communications and human operators. This engine pre-processes messages, extracts relevant information, and filters communications before they reach operators, thereby maintaining information completeness while reducing operator workload and accelerating emergency response.
Solution Approach 2:
The system performs preliminary actions by automatically analyzing incoming communications, extracting key information, and categorizing messages before human operators review them. This pre-processing includes identifying emergency vs. non-emergency communications, gathering initial details through automated dialogue, and prioritizing messages, which reduces the time operators need to spend on each communication.
2Productivity
If PSAP operators focus only on emergency communications, then response efficiency improves, but non-emergency communications may be mishandled or ignored
Solution Approach 1:
The automated analysis engine acts as an intermediary that reliably handles non-emergency communications by automatically categorizing, filtering, and managing them separately. This allows operators to focus on emergencies while the system ensures non-emergency messages are not lost or mishandled through automated routing and tracking.
Solution Approach 2:
The system applies different processing qualities to different communication types: emergency communications receive priority handling with full operator attention, while non-emergency communications receive automated processing with appropriate filtering and routing. This local differentiation ensures each type of communication is handled with the appropriate level of human involvement.
3Ease of operation
If automated systems are introduced to filter communications, then operator workload decreases, but system complexity increases
Solution Approach 1:
The patent introduces an automated communication analysis engine as an intermediary between incoming communications and human operators. This engine pre-processes messages, extracts relevant information, and filters communications before they reach operators, thereby maintaining information completeness while reducing operator workload and accelerating emergency response.
Solution Approach 2:
The system incorporates self-service capabilities where the automated analysis engine independently performs communication filtering, information extraction, and categorization without requiring complex human intervention. The engine uses machine learning models and natural language processing to autonomously manage the triage process, reducing the need for additional human resources while maintaining simplicity in operational procedures.
Data Source
AI summary
Particular example embodiments described herein can provide for a system, an apparatus, and a method for intercepting a communication to a public safety answering point (PSAP) before the communication reaches a human operator at the PSAP, using a computer model to execute a script and communicate one or more questions from the script to a user associated with the communication to the PSAP, receiving at least one response to the one or more questions, and sending the communication, the one or more questions, and the at least one response to the human operator at the PSAP. In some examples, the computer model is a chat bot.