Emergency Call Alerting With Context-Based Data Release
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Solution Overview
Problem
Call takers at PSAPs face challenges in efficiently acquiring and utilizing relevant information from callers during emergencies, as existing systems often provide unnecessary or distracting data, and there is a need for improved situational awareness while maintaining caller privacy.
Innovation Solution
An emergency call center server with an electronic processor and computer-readable medium that performs speech recognition, identifies callers, and selectively releases contextually relevant user data and emergency contact notifications based on predefined trigger conditions, ensuring only necessary information is shared with first responders and contacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all user data from caller profiles is provided to call takers, then completeness of information is improved, but information overload and distractions increase
Solution Approach 1:
The system segments user profile data into multiple categories (medical information, emergency contacts, location data, preferences) and selectively releases only the relevant segments based on the incident type, preventing information overload while maintaining completeness of necessary data
Solution Approach 2:
The system applies local quality by providing different subsets of user data to different call takers based on the specific incident context, ensuring each call taker receives the appropriate level and type of information detail rather than a uniform data set
2Loss of information
If contextual information is selectively provided based on incident type, then relevance of information is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-categorizing user profile data into different types and pre-defining release rules for each incident category, so that during an actual emergency call, the system can quickly retrieve and release appropriate data without complex real-time decision-making
Solution Approach 2:
The system introduces an intermediary component (the selective release mechanism) that sits between the user profile database and the call taker interface, automatically filtering and translating raw user data into contextually relevant information based on incident type, thereby simplifying the overall system architecture
3Reliability
If caller privacy is maintained by restricting data access, then privacy protection is improved, but availability of necessary information decreases
Solution Approach 1:
The system applies dynamics by making data accessibility dynamic rather than static - user data is released or restricted based on the specific incident context and caller preferences, allowing the system to adapt between privacy protection and information availability depending on the situation
Data Source
AI summary
Examples provide an emergency call center server including an electronic processor configured to determine that an emergency service provider has received a call from a caller regarding an incident, identify the caller based on the received call, and access a user profile associated with the caller from a database. The user profile includes a set of user data associated with the caller and a set of contextual trigger conditions defining types and/or locations of incidents. Based on the received call, the electronic processor determines satisfied ones of the set of contextual trigger conditions, selectively releases, to the emergency service provider, a first subset of user data that is associated with one or more satisfied contextual trigger conditions, and prevents release of a second subset of user data that is associated with one or more unsatisfied contextual trigger conditions.