Psychosis Risk Modeling via Mobile Communication and Mobility Data
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Solution Overview
Problem
Current methods for monitoring and detecting psychosis are often time and cost-intensive, fail to identify critical states of psychosis in a timely manner, leading to delayed or misdiagnoses, and lack a preventative approach, resulting in inadequate care for patients with psychotic disorders.
Innovation Solution
A method that analyzes communication and mobility data from mobile devices, combined with survey responses, to predict psychotic episode-risk states and generate alerts for early intervention, enabling proactive care and therapeutic interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current monitoring systems are used to detect psychosis, then patient outcomes can be influenced, but the systems are time and cost-intensive and fail to identify critical states timely
Solution Approach 1:
The system performs preliminary analysis of communication patterns, mobility data, and survey responses to establish baseline behavioral profiles before critical psychosis states develop. This enables early detection of deviations from normal patterns, allowing intervention before the patient enters a critical state, thus resolving the contradiction between reliable detection and timely identification.
Solution Approach 2:
The patent replaces intensive manual clinical assessment with automated computational analysis of digital footprints (communication logs, mobility patterns, survey data). This substitution enables continuous monitoring without the time and resource constraints of traditional methods, achieving both high detection reliability and timely identification of critical states.
2Reliability
If intensive patient assessment and monitoring are implemented, then early intervention is enabled, but time and cost resources are consumed
Solution Approach 1:
The system leverages data that patients already generate through their normal use of mobile devices and communication tools. By analyzing existing digital footprints rather than requiring dedicated monitoring equipment or intensive patient participation, the system enables early intervention capability without adding significant complexity to the patient's daily life or requiring complex monitoring infrastructure.
Solution Approach 2:
The patent uses multi-functional data sources (communication apps, mobility tracking, survey tools) that serve multiple purposes: they are part of normal patient life while simultaneously providing rich data for psychosis detection. This universality enables early intervention capability without requiring specialized complex monitoring systems, as existing everyday tools are repurposed for clinical monitoring.
3Measurement precision
If current detection standards are used, then diagnosis can be made, but delays and misdiagnoses occur due to reactionary approach
Solution Approach 1:
The system continuously monitors communication patterns, mobility behavior, and survey responses, providing real-time feedback on patient mental state. When deviations from baseline patterns indicate emerging psychosis, the system immediately alerts clinicians, enabling timely and accurate diagnosis. This continuous feedback loop eliminates the delays inherent in periodic or reactionary assessment approaches while maintaining high diagnostic precision through pattern recognition.
Data Source
AI summary
A method and system for modeling behavior and a psychotic disorder-related state of a patient, the method comprising: receiving a log of use dataset associated with communication behavior of the patient during a time period; receiving a supplementary dataset characterizing mobility-behavior of the patient during the time period; generating a predictive model based upon a passive dataset derived from the log of use dataset and the supplementary dataset; transforming at least one of the passive dataset and an output of the predictive model into an analysis of a psychotic episode-risk state of the individual associated with at least a portion of the time period; and upon detection that parameters of the psychotic episode-risk state satisfy at least one threshold condition, automatically initiating provision of a therapeutic intervention for the individual by way of at least one of the computing system and the mobile communication device.