Computer-Aided Escalation System for Digital Health Platforms
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Solution Overview
Problem
Current digital health platforms face challenges in providing timely and appropriate care to participants, particularly those with mental health conditions, as they scale, leading to potential oversight and detrimental effects due to the complexity of maintaining adequate care levels remotely.
Innovation Solution
A computer-aided escalation system and method that automatically detects the need for care adjustments, including escalation to higher levels of care or de-escalation, using a combination of rule-based and trained models to analyze participant inputs and historical data, facilitating timely interventions and workload distribution among care providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If remote digital health platforms scale to serve more participants, then accessibility and convenience are improved, but the ability to maintain appropriate levels of care for each individual deteriorates
Solution Approach 1:
The system enables automated self-monitoring and self-reporting by participants through digital interfaces, allowing the platform to collect care-relevant data without requiring proportional increases in human caregiver resources. This maintains care quality consistency while enabling platform scaling.
Solution Approach 2:
The system implements automated feedback loops where participant data is continuously monitored, analyzed, and used to trigger appropriate care responses. This automated feedback mechanism ensures consistent care quality across scaled operations without requiring proportional increases in human oversight.
2Ease of operation
If manual monitoring of participant care needs is used, then personalized care attention is improved, but the timeliness and consistency of care adjustments deteriorates due to human oversight limitations
Solution Approach 1:
The system replaces manual human monitoring and assessment with automated computational algorithms that continuously analyze participant data. This substitution eliminates delays associated with human review while maintaining personalized attention through algorithmic customization of care recommendations.
Solution Approach 2:
The system implements continuous automated monitoring and analysis of participant data without interruption or delay. This continuous action ensures that care adjustments are triggered immediately when needed, eliminating the time losses inherent in periodic manual reviews.
3Loss of time
If automated systems are implemented to detect care needs, then care adjustment timeliness is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the automated care detection process into distinct modular components: data collection modules, analysis modules, and response trigger modules. This segmentation manages complexity by organizing functions into independent, manageable units that can be developed and maintained separately.
Solution Approach 2:
The system employs universal algorithms and data structures that handle multiple types of participant data and care scenarios through a single integrated framework. This universality reduces overall system complexity by avoiding the need for separate specialized systems for different care situations.
Data Source
AI summary
A system for computer-aided escalation can include and/or interface with any or all of: a set of user interfaces (equivalently referred to herein as dashboards and/or hubs), a computing system, and a set of models. A method for computer-aided escalation includes any or all of: receiving a set of inputs; and processing the set of inputs to determine a set of outputs; triggering an action based on the set of outputs; and/or any other processes.