Mobile App Communication Data for Adherence Prediction
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Solution Overview
Problem
Current methods for monitoring patient health and treatment adherence rely heavily on manual data entry and lack precision, especially in predicting health outcomes and treatment efficacy, which can lead to inefficiencies and suboptimal care.
Innovation Solution
The development of methods that utilize patient communication data from mobile devices to create models predicting treatment adherence and health status, allowing for automated notifications and interventions based on analyzed behavior patterns and survey responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data entry methods are used for monitoring patient health and treatment adherence, then the system is simple to implement, but the measurement precision and reliability of health outcome predictions deteriorate
Solution Approach 1:
The system enables patients to self-report health data and treatment adherence through mobile device surveys, eliminating the need for manual data entry by healthcare providers. Patients automatically input their own information, which improves data precision while reducing system complexity.
Solution Approach 2:
The patent replaces manual mechanical data entry processes with automated electronic data collection through mobile applications. The system uses software-based survey administration and automated data capture to substitute human manual input, thereby improving precision without significantly increasing complexity.
2Measurement precision
If passive data collection from mobile devices is implemented, then the measurement precision of behavior pattern analysis improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The system introduces a mobile device as an intermediary between the patient and the healthcare system. The mobile application collects passive data locally and transmits it to the server, acting as a mediator that simplifies the overall system architecture while enabling precise behavior pattern analysis through structured data collection.
Solution Approach 2:
The patent segments the data collection and processing system into distinct components: mobile device data collection, server-side data storage, and model-based analysis. This segmentation allows each component to be optimized independently, improving prediction precision while managing complexity through modular architecture.
3Productivity
If automated notifications and interventions are implemented based on analyzed behavior patterns, then the productivity of patient care improves, but the device complexity and algorithm requirements increase
Solution Approach 1:
The system implements automated feedback loops where patient behavior data is continuously analyzed and used to generate timely notifications and interventions. The model compares observed behavior patterns against expected patterns and automatically triggers appropriate responses, improving care productivity through automated decision-support without requiring complex real-time processing.
Data Source
AI summary
One method for supporting a patient through a treatment regimen includes: accessing a log of use of a native communication application executing on a mobile computing device by a patient; selecting a subgroup of a patient population based on the log of use of the native communication application and a communication behavior common to the subgroup; retrieving a regimen adherence model associated with the subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and communication behavior for patients within the subgroup; predicting patient adherence to the treatment regimen based on the log of use of the native communication application and the regimen adherence model; and presenting a treatment-related notification based on the patient adherence through the mobile computing device.