Addiction Prediction Tool for Standardized EHR Relapse Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional electronic health record (EHR) systems struggle to accurately predict addiction and relapse due to data incompatibilities across multiple institutions, informational overload, and traceability issues that can skew predictive model outputs, failing to provide timely and precise interventions.
Innovation Solution
A predictive model that accesses and standardizes input variables from disparate data sources, including EHR, prescription, academic, social media, and government records, to generate a likelihood of addiction or relapse score, with alerts and disassociation mechanisms to prevent prescription completion until acknowledged by caregivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional EHR systems record health data for patients over time, then data availability is improved, but the ability to distinguish addiction from normal variations deteriorates due to insufficient data analysis capability
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and analyzing patient data in real-time, calculating risk scores before critical events occur. The predictive model proactively identifies patterns indicating potential addiction or relapse, allowing early intervention before the patient reaches a crisis point.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing predicted risk scores against actual patient outcomes, refining the predictive model over time. Caregivers receive feedback through alerts and notifications that trigger based on risk thresholds, creating a closed-loop system that improves detection accuracy through iterative learning.
2Loss of information
If traditional EHR systems provide every piece of patient information to care providers, then information completeness is improved, but care provider analytical ability is overwhelmed by informational overload
Solution Approach 1:
The system extracts only the most critical and relevant information from the vast EHR database, presenting care providers with distilled insights rather than raw data volumes. Risk scores, key risk factors, and actionable alerts are extracted and highlighted, allowing providers to focus on what matters most without being overwhelmed by irrelevant information.
Solution Approach 2:
Different levels of information are provided to different users based on their needs and roles. Care providers receive customized dashboards showing locally relevant patient information, while the system maintains comprehensive data storage. Each user interface is tailored to display only the quality and quantity of information appropriate for that specific user's decision-making needs.
3Reliability
If data is made traceable in traditional predictive models, then model transparency is improved, but prediction accuracy deteriorates due to post-hoc rationalization and data alteration
Solution Approach 1:
The system segments the data architecture into distinct layers: an immutable data storage layer that maintains original records for regulatory compliance, and a processed analysis layer that performs real-time risk assessment. This segmentation allows traceability requirements to be met in the storage layer while preserving prediction accuracy in the analysis layer by preventing retroactive data manipulation.
Solution Approach 2:
An intermediary processing layer is introduced between data collection and prediction generation. This intermediary layer validates and standardizes incoming data, applying consistent transformation rules that prevent post-hoc rationalization while maintaining an audit trail. The intermediary acts as a gatekeeper that ensures data integrity without compromising the predictive model's ability to generate accurate results.
Data Source
AI summary
Technologies are provided for predicting an individual's likelihood of addiction or relapse to pharmaceuticals that include controlled or addictive substances. The prediction can be used to generate an alert that, in some aspects, prevents care provider actions in an electronic health record system that are related to the addictive pharmaceutical until the prediction is acknowledged. Further, the data used as input in the predictive modeling can be disassociated from the prediction after the prediction is generated.