Clinical Workflow Respiratory Depression Prediction System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional computerized clinical workflows do not autonomously predict medication-induced respiratory depression in real-time, making it difficult to anticipate and mitigate adverse medical events during patient treatment.
Innovation Solution
A method and system that query electronic patient records for predefined data elements associated with medication-induced respiratory depression risks, automatically calculating a total element value and generating notices and intervention orders when thresholds are met, allowing for real-time prediction and intervention within clinical workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computerized clinical workflows are used, then ease of operation is maintained, but reliability of predicting medication-induced respiratory depression deteriorates
Solution Approach 1:
The system segments the prediction task into discrete data elements (e.g., patient demographics, medication orders, lab results) that can be independently queried and evaluated. Each data element contributes to an aggregate risk score, allowing the complex prediction problem to be broken down into manageable components that integrate with existing clinical workflows.
Solution Approach 2:
The system performs preliminary risk assessment by continuously querying electronic health records for relevant data elements before adverse events occur. By calculating aggregate values from multiple data sources in advance and generating predictions proactively, the system enables early intervention while maintaining compatibility with existing clinical processes.
2Reliability
If real-time prediction is implemented, then reliability of adverse event prediction is improved, but loss of time in clinical workflows increases
Solution Approach 1:
The system operates autonomously by automatically querying electronic health records, calculating risk scores from multiple data elements, and generating predictions without requiring manual clinician input. The system self-manages the entire prediction process, including data retrieval, aggregation, and notification, thereby eliminating time losses associated with manual assessment while maintaining real-time prediction capability.
3Productivity
If manual assessment of respiratory depression risk is used, then device complexity is reduced, but productivity of clinical workflows deteriorates
Solution Approach 1:
The system replaces manual clinician assessment with automated computational analysis. Instead of manually reviewing patient records and calculating risk scores, the system uses computerized algorithms to automatically query electronic health records, aggregate data elements, and generate predictions. This substitution of manual mechanical processes with automated computational processes significantly improves workflow efficiency while maintaining high automation levels.
Data Source
AI summary
Methods, systems, and computer-readable media are disclosed herein for predicting medication induced respiratory depression in patients. Herein, when indications are received for particular events being logged to a computerized clinical workflow, a query of patient's clinical record(s) is preformed to locate predetermined factors that are associated with medication induced respiratory depression. An overall value is calculated to stratify the possibility of adverse respiratory events for the patient. Warnings, detailed intervention instructions, and complete medical orders can be automatically generated and provided to a clinician and/or automatically input to the clinical workflow of the patient for performance.