AI Neural Network for Medical Indication Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Healthcare providers face inefficiencies when selecting medical indications for orders due to the need to review extensive lists generated by clinical decision support mechanisms, which often do not consider all relevant patient information and require manual selection from numerous options.
Innovation Solution
An AI-assisted medical indication selection system using a multi-layer neural network and content similarity engine processes patient, provider, and order information to automatically generate and prioritize medical indications based on evidence-based guidelines, reducing the need for manual selection by providing a focused list of probable indications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a qualified decision support mechanism generates a list of proposed indications based on patient age and gender, then regulatory compliance is achieved, but the list is lengthy and requires manual search through many options
Solution Approach 1:
The system segments the indication selection process into two stages: first, a qualified decision support mechanism generates a comprehensive list of proposed indications for regulatory compliance; second, an AI engine processes this list along with additional patient information to identify and rank the most relevant indications. This segmentation allows both compliance and efficiency to be achieved.
Solution Approach 2:
The AI engine acts as an intermediary between the decision support mechanism's comprehensive indication list and the provider's need for a focused recommendation. It processes the lengthy list through neural network analysis, filtering and ranking indications based on multiple patient factors to present a condensed, prioritized set of options.
2Device complexity
If traditional decision support mechanisms use only patient age and gender, then the process is simple, but additional patient information is not considered
Solution Approach 1:
The AI engine is designed to process multiple types of patient information simultaneously - demographic data, clinical history, order details, and free-text reasons. This multi-functional capability allows the system to comprehensively evaluate all relevant factors without complicating the user interface or workflow.
Solution Approach 2:
The system performs preliminary processing of all patient information through the neural network before presenting indications to the provider. By pre-analyzing patient demographics, clinical history, order specifics, and free-text inputs, the AI engine prepares a ranked list of indications in advance, saving the provider time during the actual ordering process.
3Adaptability or versatility
If providers manually select from thousands of medical indications, then comprehensive selection is possible, but productivity and efficiency are reduced
Solution Approach 1:
Instead of presenting all thousands of possible indications, the system performs a partial action by identifying and ranking only the most relevant indications based on AI analysis. This excessive filtering ensures that the top recommendations are highly probable matches, allowing providers to make efficient selections without reviewing the entire indication database.
Solution Approach 2:
The system incorporates feedback loops where the AI engine continuously learns from provider selections and outcomes. By analyzing which indications are chosen and their success rates, the neural network refines its recommendations over time, improving both the comprehensiveness and efficiency of indication selection.
Data Source
AI summary
A method includes receiving first information associated with a patient, second information associated with a provider, and third information associated with an order, and determining, using a multi-layer neural network, a medical indication corresponding to the order responsive to receiving the first information, the second information, and the third information.