Medical Device Demand Prediction System Using ML
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems fail to accurately predict medical device lending demand in advance, leading to inventory shortages, as existing technologies cannot effectively account for rapid increases in demand.
Innovation Solution
A prediction system utilizing a learned model that inputs electronic medical record data and lending record data to forecast demand, comparing the predicted demand with inventory levels and notifying the system when demand exceeds inventory, allowing for proactive inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demand prediction methods are used, then the system is simple to operate, but the prediction accuracy is insufficient leading to inventory shortages
Solution Approach 1:
The system performs preliminary machine learning model training using historical lending record data and electronic chart data before actual demand prediction. This preliminary action enables the system to establish predictive patterns in advance, improving prediction accuracy while maintaining operational simplicity during actual use.
Solution Approach 2:
A machine learning model serves as an intermediary between raw data (lending records and electronic charts) and demand prediction results. This intermediary component processes complex data relationships automatically, achieving high prediction accuracy without requiring complex manual analysis procedures.
2Reliability
If real-time demand monitoring is implemented, then inventory shortage prevention is improved, but the response time for prediction is increased
Solution Approach 1:
The system implements periodic demand prediction at predetermined intervals rather than continuous real-time monitoring. This periodic approach maintains reliable inventory management by predicting demand at optimal intervals, preventing inventory shortages while avoiding the time loss associated with continuous real-time processing.
Data Source
AI summary
The prediction system stores a learned model that is machine-learned so as to output a demand prediction result that is a prediction result of the demand of the medical device by inputting electronic medical record data in which information indicating the necessity of use of the medical device is described, using learning data including loan result data indicating a result of lending the medical device and electronic medical record data in which information indicating the necessity of use of the medical device is described. The prediction system acquires a demand prediction result by inputting the electronic medical record data into the learned model, and inputs an inventory prediction result which is a prediction result of the inventory of the medical device, compares the acquired demand prediction result with the input inventory prediction result, and notifies the medical device lending system when the demand exceeds the inventory.