Adaptive Clinical Order Template Optimization
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Solution Overview
Problem
Existing Clinical Information Systems (CIS) lack efficient methods to identify and update treatment order templates, leading to sub-optimal changes based on incomplete and subjective criteria, resulting in unnecessary template size and prolonged data entry tasks due to unused items.
Innovation Solution
A system that continuously updates treatment ordering templates by analyzing usage statistics to automatically add, delete, or modify items, using a weighted Receiver Operating Characteristic (ROC) to identify which items to include or remove, thereby optimizing template content and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If templates contain more items to ensure all possible data is available, then completeness of data is improved, but template size increases and data entry time increases
Solution Approach 1:
The patent implements dynamic template adjustment by automatically adding or removing items from templates based on real-time usage statistics. The system monitors how frequently items are selected or deselected by users and dynamically modifies template composition to include only the most relevant items for each clinical scenario, making the template adaptable rather than static.
Solution Approach 2:
The system changes the parameter of template composition by using usage statistics (selection frequency, deselection frequency, add frequency, remove frequency) to determine which items should be included or excluded. This data-driven approach transforms template parameters based on actual user behavior patterns.
2Ease of manufacture
If templates are updated based on subjective criteria or anecdotal evidence, then ease of implementation is improved, but template optimization effectiveness deteriorates
Solution Approach 1:
The system implements continuous feedback loops by monitoring user interactions with template items and using this data to automatically update templates. Usage statistics are collected, analyzed, and fed back into the template generation process, creating a self-improving system that objectively optimizes templates based on actual usage patterns rather than subjective judgment.
Solution Approach 2:
The system performs self-service by automatically updating templates based on its own collected usage data without requiring manual intervention. The automated template update mechanism analyzes usage statistics and independently determines which items to add or remove, eliminating the need for manual template optimization.
3Adaptability or versatility
If the number of templates is increased to provide more specialized options, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal template generation system that creates specialized templates dynamically based on clinical scenarios and usage patterns. Instead of maintaining separate static templates for each scenario, the system uses a single adaptive mechanism that generates appropriate templates on-demand, reducing overall system complexity while maintaining high adaptability.
Data Source
AI summary
A system continuously improves the sensitivity, specificity, precision, and accuracy of treatment ordering templates. A repository of information comprises multiple candidate order sets individually including multiple candidate items for order and associated corresponding related order parameters. An individual item for order is associated with multiple related order parameters. A data entry monitor monitors user selection of candidate items from a candidate order set and records candidate item usage data identifying items selected by a user for order from individual particular candidate order sets for multiple different candidate order sets. A data processor determines from the candidate item usage data at least one of, (a) data indicative of the number or proportion of candidate items of a particular candidate order set that were selected by a user during order entry and (b) data indicative of the number or proportion of candidate items of a particular candidate order set that were not selected by a user during order entry.