Injection Dose Determination for Priming and Device Changes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Patients using medication delivery devices like insulin pens face challenges in accurately tracking and distinguishing between actual injection doses and priming doses, leading to potential human error and incorrect dose logs, especially when switching devices or cartridges.
Innovation Solution
A computerized method to analyze injection event groups using algorithms that differentiate between actual doses and priming doses, accounting for device changes, by grouping events within a time window and applying specific detection algorithms based on the presence or absence of such changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patients manually track each injection and dose size, then they can monitor their medication usage, but it becomes cumbersome, tedious, and prone to human error
Solution Approach 1:
The medication delivery device automatically tracks and records injection events, dose sizes, and timing without requiring patient intervention. The device's processor detects injection events through sensors and automatically logs them, eliminating the manual tracking burden while maintaining accurate records of all administered doses.
Solution Approach 2:
The patent replaces manual mechanical tracking methods with an electronic detection and recording system. Sensors detect injection events and the processor automatically records dose information, substituting the manual writing or memory-based tracking system with an automated electronic logging system that reduces errors and improves reliability.
2Loss of information
If the device records all injection events including priming events, then it captures complete data, but it includes non-events that lead to incorrect dose logs
Solution Approach 1:
The patent segments injection events into different categories by analyzing temporal patterns and dose characteristics. The processor identifies clusters of injection events within time windows and distinguishes between priming events (typically smaller doses occurring in rapid succession) and actual therapeutic injections, allowing accurate classification and separate logging of each type.
Solution Approach 2:
The device initially records all injection events including priming events, then applies post-processing algorithms to filter and classify them. This approach ensures no potential dose events are missed initially, then uses analysis of dose size, timing patterns, and event clustering to identify and exclude priming events from the final dose log, maintaining both completeness and accuracy.
3Device complexity
If the device uses a single dose detection algorithm, then it simplifies processing, but it cannot accurately distinguish priming events from actual doses or handle device changes
Solution Approach 1:
The patent implements a dynamic dose detection system that adapts its algorithm based on detected conditions. The processor monitors injection patterns and automatically switches between different detection algorithms depending on whether priming events are detected or whether a device change has occurred, allowing the system to optimize its detection approach for each specific situation rather than using a fixed single algorithm.
Solution Approach 2:
The system changes detection parameters and algorithm selection based on the state of the device and injection patterns. When device changes are detected (through sensors or user input), the system adjusts its dose detection parameters and algorithm choice to account for the new device characteristics, ensuring accurate dose measurement across different devices and conditions.
4Reliability
If the device groups injection events within a time window, then it can identify priming events, but it requires complex analysis to distinguish actual doses
Solution Approach 1:
The patent performs preliminary grouping of injection events into time-based clusters before detailed analysis. By organizing events into temporal groups first, the system establishes a structured framework that simplifies subsequent identification of priming events versus actual doses, reducing the complexity of the overall analysis process while maintaining reliable event classification.
Data Source
AI summary
The techniques described herein relate to computerized methods and apparatus for determining a dose injected into a user by one or more medication delivery devices. Data is accessed that is indicative of an injection event group that includes one or more injection events associated with one or more medication delivery devices. The injection event group is processed to determine the dose injected into the user based on one or more of whether the injection event is associated with a medication delivery device change, a dose size of one or more of the injection events, a position of one or more of the injection events in the injection event group, and/or a time period between one or more injection events to a next injection event in the injection event group.