US-20260124379-A1 - CUSTOMIZED PATIENT INSTRUCTION USING AN INTELLIGENT INJECTION DEVICE PLATFORM

US20260124379A1US 20260124379 A1US20260124379 A1US 20260124379A1US-20260124379-A1

Abstract

Methods and systems for customized patient instruction using an intelligent injection device platform are disclosed. A device may include a digital twin module configured to create digital representations of patients based on collected patient parameters. A device may include a device characterization system configured to model intelligent injection device characteristics including medication type, device configuration, injection requirements, and operational complexity. A device may include a compatibility analysis engine configured to simulate patient interactions with specific intelligent injection devices using the digital twin representations. A device may include a suitability assessment module configured to determine appropriateness of device administration based on predicted patient-device interactions, wherein the digital twin module combines data from multiple sensors to model patient features and device environment characteristics, and wherein the system generates compatibility scores indicating whether a patient can safely and effectively self-administer medication using a specific intelligent injection device.

Inventors

Jeremy Corbett

Assignees

DATADOSE, LLC

Dates

Publication Date: 20260507
Application Date: 20250829

Claims (20)

1 . A digital twin simulation system for intelligent injection devices comprising: a digital twin module configured to create digital representations of patients based on collected patient parameters including physical characteristics, cognitive abilities, and medical conditions; a device characterization system configured to model intelligent injection device characteristics including medication type, device configuration, injection requirements, and operational complexity; a compatibility analysis engine configured to simulate patient interactions with specific intelligent injection devices using the digital twin representations; and a suitability assessment module configured to determine appropriateness of device administration based on predicted patient-device interactions, wherein the digital twin module combines data from multiple sensors to model patient features and device environment characteristics, and wherein the system generates compatibility scores indicating whether a patient can safely and effectively self-administer medication using a specific intelligent injection device.
2 . The system of claim 1 , wherein the digital twin module is configured to provide access to and manage a library of digital twins representing a plurality of intelligent injection device types, and wherein artificial intelligence modules request specific digital twins to train machine-learned models for dosing characteristics of specific intelligent injection devices.
3 . The system of claim 1 , wherein the device characterization system utilizes digital twins that combine data from a plurality of sensors to model at least one feature of an intelligent injection device or its environment to inform predictions regarding device status, condition, operation, utilization or maintenance.
4 . The system of claim 1 , wherein the compatibility analysis engine includes context-adaptive digital twins that digitally represent parameters of entities or workflows involved in at least one of prescription, delivery, administration or reporting of medication dosage, wherein the configuration of the digital twin is automatically adapted based on relevant contextual data.
5 . The system of claim 1 , wherein the digital twin module includes digital twins configured to simulate at least one process of an intelligent injection device environment or medication dosage administration environment to inform determinations of at least one of process performance, device condition or patient outcome.
6 . The system of claim 1 , wherein the compatibility analysis engine includes digital twins with embedded AI chatbot experts trained to interact with users to provide information about intelligent injection devices or medical dosage administration.
7 . The system of claim 1 , wherein the digital twin module includes digital twins that represent intelligent devices, medication dosage environments, and patient physical characteristics to customize a treatment plan to an individual patient’s needs.
8 . The system of claim 1 , wherein the compatibility analysis engine includes digital twins that represent intelligent devices, medication dosage environments, and drug interaction effects to model potential medication interactions.
9 . The system of claim 1 , wherein the digital twin module receives real-time data from sensor systems from a plurality of intelligent injection devices and sensor systems of the physical environment in which devices operate.
10 . The system of claim 1 , wherein the device characterization system includes digital twin data representing features, states, or characteristics of specific intelligent injection devices containing specific medications.
11 . An artificial intelligence training platform for personalized healthcare recommendations comprising: a neural network system trained on training datasets of medical dosage administration data to generate personalized health suggestions; a sensor fusion module configured to combine data from multiple sources including intelligent injection devices, wearable devices, and patient monitoring systems for enhanced training; a recommendation engine that learns from patient similarities and treatment outcomes to generate personalized recommendations; and a compliance scoring system that trains artificial intelligence modules to calculate and improve compliance scores based on injection event conformance to treatment schedules, wherein the artificial intelligence module continuously learns from patient responses to optimize future medication combinations and administration timing, and wherein the system uses reinforcement learning to improve recommendation quality based on patient adherence and outcome feedback.
12 . The platform of claim 11 , wherein the neural network system is trained on training datasets including at least one of intelligent injection device design, configuration, operation and outcome data to output predictions, classifications, recommendations, analytic results, reports or control instructions.
13 . The platform of claim 11 , wherein the sensor fusion module integrates data from multiple intelligent injection devices with IoT devices or wearable devices.
14 . The platform of claim 11 , wherein the recommendation engine utilizes machine learning systems trained on medical dosage administration data to generate a personalized health suggestion based on a patient similarity and historical treatment outcomes across multiple intelligent injection devices.
15 . The platform of claim 11 , wherein the compliance scoring system analyzes administration data from multiple intelligent injection devices to calculate adherence scores based on conformance to treatment schedules and categorizes injection events as compliant, non-compliant or indeterminate.
16 . The platform of claim 11 , wherein the neural network system performs sensor fusion to combine temperature sensor data from multiple intelligent injection devices with patient monitoring data to optimize a medication storage recommendation.
17 . The platform of claim 11 , wherein the sensor fusion module integrates custody tracking and supply chain management data from multiple intelligent injection devices to detect fraud.
18 . The platform of claim 11 , wherein the recommendation engine performs predictive analytics on administration data from multiple intelligent injection devices to forecast optimal medication timing and dosage adjustment based on an individual patient response pattern.
19 . The platform of claim 11 , wherein the compliance scoring system generates an automated alert and categorization summary based on analysis across multiple intelligent injection devices to facilitate real-time clinical decision support.
20 . The platform of claim 11 , wherein the recommendation engine analyzes supply and demand patterns across multiple intelligent injection devices and locations to generate a medication inventory recommendation.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part of U.S. patent application no. 19/281,005, filed on July 25, 2025, which is a continuation of U.S. patent application no. 19/031,862, filed on January 18, 2025, which is a continuation of U.S. patent application no. 19/030,829, filed on January 17, 2025, which claims the benefit of U.S. provisional application no. 63/715,505, filed on November 1, 2024, which are all hereby incorporated by reference in their entirety. FIELD The present disclosure relates generally to a management platform for injection devices, such as syringes, and, more particularly, to data reporting regarding administration completion of an injectable pharmaceutical. BACKGROUND Understanding administration of drugs and their interaction in the healthcare ecosystem are important in delivering beneficial patient care. Applicant appreciates that regular pharmaceutical therapy and a need for real time data management related to both timing of and compliance in dosing bundled with increased information sharing across the care continuum can provide valuable insight for improving care and, in turn, reducing fraud, waste, and abuse. SUMMARY In some aspects, the techniques described herein relate to a digital twin simulation system for intelligent injection devices including: a digital twin module configured to create digital representations of patients based on collected patient parameters including physical characteristics, cognitive abilities, and medical conditions; a device characterization system configured to model intelligent injection device characteristics including medication type, device configuration, injection requirements, and operational complexity; a compatibility analysis engine configured to simulate patient interactions with specific intelligent injection devices using the digital twin representations; a suitability assessment module configured to determine appropriateness of device administration based on predicted patient-device interactions, wherein the digital twin module combines data from multiple sensors to model patient features and device environment characteristics, and wherein the system generates compatibility scores indicating whether a patient can safely and effectively self-administer medication using a specific intelligent injection device. In some aspects, the techniques described herein relate to a system, wherein the digital twin module is configured to provide access to and manage a library of digital twins representing a plurality of intelligent injection device types, and wherein artificial intelligence modules request specific digital twins to train machine-learned models for dosing characteristics of specific intelligent injection devices. In some aspects, the techniques described herein relate to a system, wherein the device characterization system utilizes digital twins that combine data from a plurality of sensors to model at least one feature of an intelligent injection device or its environment to inform predictions regarding device status, condition, operation, utilization or maintenance. In some aspects, the techniques described herein relate to a system, wherein the compatibility analysis engine includes context-adaptive digital twins that digitally represent parameters of entities or workflows involved in at least one of prescription, delivery, administration or reporting of medication dosage, wherein the configuration of the digital twin is automatically adapted based on relevant contextual data. In some aspects, the techniques described herein relate to a system, wherein the digital twin module includes digital twins configured to simulate at least one process of an intelligent injection device environment or medication dosage administration environment to inform determinations of at least one of process performance, device condition or patient outcome. In some aspects, the techniques described herein relate to a system, wherein the compatibility analysis engine includes digital twins with embedded AI chatbot experts trained to interact with users to provide information about intelligent injection devices or medical dosage administration. In some aspects, the techniques described herein relate to a system, wherein the digital twin module includes digital twins that represent intelligent devices, medication dosage environments, and patient physical characteristics to customize a treatment plan to an individual patient's needs. In some aspects, the techniques described herein relate to a system, wherein the compatibility analysis engine includes digital twins that represent intelligent devices, medication dosage environments, and drug interaction effects to model potential medication interactions. In some aspects, the techniques described herein relate to a system, wherein the digital twin module receives real-time data from sensor systems from a plurality of intelligent injection devices and sensor systems of th