US-20260124391-A1 - INTELLIGENT DOSING PLATFORM WITH ADVANCED PREDICTIVE ANALYTICS AND FORECASTING CAPABILITIES FOR INJECTABLE MEDICATION MANAGEMENT

US20260124391A1US 20260124391 A1US20260124391 A1US 20260124391A1US-20260124391-A1

Abstract

An intelligent dosing platform incorporating comprehensive predictive analytics and forecasting capabilities for injectable medication administration. The platform includes predictive analytics modules configured to generate forecasts for financial costs, medication demand patterns, and adverse event probabilities using machine learning algorithms and statistical modeling techniques. The system features financial modeling modules that analyze cost patterns associated with medication procurement, administration infrastructure, and insurance reimbursements, while demand forecasting modules predict medication needs based on seasonal trends, patient population changes, and treatment protocol modifications. Adverse event prediction modules identify risk factors using patient risk profiles and clinical outcome histories. The platform incorporates machine learning capabilities that continuously improve prediction accuracy using patient outcome data and administration patterns. Comprehensive reporting modules generate customized predictive reports for healthcare administrators, pharmaceutical suppliers, and insurance providers, while optimization modules recommend actions for cost reduction and patient safety improvement based on predictive insights.

Inventors

Jeremy Corbett

Assignees

DATADOSE, LLC

Dates

Publication Date: 20260507
Application Date: 20251029

Claims (20)

1 . A prediction and forecasting system for injectable medication administration, comprising: a predictive analytics module configured to generate forecasts for one or more of financial costs, medication demand, and adverse event probabilities; a data analysis module configured to process historical administration data to identify patterns related to one or more of cost trends, supply requirements, and safety indicators; and a recommendation engine configured to provide guidance based on one or more of financial projections, demand predictions, and adverse event risk assessments.
2 . The prediction and forecasting of claim 1 , wherein the predictive analytics module is configured to utilize one or more of machine learning algorithms, statistical modeling techniques, and artificial intelligence methods for generating forecasts.
3 . The prediction and forecasting of claim 1 , wherein the data analysis module is configured to process data including one or more of patient demographics, medication histories, administration patterns, and healthcare utilization metrics.
4 . The prediction and forecasting of claim 1 , wherein the recommendation engine is configured to generate actionable insights for one or more of budget planning, inventory management, and clinical risk mitigation.
5 . The prediction and forecasting of claim 1 , further comprising a financial modeling module configured to analyze cost patterns associated with one or more of medication procurement, administration infrastructure, and patient care delivery.
6 . The prediction and forecasting of claim 1 , further comprising a demand forecasting module configured to predict medication needs based on one or more of seasonal trends, patient population growth, and treatment protocol changes.
7 . The prediction and forecasting of claim 1 , further comprising an adverse event prediction module configured to identify risk factors using one or more of patient risk profiles, medication interaction databases, and clinical outcome histories.
8 . The prediction and forecasting of claim 1 , wherein the predictive analytics module is configured to generate forecasts across one or more of short-term planning horizons, medium-term strategic planning, and long-term trend analysis.
9 . The prediction and forecasting of claim 1 , further comprising a reporting module configured to generate predictive reports for one or more of healthcare administrators, pharmaceutical suppliers, and insurance providers.
10 . A comprehensive prediction and forecasting platform for injectable medication management, comprising: a predictive analytics module configured to generate forecasts for one or more of financial costs, medication demand, and adverse event probabilities; a data analysis module configured to process historical administration data to identify patterns related to one or more of cost trends, supply requirements, and safety indicators; a recommendation engine configured to provide guidance based on one or more of financial projections, demand predictions, and adverse event risk assessments; a financial modeling module configured to predict costs associated with one or more of medication procurement, administration overhead, and insurance reimbursements; a demand forecasting module configured to anticipate medication needs based on one or more of patient population trends, seasonal variations, and treatment protocol changes; an adverse event prediction module configured to identify risk factors for one or more of medication side effects, administration complications, and treatment failures; a machine learning module configured to continuously improve prediction accuracy using one or more of patient outcome data, administration patterns, and environmental factors; and a reporting module configured to generate predictive reports for one or more of healthcare administrators, pharmaceutical suppliers, and insurance providers.
11 . The comprehensive prediction and forecasting platform of claim 10 , wherein the financial modeling module is configured to analyze cost factors including one or more of medication unit costs, storage expenses, administration labor costs, and waste reduction opportunities.
12 . The comprehensive prediction and forecasting platform of claim 10 , wherein the demand forecasting module is configured to incorporate data from one or more of epidemiological trends, demographic shifts, and healthcare policy changes.
13 . The comprehensive prediction and forecasting platform of claim 10 , wherein the adverse event prediction module is configured to utilize data sources including one or more of clinical trials databases, post-market surveillance reports, and real-world evidence studies.
14 . The comprehensive prediction and forecasting platform of claim 10 , wherein the machine learning module is configured to employ one or more of neural networks, decision trees, regression analysis, and ensemble methods for prediction improvement.
15 . The comprehensive prediction and forecasting platform of claim 10 , wherein the reporting module is configured to generate customized reports based on one or more of stakeholder roles, decision-making timeframes, and organizational priorities.
16 . The comprehensive prediction and forecasting platform of claim 10 , further comprising a risk assessment module configured to evaluate prediction confidence levels and identify one or more of uncertainty factors, data quality issues, and model limitations.
17 . The comprehensive prediction and forecasting platform of claim 10 , further comprising an optimization module configured to recommend actions for one or more of cost reduction, supply chain efficiency, and patient safety improvement based on predictive insights.
18 . The comprehensive prediction and forecasting platform of claim 10 , wherein the predictive analytics module is configured to integrate with one or more of electronic health record systems, pharmacy management platforms, and financial management systems for real-time data access.
19 . The comprehensive prediction and forecasting platform of claim 10 , further comprising an alert system configured to notify stakeholders of one or more of significant forecast changes, threshold breaches, and emerging risk patterns requiring immediate attention.
20 . A computer-implemented method for prediction and forecasting in injectable medication administration, comprising: collecting, via a data analysis module, historical administration data including one or more of cost trends, supply requirements, and safety indicators; processing, via the data analysis module, the historical data to identify patterns related to one or more of financial performance, medication utilization, and adverse event frequencies; generating, via a predictive analytics module, forecasts for one or more of financial costs, medication demand, and adverse event probabilities using the identified patterns; modeling, via a financial modeling module, costs associated with one or more of medication procurement, administration overhead, and insurance reimbursements; forecasting, via a demand forecasting module, medication needs based on one or more of patient population trends, seasonal variations, and treatment protocol changes; predicting, via an adverse event prediction module, risk factors for one or more of medication side effects, administration complications, and treatment failures; improving, via a machine learning module, prediction accuracy using one or more of patient outcome data, administration patterns, and environmental factors; providing, via a recommendation engine, guidance based on one or more of financial projections, demand predictions, and adverse event risk assessments; generating, via a reporting module, predictive reports for one or more of healthcare administrators, pharmaceutical suppliers, and insurance providers; assessing, via a risk assessment module, prediction confidence levels and identifying one or more of uncertainty factors, data quality issues, and model limitations; optimizing, via an optimization module, recommendations for one or more of cost reduction, supply chain efficiency, and patient safety improvement; and alerting, via an alert system, stakeholders of one or more of significant forecast changes, threshold breaches, and emerging risk patterns requiring immediate attention.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of International Application No. PCT/US2025/053140, filed on Oct. 29, 2025, which is a continuation of U.S. patent application Ser. No. 19/373,017, filed on Oct. 29, 2025, which is a continuation-in-part of U.S. patent application Ser. No. 19/314,854, filed on Aug. 29, 2025, which is a continuation-in-part of U.S. patent application Ser. No. 19/281,005, filed on Jul. 25, 2025, which is a continuation of U.S. patent application Ser. No. 19/031,862, filed on Jan. 18, 2025, which is a continuation of U.S. patent application Ser. No. 19/030,829, filed on Jan. 17, 2025, which claims the benefit of U.S. provisional application No. 63/715,505, filed on Nov. 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 prediction and forecasting system for injectable medication administration, including: a predictive analytics module configured to generate forecasts for one or more of financial costs, medication demand, and adverse event probabilities; a data analysis module configured to process historical administration data to identify patterns related to one or more of cost trends, supply requirements, and safety indicators; and a recommendation engine configured to provide guidance based on one or more of financial projections, demand predictions, and adverse event risk assessments. In some aspects, the techniques described herein relate to a system, wherein the predictive analytics module is configured to utilize one or more of machine learning algorithms, statistical modeling techniques, and artificial intelligence methods for generating forecasts. In some aspects, the techniques described herein relate to a system, wherein the data analysis module is configured to process data including one or more of patient demographics, medication histories, administration patterns, and healthcare utilization metrics. In some aspects, the techniques described herein relate to a system, wherein the recommendation engine is configured to generate actionable insights for one or more of budget planning, inventory management, and clinical risk mitigation. In some aspects, the techniques described herein relate to a system, further including a financial modeling module configured to analyze cost patterns associated with one or more of medication procurement, administration infrastructure, and patient care delivery. In some aspects, the techniques described herein relate to a system, further including a demand forecasting module configured to predict medication needs based on one or more of seasonal trends, patient population growth, and treatment protocol changes. In some aspects, the techniques described herein relate to a system, further including an adverse event prediction module configured to identify risk factors using one or more of patient risk profiles, medication interaction databases, and clinical outcome histories. In some aspects, the techniques described herein relate to a system, wherein the predictive analytics module is configured to generate forecasts across one or more of short-term planning horizons, medium-term strategic planning, and long-term trend analysis. In some aspects, the techniques described herein relate to a system, further including a reporting module configured to generate predictive reports for one or more of healthcare administrators, pharmaceutical suppliers, and insurance providers. In some aspects, the techniques described herein relate to a comprehensive prediction and forecasting platform for injectable medication management, including: a predictive analytics module configured to generate forecasts for one or more of financial costs, medication demand, and adverse event probabilities; a data analysis module configured to process historical administration data to identify patterns related to one or more of cost trends, supply requirements, and safety indicators; a recommendation engine configured to provide guidance based on one or more of financial projections, demand predictions, and adverse event risk assessments; a financial modeling module configured to predict costs associated with one or more of medication procurement, administration overhead, and