ML Adverse-Event Report Generation for Clinical Trial Workflows

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Solution Overview

Problem

The process of documenting and reporting adverse events in clinical trials is labor-intensive and time-consuming, requiring significant resources and expertise, which escalates costs when outsourced.

Innovation Solution

A machine-learning model is used to generate adverse-event reports by processing clinical data from various sources, including adverse-event, concomitant-medications, and laboratory-results data, to create comprehensive reports with hyperlinked clinical data items and empty fields for user modification, trained on previous reports.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual documentation methods are used for adverse events, then reports can be generated with human expertise and oversight, but the process becomes labor-intensive and time-consuming

Engineering Contradiction:

Improvereport accuracyVSAvoidreporting time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service automated report generation by allowing the machine-learning model to autonomously process clinical data and generate adverse-event reports without requiring manual intervention for each report, while still maintaining quality through feedback mechanisms

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual documentation process with an automated machine-learning-based system that processes clinical data and generates reports algorithmically, substituting human labor with computational processes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If more resources and expertise are allocated to adverse-event reporting, then report quality and compliance improve, but costs escalate

Engineering Contradiction:

ImprovecomplianceVSAvoidresources

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system performs self-service by automatically ensuring compliance through the machine-learning model's trained understanding of regulatory requirements, eliminating the need for additional human resources dedicated to compliance verification

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses copying by training the machine-learning model on previous adverse-event reports and regulatory documents, allowing it to replicate compliance patterns and reporting standards without requiring expert human reviewers for each new report

Inventive Principle:

Principle #26Copying

3Productivity

If automated systems are used to reduce manual work, then reporting efficiency improves, but the complexity of the system increases

Engineering Contradiction:

Improvereporting efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system manages complexity through feedback mechanisms where user corrections and modifications to generated reports are fed back into the training process, allowing the model to learn and improve while maintaining a relatively simple operational interface

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary action by pre-training the machine-learning model on extensive clinical data and regulatory documents before deployment, so that the complex processing is done in advance during training rather than during actual report generation

Inventive Principle:

Principle #10Preliminary action

4Loss of information

If comprehensive clinical data is processed to ensure complete reporting, then report completeness improves, but processing time increases

Engineering Contradiction:

Improvedata completenessVSAvoidprocessing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system replaces manual data processing with automated machine-learning-based processing that can efficiently analyze comprehensive clinical data including lab results, vital signs, and medication information simultaneously without the sequential bottlenecks of manual review

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260038649A1Machine-learning techniques for generating adverse-event reports

Publication Date: 2026.02.05 TRIALSPARK INC D B A FORMATION BIO

AI summary

Disclosed embodiments may provide techniques for generating adverse-event reports using machine-learning models. A computer-implemented method can include receiving a selection of a particular subject from a plurality of subjects participating on a clinical trial. A plurality of identifiers associated with the plurality of subjects can be displayed on a first portion of a user interface. The computer-implemented method can also include accessing clinical data associated with a particular subject, in which the clinical data includes a plurality of clinical-data items. The computer-implemented method can also include receiving a request to generate an adverse-event report. The computer-implemented method can also include processing the clinical data using a machine-learning model to generate one or more sections associated with the adverse-event report. The computer-implemented method can also include causing the one or more sections associated with the adverse-event report to be displayed on a second portion of the user interface.