Patient Discovery Workflow for Automated Triage Distribution
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Solution Overview
Problem
Current patient management systems in healthcare face challenges in efficiently managing incidental findings, resource allocation, and optimizing clinical workflows due to manual processes, human error, and lack of standardized follow-up protocols, leading to potential oversight and mismanagement.
Innovation Solution
A workflow optimization system that includes a patient discovery circuit for retrieving relevant patient data, a feature extraction circuit for structuring data, and a patient distribution circuit for prioritization and distribution based on medical features and resource availability, using neural networks for data transformation and aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated tools are used to identify patients with incidental findings, then patient discovery sensitivity is improved, but information overload occurs leading to clinician burden
Solution Approach 1:
The system segments the patient management workflow into distinct automated components: patient discovery through NLP analysis of radiology reports, feature extraction from unstructured data, and automated triage prioritization. This segmentation allows high-sensitivity detection to be isolated from clinician tasks, automatically filtering and prioritizing patients before presentation to providers.
Solution Approach 2:
An automated triage system acts as an intermediary between the high-sensitivity patient discovery system and clinicians. This intermediary layer processes discovered patients through feature extraction and prioritization algorithms, transforming the raw high-volume output into a manageable, prioritized list that reduces clinician burden while maintaining discovery sensitivity.
2Adaptability or versatility
If manual patient management processes are used, then resource allocation flexibility is maintained, but administrative costs increase and delays occur
Solution Approach 1:
The system implements self-service automation where the triage system automatically performs patient discovery, feature extraction, and prioritization without manual administrative intervention. This self-service capability eliminates costly manual processes while maintaining adaptability through configurable search criteria and distribution rules that can be adjusted based on clinical needs.
3Reliability
If high sensitivity settings are used in patient discovery systems, then critical cases are captured, but information overload occurs creating clinician burden
Solution Approach 1:
The system performs preliminary feature extraction and prioritization processing on all discovered patients before they are presented to clinicians. By pre-processing the high-volume output of high-sensitivity discovery systems and organizing patients by priority levels, the system maintains comprehensive critical case detection while reducing the effective volume of actionable items requiring clinician attention.
Data Source
AI summary
A workflow optimisation system and method for at least one of: prioritisation of patients and distribution of patients to a specified treatment pathway is described. The system comprising: a database comprising patient medical data; a patient discovery circuit for receiving medical data from the database to transform the received medical data to retrieved patient data; a feature extraction circuit configured to process the retrieved patient data to produce structured data for each of the patients, and aggregate the structured data to a single vector for each patient, where the single vector summarises medical features for the patient; and a patient distribution circuit for receiving the single vector for each patient and determining, from the single vector for each patient, at least one of: a patient prioritisation list to prioritise patients for distribution, and a patient distribution list to distribute the patients to one or more distribution targets.