ML Care Pathway Stewardship for Faster Clinical Coordination

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Solution Overview

Problem

Healthcare stewardship across multiple service areas is time and labor intensive, and there is an increasing demand to improve patient treatment time and reduce patient treatment time and associated healthcare costs, and there is a need to enhance patient satisfaction and improve the quality of care for patients, facilitate efficient utilization of healthcare provider skills and resources, and optimize care coordination and delivery workflows while meeting healthcare compliance, quality of patient care, and patient safety requirements.

Innovation Solution

A system and method utilizing machine learning techniques to standardize healthcare service practices, improve workflow efficiency, and enhance regulatory compliance by training a machine learning model with clinical, historical, and regional care pathway datasets to determine patient criticality levels, care events of interest, and treatment recommendations, automatically surfacing these for healthcare personnel review, and administering treatments accordingly.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If healthcare personnel manually manage and coordinate care across multiple service areas, then patient care quality can be maintained through human judgment and flexibility, but time consumption and labor intensity increase significantly

Engineering Contradiction:

Improvepatient care qualityVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The machine learning model autonomously performs care coordination tasks including identifying care events, determining patient criticality levels, and generating treatment recommendations without requiring manual intervention for each task, thereby reducing time consumption while maintaining care quality through algorithmic decision-making

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes of care coordination with an automated machine learning system that processes patient data, identifies patterns, and generates recommendations, substituting human labor with computational mechanisms to reduce time consumption

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If healthcare personnel manually review and process patient data, then flexibility in clinical judgment is maintained, but cognitive burden increases leading to potential errors

Engineering Contradiction:

Improveflexibility in clinical judgmentVSAvoidcognitive burden

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The machine learning model acts as an intermediary between raw patient data and healthcare personnel decisions, processing and filtering information to present only relevant recommendations, thereby reducing cognitive burden while preserving clinical flexibility through human-in-the-loop review

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system provides feedback to healthcare personnel through treatment recommendations that can be reviewed and adjusted, maintaining flexibility in clinical judgment while reducing cognitive load by providing structured guidance rather than requiring unsolicited manual processing

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If manual care coordination is used across multiple service areas, then adaptability to individual patient needs is maintained, but workflow efficiency decreases

Engineering Contradiction:

Improveadaptability to patient needsVSAvoidworkflow efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The machine learning model changes the parameters of care coordination by automatically adjusting treatment recommendations based on patient criticality levels and care event identification, maintaining adaptability through algorithmic parameter adjustment rather than manual processes, thereby improving workflow efficiency

Inventive Principle:

Principle #35Parameter changes

4Reliability

If healthcare personnel manually manage treatment protocols, then compliance with regulatory requirements can be ensured through human oversight, but labor costs and operational expenses increase

Engineering Contradiction:

Improveregulatory complianceVSAvoidlabor resources

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The machine learning model autonomously ensures regulatory compliance by automatically applying care pathways and treatment protocols based on pre-programmed regulatory requirements, eliminating the need for manual compliance monitoring and reducing labor resources while maintaining reliability

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250372241A1Augmenting healthcare stewardship using machine learning

Publication Date: 2025.12.04 KAISER FOUNDATION HOSPITALS

AI summary

A system and method for facilitating a member journey through healthcare service by augmenting stewardship workflow across multiple service areas is disclosed. The system and method includes augmenting the healthcare stewardship workflow by standardizing the healthcare service practices, increasing the quality of care for patients, improving the workflow efficiency of healthcare personnel, reducing the costs associated with providing healthcare services, and enabling the ease of regulatory compliance using machine learning techniques. The system and method provides the healthcare personnel with access to healthcare-relevant and fact-based artificial intelligence powered by machine learning for decision support opportunities to drive recommended care pathways.