Predictive Bottleneck Model for Healthcare Workflow Optimization

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Solution Overview

Problem

Modern healthcare facilities face inefficiencies due to segregated departments, lack of data integration, and inadequate data analysis, leading to bottlenecks in patient care that decrease the quality of care and operational capacity.

Innovation Solution

A computerized system and method for creating and updating predictive bottleneck models using aggregated data and sensors, which generates interactive GUIs with recommendations for corrective actions to mitigate bottlenecks, improving data integration and analysis through rule-based automation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If traditional healthcare systems use segregated departments and units, then organizational structure is simplified, but coordination between departments deteriorates and bottlenecks increase

Engineering Contradiction:

Improveorganizational structureVSAvoidpatient care efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent implements a unified healthcare management platform that merges data and operations across previously segregated departments. The system integrates patient flow data, caregiver schedules, and resource allocation into a single coordinated framework, enabling cross-departmental optimization of patient care workflows.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system employs real-time monitoring and predictive analytics to continuously monitor healthcare workflows, identifying bottlenecks before they impact patient care. Feedback loops enable dynamic adjustment of resource allocation and scheduling across departments based on actual workflow conditions.

Inventive Principle:

Principle #23Feedback

2Device complexity

If traditional systems lack comprehensive data integration, then data management is simpler, but analysis capability and operational capacity deteriorate

Engineering Contradiction:

Improvedata managementVSAvoidoperational capacity

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent consolidates multiple data sources including patient flow information, caregiver schedules, equipment status, and historical performance data into a unified data warehouse. This integrated data structure enables comprehensive analysis of healthcare operations and predictive modeling of future workflows.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system performs preliminary data processing and predictive analysis to forecast potential bottlenecks before they occur. By analyzing historical data patterns and current workflow conditions, the system proactively identifies areas needing intervention to maintain optimal operational capacity.

Inventive Principle:

Principle #10Preliminary action

3Device complexity

If caregivers manually analyze data for bottleneck identification, then system complexity is reduced, but response time and analysis accuracy deteriorate

Engineering Contradiction:

Improvesystem complexityVSAvoidbottleneck detection time

Core Design Contradiction:

Device complexityVSLoss of time

Solution Approach 1:

The system automatically monitors healthcare workflows, collects data from various sources, and identifies bottlenecks without requiring manual intervention. The predictive model continuously self-updates using new data to improve its accuracy in detecting workflow inefficiencies and predicting future bottlenecks.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Real-time data collection and automated analysis provide immediate feedback on workflow conditions, enabling rapid identification and response to emerging bottlenecks. The system continuously monitors key performance indicators and triggers alerts when thresholds are exceeded.

Inventive Principle:

Principle #23Feedback

4Power

If traditional systems do not use predictive modeling, then computational requirements are lower, but ability to prevent bottlenecks deteriorates

Engineering Contradiction:

Improvecomputational powerVSAvoidbottleneck prediction accuracy

Core Design Contradiction:

PowerVSReliability

Solution Approach 1:

The system employs predictive analytics to forecast potential bottlenecks before they impact patient care. By analyzing historical data patterns, seasonal variations, and current workflow conditions, the model proactively identifies areas at risk and enables preventive interventions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The predictive model continuously refines its parameters by learning from new data and adjusting to changing healthcare conditions. This adaptive approach allows the system to maintain high prediction accuracy despite variations in workflow patterns, seasonal demand changes, and organizational adjustments.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20240347186A1Systems and methods for computer modeling for healthcare bottleneck prediction and mitigation

Publication Date: 2024.10.17 TELETRACKING TECHNOLOGIES INC

AI summary

Systems and methods are disclosed for managing predictive bottleneck models. In one embodiment, a computerized system may comprise a storage medium storing instructions, and a processor in communication with a communications network. The processor may be configured to receive, from a user device, bottleneck data indicating a bottleneck within a facility; compile, based on the received indication, contextual data associated with the bottleneck; analyze the bottleneck data and the contextual data conjunctively; determine a relationship between the bottleneck data and the contextual data; and update a predictive bottleneck model based on the determined relationship.