Optimized Practice Process Model for Clinical Workflow Analysis
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Solution Overview
Problem
Current clinical process optimization methods, such as transformational consulting and management information systems, are inefficient and fail to effectively identify and prioritize opportunities for improving healthcare processes due to their focus on individual aspects rather than comprehensive, end-to-end process analysis.
Innovation Solution
The development of optimized practice process models that define optimal clinical process flows, identify critical levers, and provide data for quantifying benefits and efforts, allowing for the analysis and prioritization of opportunities for process optimization across clinical, financial, operational, and regulatory aspects, with a closed-loop system for continuous monitoring and refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformational consulting is used to evaluate and identify areas for improvement in clinical processes, then comprehensive analysis of current processes is achieved, but the approach becomes time consuming and labor intensive
Solution Approach 1:
The patent pre-defines optimized practice process models with identified critical levers and improvement opportunities before actual clinical process evaluation. This preliminary structuring allows the system to directly map current processes against pre-analyzed optimal models, eliminating the need for time-consuming manual consultation while maintaining comprehensive analysis capability
Solution Approach 2:
The patent creates simplified digital representations (copies) of optimized clinical processes that capture the essential improvement opportunities. These digital models can be rapidly applied and compared against actual processes without requiring physical consultant intervention, thus reducing time and labor while preserving analytical depth
2Quantity of substance
If management information systems collect and analyze clinical data, then data gathering capability is enhanced, but the systems lack flexibility and sophistication for modeling proposed changes
Solution Approach 1:
The patent introduces optimized practice process models as an intermediary layer between raw clinical data and analysis functions. These models serve as templates that guide data collection and enable sophisticated modeling of proposed changes by providing a structured framework that connects data gathering with predictive simulation capabilities
Solution Approach 2:
The patent breaks down clinical processes into discrete activities and identifies specific critical levers within each activity. This segmentation allows the system to collect data at granular levels while maintaining the flexibility to model changes in specific areas without requiring complete process re-analysis, thus enhancing both data gathering and modeling sophistication
3Measurement precision
If evidence-based modeling uses objective guidelines to evaluate outcomes, then empirical data comparison is achieved, but the approach fails to account for changes' effect on the entire clinical process
Solution Approach 1:
The patent merges individual objective guidelines into integrated optimized practice process models that represent complete end-to-end clinical processes. By combining multiple discrete guidelines into unified process models that span the entire clinical workflow, the system maintains precise empirical data comparison while capturing the interconnected effects of changes across the whole process
4Loss of information
If comprehensive clinical process analysis is performed to identify improvement opportunities, then complete understanding of process flows is achieved, but the complexity makes it difficult to identify high-impact opportunities efficiently
Solution Approach 1:
The patent applies local quality by identifying and emphasizing critical levers—specific activities within the clinical process that have disproportionate impact on outcomes. Rather than treating all process activities equally, the system focuses analytical resources on these key areas, maintaining complete process understanding while dramatically improving efficiency in identifying high-impact opportunities
Data Source
AI summary
Systems, methods, and graphical user interfaces are provided for identifying, analyzing, and adopting opportunities for optimizing clinical processes within clinical facilities. An optimized practice process model may be defined for a particular clinical procedure, setting forth an optimal clinical process. In addition, critical levers may be identified within the optimal clinical process, representing the activities that have the greatest impact on outcomes. Clinical facilities may collect current measures for the critical levers, and the current measures may be compared against an optimal, benchmark, and/or target measure. Based on the comparison, opportunities for clinical process optimization may be identified. Those opportunities may then be analyzed and prioritized for adoption into a facility's current practice. Clinical processes within healthcare facilities may be further improved by continuously monitoring facility data and identifying further opportunities of optimization. Further, collected data may be used to refine the optimized practice process model, allowing for further optimization.