Clinical Process Optimization Interface for Opportunity 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 improvement across the entire clinical process, often focusing on individual aspects rather than holistic changes.
Innovation Solution
The development of optimized practice process models that define optimal end-to-end clinical processes, identify critical levers for improvement, and provide data for quantifying benefits and efforts, allowing for the analysis and prioritization of opportunities for process optimization using computer-readable media and graphical user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transformational consulting is used to evaluate and identify areas for clinical process improvement, then improvement opportunities can be identified, but the process becomes time consuming and labor intensive
Solution Approach 1:
The system performs preliminary analysis by automatically collecting clinical data, comparing it against evidence-based guidelines, and pre-identifying improvement opportunities before consultant involvement. This preliminary screening reduces the scope and time required for subsequent consulting evaluation.
Solution Approach 2:
The patent replaces manual consulting evaluation with an automated computer-based system that collects clinical data, compares it to evidence-based guidelines, and generates improvement recommendations automatically, eliminating the time-consuming manual analysis performed by consultants.
2Quantity of substance
If management information systems collect and analyze clinical data, then data availability improves, but the systems lack flexibility and sophistication for modeling proposed changes
Solution Approach 1:
The system dynamically adapts to different clinical scenarios by allowing users to input various proposed changes and automatically modeling their potential impacts. The system can simulate different intervention strategies and predict outcomes based on evidence-based guidelines, providing flexible analysis for diverse improvement scenarios.
Solution Approach 2:
The patent introduces an evidence-based guideline database as an intermediary between raw clinical data and analysis results. This intermediary layer enables sophisticated modeling by serving as a reference framework against which proposed changes can be evaluated, allowing the system to model impacts without requiring complex custom programming for each scenario.
3Measurement precision
If evidence-based modeling compares empirical data to objective guidelines, then outcome evaluation improves, but the system fails to account for effects on the entire clinical process
Solution Approach 1:
The system is designed as a universal platform that can evaluate multiple aspects of clinical processes simultaneously. It compares empirical data against evidence-based guidelines while also modeling the broader impacts on the entire clinical workflow, combining precise outcome measurement with comprehensive process analysis in a single integrated system.
Solution Approach 2:
The patent segments the clinical process evaluation into manageable components (data collection, guideline comparison, impact modeling) that can be independently analyzed and then integrated. This segmentation allows precise measurement of specific outcomes while maintaining the ability to evaluate overall process effects by combining results from individual component analyses.
4Adaptability or versatility
If consultants work with clients to determine changes on the fly, then customization to facility needs improves, but the approach becomes inefficient and labor intensive
Solution Approach 1:
The system enables facilities to independently analyze their own clinical processes by automatically collecting their data, comparing it to evidence-based guidelines, and generating customized improvement recommendations. This self-service capability provides customization to facility needs without requiring external consulting resources, dramatically improving efficiency while maintaining adaptability.
Data Source
AI summary
User interfaces are provided for analyzing 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. User interfaces are provided for analyzing the opportunities.