Clinical Process Prioritization via Optimized Practice Models
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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 comprehensive changes.
Innovation Solution
The development of optimized practice process models that define end-to-end activities and identify critical levers for clinical, financial, and operational improvements, with associated benefit and effort metrics, allowing for data-driven analysis and prioritization of opportunities for process optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transformational consulting is used to evaluate and identify areas for clinical process improvement, then opportunities for process optimization 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, operational data, and financial data from multiple sources, pre-processing this information, and generating initial process improvement opportunities before consultant involvement. This preliminary action reduces the time and effort required during the actual consulting engagement while maintaining reliable identification of improvement opportunities.
2Quantity of substance
If management information systems collect and analyze wide variety of clinical data, then data availability for process improvement is enhanced, but the systems lack flexibility and sophistication for modeling proposed changes
Solution Approach 1:
The system integrates multiple functions into a single platform: data collection from clinical, operational, and financial sources; data storage and management; process modeling and simulation; and opportunity identification. This multi-functional system simultaneously handles large quantities of diverse data while providing sophisticated modeling capabilities to evaluate proposed process changes, eliminating the need for separate specialized systems.
3Measurement precision
If evidence-based modeling compares empirical data to objective guidelines, then outcomes can be evaluated, but the approach fails to account for effects on the entire clinical process
Solution Approach 1:
The system segments the clinical process into discrete activities and processes, allowing individual evaluation against objective guidelines while maintaining visibility of interconnections. Each segment can be analyzed independently for precision against guidelines, yet the system aggregates these segmented analyses to evaluate the cumulative effect on the entire clinical process, resolving the contradiction between precise measurement and comprehensive analysis.
4Adaptability or versatility
If consultants work with clients to determine changes on the fly, then customized solutions can be developed, but the process becomes inefficient and cannot identify all potential opportunities
Solution Approach 1:
The system enables self-service by automatically generating process improvement opportunities and customized change recommendations based on the organization's specific data and context. The system adapts to each client's unique situation by analyzing their specific clinical, operational, and financial data patterns, providing customized solutions without requiring extensive manual consultant intervention, thereby maintaining both adaptability and efficiency.
Data Source
AI summary
Methods and user interfaces are provided for prioritizing 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 prioritizing the opportunities, for example, based on benefit indexes and effort indexes determined for each opportunity.