Protocol Adherence Analytics for Unnecessary Utilization Reduction
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Solution Overview
Problem
Current methods for managing Ambulatory Care Sensitive Conditions (ACSCs) fail to deliver personalized entity management, address communication lapses, ensure consistent adherence to external follow-ups, and utilize accurate predictive models to capture the intricate nature of ACSC risk, leading to suboptimal protocol adherence and increased internal system utilization.
Innovation Solution
A centralized system and method that integrates multiple data sets, employing advanced analytical methodologies like machine-learning algorithms to identify patterns and correlations, providing insights and actionable recommendations to improve protocol adherence and reduce complexity, while monitoring entity data over time and adjusting models for changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex protocols are used for ACSC management, then comprehensive care coverage is improved, but protocol adherence becomes more difficult
Solution Approach 1:
The patent segments the complex protocol adherence problem into multiple analysis dimensions including entity-specific factors, protocol characteristics, historical performance, and contextual variables. By dividing the comprehensive care coverage into manageable analytical components, the system can process and address each segment individually through targeted interventions while maintaining overall protocol comprehensiveness.
Solution Approach 2:
The system dynamically adjusts protocol parameters and recommendations based on analyzed entity characteristics, historical adherence patterns, and risk factors. By changing protocol parameters to match individual entity needs and capabilities, the system maintains comprehensive care coverage while adapting protocols to be more adherable for specific populations.
2Quantity of substance
If traditional management techniques are used, then resource utilization is maintained, but unnecessary internal system utilization increases
Solution Approach 1:
The system performs preliminary analysis of entity adherence risks and predicts potential protocol failures before they occur. By identifying high-risk entities and implementing preventive interventions in advance, the system prevents unnecessary internal system utilization such as emergency department visits and hospital admissions, thereby reducing wasted resource consumption.
Solution Approach 2:
The system continuously monitors adherence metrics, risk factors, and utilization patterns, then feeds this information back to adjust management strategies. By implementing feedback loops that connect adherence analysis with resource allocation decisions, the system optimizes resource utilization by directing resources to high-risk entities while reducing unnecessary utilization among low-risk populations.
3Ease of operation
If reactive protocol simplification is applied, then some adherence issues are addressed, but proactive identification of adherence barriers is lost
Solution Approach 1:
The system proactively identifies adherence barriers by analyzing entity characteristics, historical data, and risk factors before protocol non-adherence occurs. By performing preliminary risk assessment and identification of potential barriers, the system can implement preventive simplification strategies and targeted education before adherence problems develop, eliminating the need for reactive simplification.
Solution Approach 2:
The system dynamically adjusts protocol complexity and recommendations based on real-time analysis of entity needs, capabilities, and risk factors. Rather than applying static simplification rules, the system adapts protocol requirements dynamically to match individual entity circumstances, providing proactive identification and resolution of adherence barriers while maintaining appropriate care standards.
Data Source
AI summary
Systems and methods are disclosed for determining unnecessary internal system utilization based on protocol adherence. A method includes receiving a first data object, generating an entity data object, and generating a verified entity data object based on comparing one or more metrics of the entity data object against one or more predetermined threshold values, wherein entities of the verified entity data object are a subset of the entities of the entity data object. The method further includes generating a compliance indicator for each entity of the verified entity data object. The method furthermore includes generating a utilization adjustment data object and causing the utilization adjustment data object to be displayed on a Graphical User Interface (GUI).