Unified EHR Case Management System for Anomaly Detection
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Solution Overview
Problem
The healthcare market lacks integration of financial, clinical, and access management services, leading to a lack of transparency and inefficiencies, including fraud and abuse, due to proprietary payment transactions and fragmented data systems.
Innovation Solution
A system for collecting and unifying fragmented data from multiple sources, applying algorithms to identify anomalies, and providing decision matrices for addressing these anomalies, enabling transparent and reliable decision-making across financial, clinical, and access components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fragmented data from multiple sources is collected and unified, then transparency and reliability of healthcare data is improved, but system complexity and integration difficulty increase
Solution Approach 1:
The system segments data from different sources (financial, clinical, access management) into distinct modules while maintaining a unified interface. Each data source is processed independently through standardized algorithms, allowing the system to manage complexity through modular design while achieving integrated transparency.
Solution Approach 2:
The patent introduces an intermediary processing layer that standardizes and harmonizes data from multiple proprietary sources. This intermediary layer applies uniform algorithms to transform fragmented data into a coherent unified format, mediating between diverse data sources and the decision-making interface.
2Measurement precision
If algorithms are applied to identify anomalies in unified data, then detection precision and fraud reduction are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data standardization and preprocessing before anomaly detection algorithms are applied. By pre-processing the unified data into a standardized format with consistent structures, the system reduces the computational burden during the actual anomaly detection phase, thereby decreasing processing time while maintaining detection precision.
3Reliability
If decision matrices are provided for addressing anomalies, then decision-making quality and patient safety are improved, but system complexity and operational overhead increase
Solution Approach 1:
The system provides self-service decision support by automatically generating and presenting decision matrices with multiple options for addressing identified anomalies. The system autonomously processes the anomaly detection and presents structured decision options, reducing the need for complex manual analysis while improving decision quality through comprehensive data-driven options.
Data Source
AI summary
A method and system for transparent recording, storing, and accessing financial, clinical, and data/information case management services. The method includes collecting service data pertaining to a customer from a plurality of sources, storing the data, and providing multiple points of access to the data for retrieval by an individual via a user interface. A decision-support system may operate independently as an electronic health record or in conjunction with an integrated electronic health record and allows customers or their representatives to plan, facilitate, and monitor the management of financial, clinical, and data/information issues pertaining to their healthcare. In particular, the system is adapted to identify data tending to indicate an anomaly in the customer's health record and facilitate the selection and implementation of one or more options to address and/or correct the anomaly.