Context Sensor for EHR Data Accuracy and Collection Time
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems face challenges in efficiently collecting and analyzing data, connecting disparate systems, improving Electronic Health Record (EHR) systems, and interacting seamlessly with insurance company data analytics to support Medicare risk reduction programs, leading to time-consuming data replication and limited access to existing EHR data.
Innovation Solution
A distributed architecture system that surfaces contextually relevant content from third-party analytics systems within the workflow of healthcare providers, using an Insights Application (IA) and Context Sensor to correlate patient data with external information, providing alerts and detailed views for improved care coordination and risk adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual chart audit is used for risk adjustment, then data accuracy can be ensured, but data collection time and labor intensity increase significantly
Solution Approach 1:
The system creates electronic copies of patient data from EHR systems and other sources, transforming physical/manual chart review into digital data processing. This allows automated analysis while maintaining data accuracy, eliminating the need for manual copying and review of patient records.
Solution Approach 2:
The patent replaces the mechanical manual process of chart auditing with an automated electronic system that uses software algorithms to process, analyze, and validate patient data. This substitution of mechanical human labor with automated computing systems dramatically reduces data collection time while maintaining or improving accuracy through consistent application of validation rules.
2Reliability
If EHR systems are kept closed for data security, then data protection is improved, but access to existing EHR data for analytics is limited
Solution Approach 1:
The system introduces an intermediary layer that sits between the closed EHR systems and the analytics platform. This intermediary securely connects to multiple EHR systems, extracts necessary data through authorized interfaces, and delivers it to the risk adjustment platform without requiring EHR systems to open their data structures or compromise their security models.
Solution Approach 2:
The architecture segments the system into independent components: EHR systems remain closed and secure, the intermediary handles data extraction and transmission, and the analytics platform processes the data. This segmentation allows each component to maintain its security boundaries while enabling collaborative functionality across the distributed system.
3Quantity of substance
If multiple disparate systems are connected for comprehensive data collection, then data completeness improves, but system complexity and integration difficulty increase
Solution Approach 1:
The system employs a universal intermediary platform that can connect to multiple different EHR systems and data sources through standardized interfaces. This multi-functional intermediary handles diverse data formats, protocols, and system architectures uniformly, reducing the complexity that would otherwise arise from creating custom integrations for each system pair.
Solution Approach 2:
The intermediary acts as a mediating layer that abstracts the complexity of connecting disparate systems. It provides a unified interface for data collection from multiple sources while managing the underlying complexity of protocol translation, data normalization, and system compatibility, thereby improving data completeness without proportionally increasing visible system complexity.
4Adaptability or versatility
If data is collected from multiple external sources, then analytics capability improves, but data replication and processing time increase
Solution Approach 1:
The system performs preliminary data collection, validation, and normalization at the point of data entry or during initial extraction from source systems. By preparing and validating data upfront rather than performing extensive processing later, the system improves analytics capability while reducing the time required for subsequent data processing and risk adjustment calculations.
Data Source
AI summary
Systems and methods for detecting interactions with private user data within data management systems are discussed herein. A communication session is initiated between a software-defined interaction sensor and a first data management system, where the first data management system includes historical records corresponding to a plurality of private users. The first data management system authenticates the software-defined interaction sensor and grants the software-defined interaction sensor access to monitor for predetermined data interactions at the first data management system. The software-defined interaction sensor detects, over the communication session, one or more triggering data interactions corresponding to particular private user data at the first data management system, and furthermore transmits to a second data management system information corresponding to the one or more triggering data interactions. The second data management system is operatively configured to transmit to the first data management system third-party historical records corresponding to the particular private user data.