Generic Repository for Healthcare Gap Identification
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Solution Overview
Problem
The variability in electronic medical record systems across different providers and vendors leads to inefficiencies in data integration and analysis, resulting in incomplete and inaccurate data, which hinders the automated identification of care gaps and increases the cost and time required for healthcare providers to implement solutions.
Innovation Solution
A system that normalizes clinical data from various sources into a generic format, allowing for the conversion of human-readable medical rules into machine-executable language, enabling the identification of patient cohorts and care gaps within a semantically normalized data repository.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If providers implement point-to-point bridges between each data source and application, then data integration capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements a universal data repository that can store clinical data from multiple different data sources in a standardized format. This single repository serves multiple applications (quality reporting, care management, etc.) without requiring separate point-to-point bridges for each application, thereby reducing overall system complexity while maintaining broad adaptability.
Solution Approach 2:
The standardized data repository acts as an intermediary layer between diverse data sources and various applications. Instead of applications directly connecting to specific data sources (requiring custom bridges), the repository mediates by providing a uniform interface that all applications can access, eliminating the need for multiple specialized integration layers.
2Measurement precision
If providers implement provider-specific bridges for each data source, then data extraction accuracy is improved, but loss of time and cost increase
Solution Approach 1:
The patent changes the data representation parameters by establishing a standardized data model that uniformly describes clinical data from all sources. This standardization allows accurate data extraction without requiring custom extraction logic for each source, as the same standardized parameters can be applied across all data sources, significantly reducing implementation time.
3Device complexity
If applications are not able to deal with various types of data, then device complexity is reduced, but loss of information occurs
Solution Approach 1:
The standardized data repository provides a universal data model that can represent various types of clinical data (labs, progress notes, discharge summaries, medications, allergies) in a unified structure. This allows applications to access and process diverse data types through a single standardized interface, maintaining data completeness without increasing application complexity.
Data Source
AI summary
By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A medical domain specific language may be used to interact with the data repository for identifying cohorts and gaps in care for the respective cohorts. Any rules for finding gaps in care are converted into the medical domain specific language for determining gaps. This standardization in both the data repository and rule application may allow for a true cost and time to value solution accessible to many different medical practices.