Phenotype Analysis System Using Engine Marketplace
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Solution Overview
Problem
Current electronic medical record (EMR) systems lack a single, controlled field to reflect the current status of patient phenotypes, making it difficult for clinicians to query and obtain accurate phenotype information.
Innovation Solution
A phenotype analysis system that includes an interface for receiving phenotype queries, a processor to execute instructions from multiple phenotype engines, and a network to facilitate the execution of these queries across different entities, enabling the automated phenotyping of clinical phenotypes based on predefined rules and ontological analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians manually search through multiple data sources (problem list, medication list, lab values) to determine phenotype status, then they can obtain phenotype information, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary computation by automatically deriving phenotype statuses from EMR data and storing them in a phenotype database before clinicians need the information. Phenotype engines pre-process patient data using predefined rules and algorithms to calculate phenotype statuses, so when clinicians query for phenotype information, the results are already available or can be quickly retrieved, eliminating the need for manual searching through multiple data sources.
2Ease of operation
If a single controlled field for phenotype status is implemented, then phenotype querying becomes simple and efficient, but the system complexity increases
Solution Approach 1:
The system segments the complex task of phenotype analysis into multiple independent phenotype engines, each responsible for specific phenotypes or data sources. These engines operate separately and their results are aggregated to provide comprehensive phenotype status. This segmentation allows the system to manage complexity through modular design while providing simple phenotype querying capabilities to clinicians.
Solution Approach 2:
The system introduces a phenotype database and phenotype engine layer as intermediaries between the raw EMR data and the clinician's query interface. This intermediary layer automatically derives and stores phenotype statuses, transforming complex multi-source data into simplified phenotype information that clinicians can easily query and interpret, thereby improving ease of operation while managing system complexity through abstraction.
3Reliability
If multiple phenotype engines from different entities are used, then phenotype analysis accuracy and expertise coverage improve, but the device complexity and coordination requirements increase
Solution Approach 1:
The system merges multiple phenotype engines from different entities into a unified phenotype engine array that operates under a common framework. Each engine maintains its specialized expertise while contributing to a consolidated phenotype analysis process. The system combines their outputs through standardized interfaces and aggregation logic, achieving improved reliability through diverse expertise coverage while managing complexity through unified coordination mechanisms.
Solution Approach 2:
The phenotype engine array is designed with universal interfaces and standardized data formats that allow different phenotype engines to work together seamlessly. Each engine can process various types of phenotype queries using common rules and algorithms, making the system multi-functional and adaptable to different phenotype analysis needs while reducing complexity through standardization and interoperability.
Data Source
AI summary
Phenotype analysis systems and methods. The methods described herein may involve receiving a phenotype query related to at least one phenotype of a patient. A processor executing instructions stored on memory may then select at least one of a first phenotype engine provided by a first entity and a second phenotype engine provided by a second entity to execute the received query on medical data associated with the patient. An interface may then output a result in response to the received phenotype query from at least one of the first and second phenotype engines.