Clinical Data Prioritization Using Phenotype Relevance Ranking
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Solution Overview
Problem
The overwhelming volume of clinical data in Electronic Medical Records (EMRs) overwhelms medical professionals, leading to inefficient review processes and potential omission of relevant information due to the prioritization of recently acquired data.
Innovation Solution
A system and method for retrieving, phenotyping, and assigning relevance scores to medical data from multiple sources, followed by ranking and displaying the codes in order of relevance to facilitate efficient review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all clinical data from multiple sources is displayed in the EMR system, then the completeness of medical information is improved, but the complexity of data processing and review time increases significantly
Solution Approach 1:
The patent segments the overwhelming clinical data into discrete phenotypic codes representing specific patient characteristics, conditions, and findings. Each code acts as a manageable unit that can be independently processed, stored, and retrieved, transforming the monolithic data problem into manageable segments that reduce processing complexity while maintaining information completeness.
Solution Approach 2:
The patent introduces phenotypic codes as an intermediary layer between raw clinical data and the user interface. These codes serve as mediators that aggregate and structure heterogeneous data from multiple sources (labs, imaging, notes, medications) into a standardized format, simplifying the interface between data storage and clinical review without losing information.
2Loss of information
If all clinical data is displayed with equal prominence, then the completeness of information review is improved, but the time required for data review increases significantly
Solution Approach 1:
The patent changes the parameter of data presentation from equal prominence to prioritized ordering based on clinical relevance. By assigning and utilizing relevance scores, the system transforms the display parameter from uniform to differentiated, allowing clinicians to review high-priority information first while maintaining access to complete data if needed.
Solution Approach 2:
The system performs preliminary prioritization of clinical data by calculating relevance scores and ordering phenotypic codes before presentation to the clinician. This preliminary action of pre-ranking information allows clinicians to immediately see the most relevant findings without having to search through all data, reducing review time while ensuring complete information remains available.
3Speed
If recently acquired data is prioritized in display, then the timeliness of information is improved, but the accuracy of diagnostic decision-making deteriorates due to omission of relevant historical information
Solution Approach 1:
The patent changes the prioritization parameter from temporal (recency) to clinical relevance based on phenotypic coding and scoring. Instead of sorting by acquisition time, the system orders data by calculated relevance scores that reflect diagnostic importance, allowing both timely and historically significant information to be appropriately prioritized based on clinical context rather than timestamp.
Data Source
AI summary
A system and method prioritizes and presents heterogenous medical data. The method includes retrieving medical data of a patient, the medical data including data from multiple data sources. The method includes phenotyping the medical data to generate a reasoning trail and reasoning outcome including one or more codes, the reasoning trail including a basis for which the reasoning outcome is determined. The method includes assigning a relevance score to each of the one or more codes. The method includes ranking the codes based on the relevance score of each of the one or more codes. The method includes displaying the codes in rank order.