Patient Population Assignment in Electronic Health Records
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Solution Overview
Problem
Conventional electronic health record systems (EHRs) require clinicians to manually examine numerous patient health records to identify patients at high risk for various medical conditions, which is inefficient and time-consuming, especially in large patient populations.
Innovation Solution
A distributed computer-executable population health application that collaborates with EHRs to automatically assign patients to health populations, using client and server software to identify and present patients at risk, thereby streamlining the process of identifying high-risk patients and proactive care needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional EHR systems present patient health records one at a time for manual review, then clinicians can examine each record in detail, but the time and effort required to identify high-risk patients increases significantly
Solution Approach 1:
The system performs preliminary automated analysis of patient records before presenting them to clinicians. Risk scores and population assignments are calculated in advance using algorithms that evaluate multiple risk factors, allowing clinicians to review pre-sorted lists rather than examining records sequentially from scratch
Solution Approach 2:
The system creates simplified copies or representations of complex patient records in the form of risk scores and population category assignments. These condensed representations allow rapid scanning and comparison while retaining the essential information needed for risk assessment
2Reliability
If clinicians manually examine numerous patient health records to identify high-risk patients, then comprehensive risk assessment can be achieved, but productivity and efficiency decrease
Solution Approach 1:
The system replaces the manual mechanical process of reviewing each patient record with automated computational algorithms. Software automatically calculates risk scores by evaluating multiple risk factors across patient populations, eliminating the need for manual examination while maintaining comprehensive assessment through systematic algorithmic analysis
3Measurement precision
If conventional EHRs require manual updating of patient risk lists, then data accuracy can be maintained, but the complexity and time required for data management increases
Solution Approach 1:
The system performs self-service by automatically calculating and updating risk scores and population assignments without requiring manual clinician intervention. The algorithms continuously process patient data and maintain up-to-date risk classifications automatically, reducing data management complexity while preserving accuracy through systematic computational methods
Data Source
AI summary
Technologies pertaining to assigning patients to patient populations and graphically indicating that the patients have been assigned to the patient populations are described herein. A graphical user interface includes interactive elements that are configured to indicate to a healthcare worker that a patient has been assigned to a population, and further to depict proposed actions based upon the patient being assigned to the population.