Computerized GDM Risk Assessment System for Early Workflow Assignment
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Solution Overview
Problem
Conventional methods for detecting gestational diabetes mellitus (GDM) risk are inadequate for early identification, typically occurring at 24-28 weeks of pregnancy, which delays preventative measures and lacks reliable early detection.
Innovation Solution
A computerized system using machine learning to assess GDM risk based on medical information such as age, BMI, blood pressure, and family history, allowing for early detection and automatic initiation of preventative workflows before 24 weeks of pregnancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional GDM screening methods are used, then the detection process is simple and standardized, but the detection timing is delayed to 24-28 weeks of pregnancy
Solution Approach 1:
The system performs preliminary risk assessment actions before the conventional 24-28 week screening by evaluating multiple risk factors (age, BMI, blood pressure, family history, prior pregnancies) at the time of prenatal visit to identify women at risk for GDM earlier in pregnancy
Solution Approach 2:
The patent introduces an intermediary risk assessment system that mediates between conventional screening and early detection needs, using a composite risk score based on multiple factors to enable earlier identification while maintaining clinical reliability
2Loss of time
If early GDM risk assessment is performed using multiple factors, then early detection and preventative measures are enabled, but the assessment complexity increases
Solution Approach 1:
The risk assessment system is designed to be universally applicable to all pregnant women regardless of risk level, using a standardized multi-factor evaluation framework that can identify both high-risk and low-risk patients through the same comprehensive assessment process
Solution Approach 2:
The system transforms the assessment from a single-point-in-time glucose test to a multi-parameter evaluation framework, changing the input parameters from just glucose levels to include demographics, physical measurements, medical history, and social factors, thereby enabling earlier and more comprehensive risk identification
3Measurement precision
If comprehensive medical information is collected for risk assessment, then detection accuracy is improved, but the data collection process becomes more complex
Solution Approach 1:
The comprehensive data collection process is segmented into distinct categories including demographic information, physical measurements (BMI, blood pressure), medical history (prior pregnancies, family history), and social factors, making the complex data gathering process more manageable and systematic while maintaining high detection accuracy
Data Source
AI summary
Computerized systems and methods are provided for determining the risk of developing gestation diabetes mellitus (GDM) and assigning workflows based on such a determination. The systems and methods can include receiving medical information associated with an individual, determining whether the individual is at risk of developing GDM based on the received medical information, and performing one or more response actions. The one or more response actions can include assigning a workflow for preventative treatment of GDM, providing a notification that the individual is at risk of GDM, or a combination thereof.