Pregnancy Symptom Alert System Using Dynamic Risk Analysis
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Solution Overview
Problem
Expectant mothers often hesitate to report pregnancy symptoms due to concerns about being perceived as hypochondriac, leading to delayed detection of potential risks, as symptoms can be normal but indicate serious conditions depending on patterns and contextual factors.
Innovation Solution
A computer-implemented system that allows expectant mothers to report symptoms through a mobile interface, analyzing these reports over a dynamic time window based on the initial symptom, considering the mother's profile and physiological parameters to generate a risk alert only when a potential risk is identified, thus encouraging timely reporting without immediate medical involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If expectant mothers are encouraged to report all symptoms immediately, then early risk identification is improved, but the reliability of reporting deteriorates due to false alarms from normal pregnancy symptoms
Solution Approach 1:
The system performs preliminary analysis of symptom patterns using pre-defined risk criteria and algorithms before generating alerts. This preliminary action filters out normal pregnancy symptoms by comparing them against established risk patterns, allowing early risk identification while maintaining reporting reliability through systematic evaluation rather than immediate reaction to all symptoms
Solution Approach 2:
The system implements feedback mechanisms where symptom reports are continuously analyzed and compared against evolving risk patterns. The feedback loop allows the system to learn from reported symptoms and refine risk assessment, improving both early detection capability and reporting reliability by distinguishing between normal and concerning symptom patterns over time
2Reliability
If a system generates alerts for any reported symptom, then sensitivity to potential risks is improved, but the loss of information deteriorates due to alert fatigue and ignored warnings
Solution Approach 1:
The system applies local quality by differentiating alert generation based on specific symptom patterns and contextual factors. Rather than uniform alerting for all symptoms, the system tailors alert generation to specific risk patterns, ensuring that alerts are generated only when symptom combinations match predefined risk criteria, thereby maintaining high sensitivity while preserving information value
Solution Approach 2:
The system changes parameters by dynamically adjusting alert thresholds and risk assessment criteria based on gestational age, symptom patterns, and individual patient factors. This parameter adaptation allows the system to maintain high detection sensitivity across different pregnancy stages while reducing false alarms that would lead to alert fatigue and information loss
3Speed
If medical practitioners are contacted immediately for every symptom report, then responsiveness to potential risks is improved, but the device complexity and resource requirements deteriorate
Solution Approach 1:
The system segments the response pathway by creating distinct handling paths for different symptom patterns. Normal pregnancy symptoms follow a routine monitoring path, while symptom patterns matching predefined risk criteria trigger immediate practitioner alerts. This segmentation enables rapid response to genuine risks while simplifying the system by avoiding unnecessary practitioner contact for normal symptoms
Solution Approach 2:
The system introduces an intermediary layer of automated symptom pattern analysis and risk assessment algorithms between symptom reporting and practitioner notification. This intermediary automatically evaluates reported symptoms against risk criteria, filtering out normal variations and only escalating genuine risks to practitioners, thereby maintaining rapid response capability while reducing system complexity and resource requirements
Data Source
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AI summary
A computer-implemented system and method are for alerting an expectant mother to a medical risk during pregnancy. A profile of the expectant mother is used, and reports are received from the expectant mother identifying experienced symptoms. In response to a report at a particular time, any reports received over a subsequent time window are monitored, and based on the combination of reports received during the time window and the profile of the expectant mother, the need for a risk alert is determined. The user is more willing to report symptoms, because a risk alert (which functions as reporting symptoms to a medical expert) only takes place when a real risk is identified.