Maternity Severity Index Identification System
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Solution Overview
Problem
Current systems for determining maternity risks are inadequate as they primarily focus on physical symptoms without considering behavioral and socio-economic factors, leading to delayed identification of high-risk pregnancies and increased maternal and fetal mortality.
Innovation Solution
A maternity severity index identification system that uses a computing unit to present queries to patients, eliciting responses on health, socio-economic, and behavioral states, and a backend server with a risk assessment module based on deep learning models to determine a patient's risk persona, predict maternity risks, and generate intervention strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only physical symptoms/tests are considered for determining high-risk pregnancies, then the determination process is simple, but the accuracy and comprehensiveness of risk identification is insufficient
Solution Approach 1:
The risk assessment is segmented into multiple dimensions: physical health factors, behavioral factors, and socio-economic factors. Each dimension is assessed separately through specific questions and tests, then integrated to form a comprehensive risk profile. This segmentation allows the system to handle complexity systematically while improving overall accuracy.
Solution Approach 2:
The assessment moves from a single-dimensional physical health evaluation to a multi-dimensional evaluation that incorporates behavioral patterns and socio-economic conditions. By adding these new dimensions, the system achieves more comprehensive risk identification without overwhelming complexity.
2Measurement precision
If comprehensive risk assessment including behavioral and socio-economic factors is implemented, then the accuracy of high-risk pregnancy identification is improved, but the complexity of the assessment process increases
Solution Approach 1:
The assessment tool is designed as a universal framework that can evaluate multiple risk dimensions through a single integrated system. The same platform handles physical health screening, behavioral pattern analysis, and socio-economic factor assessment, eliminating the need for separate complex evaluation systems for each dimension.
Solution Approach 2:
The system dynamically adjusts the number and type of assessment parameters based on the patient's responses and risk profile. Initially, comprehensive parameters are presented, but the system can adapt the depth of assessment in subsequent interactions, managing complexity while maintaining high accuracy.
3Adaptability or versatility
If manual management and validation of patient history data is performed, then the system can be customized for individual patients, but the workload and time consumption increase significantly
Solution Approach 1:
The system enables patients to self-enter and self-update their health information, behavioral patterns, and socio-economic data. This self-service approach reduces the manual workload significantly while maintaining the ability to customize assessments for individual patients through their own provided data.
Solution Approach 2:
The system automatically validates and cross-checks patient-provided data against established criteria and previous assessments, providing immediate feedback on data consistency and completeness. This automated feedback loop reduces manual validation time while ensuring data quality and customization accuracy.
4Stability of the object's composition
If risk factors are not updated regularly, then the system remains stable and manageable, but the accuracy of risk prediction deteriorates over time
Solution Approach 1:
The system continuously monitors and updates risk factors as patients provide new information throughout the pregnancy period. This continuous updating mechanism maintains prediction accuracy without requiring system redesign, as the same stable framework adapts to changing patient conditions through ongoing data collection.
Solution Approach 2:
While maintaining a stable core assessment framework, the system dynamically adjusts risk predictions based on new patient data. The stability of the underlying model is preserved while the outputs adapt in real-time, achieving both system stability and improving prediction accuracy over time.
Data Source
AI summary
Determining a maternity severity index of a subject user includes providing at least one input query on a state of a user. An output module elicits a response from the user to the input query. A data receiving component receives datasets pertaining to risks associated with maternity from data sources. A data repository, including a risk assessment database having sample maternity data parameter, and data-sets are constantly upgraded with parameters, assessments, and recommendation plans generated for other patients. A risk assessment module determines a current risk persona of the subject user categorized in the form of risk-based clusters based on a response of the user to the query, the received data-sets, and the sample maternity data parameters. A series of predicted maternity risk realizations are calculated by correlating the response of the user with the sample maternity data parameters. A risk report is generated which determines a maternity risk possibility.