AI Ovarian Stimulation Workflow Optimization
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Solution Overview
Problem
Current methods for optimizing ovarian stimulation in IVF treatments are subjective and lack standardization, often relying on individual medical professional experience, and do not adequately consider prior patient data reliability or medical establishment workflow, leading to suboptimal egg outcomes and inefficient resource allocation.
Innovation Solution
The development of predictive models that use patient-specific data to optimize ovarian stimulation protocols, including predicting optimal medication doses and workflow management for medical establishments, to enhance egg retrieval outcomes and streamline treatment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If predictive models are used to standardize ovarian stimulation protocols, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary AI system that mediates between patient data and clinical decisions. The predictive model acts as a mediator that processes multiple input parameters (patient history, hormonal levels, follicle measurements) and outputs standardized protocol recommendations, resolving the contradiction by providing structured precision through a controlled intermediary system rather than direct complex interventions
Solution Approach 2:
The predictive model system is designed to perform multiple functions: predicting egg outcomes, optimizing stimulation protocols, scheduling workflows, and resource allocation. This multi-functionality allows a single system to address various aspects of IVF treatment standardization simultaneously, improving manufacturing precision across multiple process parameters while consolidating complexity into one universal tool
2Reliability
If prior patient data is extensively analyzed to improve prediction reliability, then reliability is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing patient data during initial consultations and previous treatment cycles. Historical data is stored and structured in advance, allowing the predictive model to quickly retrieve and analyze relevant information during actual treatment planning without time-consuming processing delays
Solution Approach 2:
The predictive model extracts only the most relevant features and parameters from extensive prior patient data rather than analyzing every available data point. By identifying and extracting key predictive variables (hormonal levels, follicle counts, previous response patterns), the system achieves high prediction reliability while minimizing data processing time through selective extraction
3Productivity
If medical establishment workflow is optimized using predictive models, then productivity is improved, but device complexity increases
Solution Approach 1:
The workflow optimization system is designed to be dynamic and adaptive rather than static. It continuously learns from actual clinic performance data and adjusts predictions and recommendations accordingly. This dynamic nature allows the system to improve productivity over time while managing complexity through adaptive algorithms that evolve with usage rather than requiring complex manual configuration
Solution Approach 2:
The system implements feedback loops where actual workflow outcomes and egg retrieval results are fed back into the predictive model. This feedback mechanism allows the system to self-optimize and improve productivity automatically, reducing the need for complex external management and allowing the system to manage its own complexity through learned patterns from real-world performance data
Data Source
AI summary
Systems and methods for implementing machine-learning models for optimizing medical workflow are described herein. In some variations, a computer-implemented method may include optimizing an ovarian stimulation workflow for a medical establishment having a group of patients. The methods may include making per-patient predictions for the group of patients, such as individual egg outcome predictions or predicting individual trigger day probability predictions, and, based on the per-patient predictions and one or more predictive models, making group predictions for the group of patients, such as a total number of eggs retrieved prediction or a total egg retrieval day probability prediction. The predictions may be made over a future timeframe such as a future day or set of future days. Also described herein are methods for optimizing ovarian stimulation for a patient, including predicting an optimal dose of ovarian stimulation to administer to the patient.