Appointment Duration Prediction for Provider-Specific Scheduling
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Solution Overview
Problem
Existing scheduling systems struggle to accurately determine appointment durations based on the activities involved, leading to inefficiencies and inaccuracies in scheduling processes.
Innovation Solution
A machine learning-based system that analyzes historical data to predict appointment durations by considering the activities, timing, and provider attributes, and iteratively updates the model using actual appointment data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scheduling systems use fixed or manual methods to determine appointment durations, then the scheduling process is simple to implement, but the accuracy and efficiency of appointment duration determination deteriorates
Solution Approach 1:
The patent replaces manual or rule-based scheduling methods with an artificial intelligence system that uses machine learning models to automatically determine appointment durations. The AI system analyzes historical data, activity types, and provider characteristics to predict optimal durations, substituting mechanical decision-making processes with intelligent algorithms that continuously learn and improve accuracy.
Solution Approach 2:
The scheduling system performs self-optimization by automatically collecting data from actual appointments and using this information to train and refine its machine learning models. The system autonomously improves its duration prediction accuracy over time without requiring external intervention, continuously adapting to new patterns and provider-specific characteristics.
2Adaptability or versatility
If scheduling systems use standardized appointment durations for all providers, then the system is easy to operate, but the adaptability to individual provider needs and activity variations deteriorates
Solution Approach 1:
The patent implements provider-specific customization by training separate machine learning models for each healthcare provider based on their individual historical appointment data. Each model learns the unique patterns, activity types, and time requirements specific to that provider, enabling highly adapted duration recommendations rather than applying uniform standards across all providers.
Solution Approach 2:
The system dynamically adapts appointment duration recommendations based on real-time factors including the specific activities scheduled, provider characteristics, and historical performance data. The machine learning models continuously update their predictions based on actual appointment outcomes, making the system flexible and responsive to changing conditions rather than relying on static, pre-defined duration tables.
3Measurement precision
If scheduling systems collect and analyze extensive historical data to improve duration predictions, then the prediction accuracy improves, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing and model training in advance by continuously learning from historical appointment data as it becomes available. The machine learning models are trained incrementally on accumulated data, so when predictions are needed, the system already has pre-processed patterns and insights ready, reducing the computational burden at the moment of prediction.
Solution Approach 2:
The patent uses historical appointment data as training examples to create simplified representations of optimal duration patterns. The machine learning models capture essential relationships from extensive historical data and store them as learned parameters, allowing the system to make accurate predictions using compact model structures rather than re-processing raw historical data for each prediction.
Data Source
AI summary
Techniques are disclosed that determine time allotments for appointments based on activities to be performed. A system determines a time allotment for an appointment by applying a machine learning model trained to calculate a duration for performing a set of activities involved in the appointment. The machine learning model may calculate the duration based on the set of activities, the timing of the appointment, and attributes of the provider.