Predictive Analytics Engine for Healthcare Workflow Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current healthcare systems face challenges in efficiently managing patient scheduling, leading to delays in diagnosis, idle resources, and missed opportunities due to siloed information systems and technological limitations, resulting in patient no-shows, underutilization of equipment, and logistical barriers.
Innovation Solution
The implementation of a predictive analytics engine that integrates artificial intelligence and machine learning to analyze historical patterns, weather data, and external factors to predict patient no-shows, optimize scheduling, and provide real-time insights for improved resource management and patient access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling systems are used, then system simplicity is maintained, but patient no-show rates remain high and resource utilization is poor
Solution Approach 1:
The patent introduces a predictive analytics engine as an intermediary layer between existing healthcare information systems (HIS, RIS, PACS, EMR) and scheduling operations. This engine aggregates data from multiple siloed systems, applies machine learning models to predict patient no-shows, and provides recommendations without replacing core systems, thereby improving resource utilization while maintaining relative system simplicity.
Solution Approach 2:
The predictive analytics engine serves multiple functions: it predicts patient no-shows, optimizes scheduling decisions, identifies high-value patients, and provides real-time insights for resource allocation. By consolidating these diverse functions into a single multi-functional platform, the system improves overall productivity without proportionally increasing complexity.
2Measurement precision
If data from multiple healthcare systems is integrated, then predictive accuracy improves, but data overload and system complexity increase
Solution Approach 1:
The patent extracts and isolates only the most relevant predictive features from vast healthcare data sources, such as historical attendance patterns, patient demographics, and appointment characteristics. By filtering out unnecessary data and focusing on key predictive indicators, the system achieves high prediction accuracy while managing data volume effectively.
Solution Approach 2:
The predictive analytics engine segments data processing into distinct modules: data aggregation from multiple sources, feature extraction and selection, machine learning model processing, and result generation. This segmentation allows the system to handle large data volumes systematically, processing only essential information for each predictive task.
3Productivity
If real-time predictive analytics are implemented, then scheduling optimization improves, but computational requirements and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and aggregating historical data before real-time prediction needs arise. Machine learning models are trained in advance on historical attendance patterns, and predictive frameworks are established beforehand, allowing the system to generate real-time predictions with reduced computational burden during actual scheduling operations.
Solution Approach 2:
The predictive analytics engine operates periodically, updating predictions at scheduled intervals rather than continuously. This periodic operation reduces computational resource consumption while still providing timely scheduling optimization, balancing real-time effectiveness with energy efficiency.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems, methods, and apparatus to generate and utilize predictive workflow analytics and inferencing are disclosed and described. An example apparatus includes memory circuitry including instructions and a plurality of artificial intelligence (AI) models; and processor circuitry to execute the instructions to implement at least: a smart scheduling engine to train at least one of the plurality of AI models, update at least one of the plurality of AI models, and inference a prediction using at least one of the plurality of AI models; and a smart scheduling application programming interface (API) to facilitate interaction with at least one of the plurality of AI models to trigger the prediction and to configure resources for an appointment based on the prediction.