Census Forecasting Model Selection for Inpatient Units
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Solution Overview
Problem
Processing, searching, and analyzing vast amounts of unstructured data in a hospital environment is computationally expensive and time-consuming, making it difficult to extract insights effectively.
Innovation Solution
A system that includes a grouping component, a group stability component, a model selection component, and a patient census component, which defines groups of beds based on various factors, determines occupancy variability, selects appropriate census forecasting models, and applies these models to current patient flow data to forecast expected occupancy levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If unstructured data from multiple digital systems is processed and analyzed, then insights can be extracted, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent segments the monolithic data processing task into multiple parallel processing streams, each handling specific data types (census data, patient flow data, clinical data) through dedicated processing pipelines. This segmentation enables concurrent processing of different data sources, reducing overall processing time while maintaining comprehensive insight extraction.
Solution Approach 2:
The system performs preliminary data preprocessing, validation, and transformation before main analysis. Historical data is pre-aggregated and stored in optimized formats, and data quality issues are addressed in advance through automated cleaning routines. This preliminary action reduces the computational burden during real-time processing.
2Measurement precision
If multiple census forecasting models are selected based on occupancy variability, then forecast accuracy improves, but system complexity increases
Solution Approach 1:
The system dynamically selects and switches between different forecasting models based on real-time occupancy variability metrics. When variability is low, simpler models are used; when variability increases, more complex models are activated. This dynamic adaptation maintains high accuracy while avoiding unnecessary computational complexity in stable conditions.
Solution Approach 2:
The system changes operational parameters (model selection, processing intensity, aggregation levels) based on detected occupancy variability thresholds. This parameter-based adaptation allows the system to optimize between accuracy and complexity by adjusting its behavior according to current data characteristics rather than maintaining fixed complexity.
3Reliability
If real-time patient flow data is continuously monitored and analyzed, then census prediction accuracy improves, but computational resource consumption increases
Solution Approach 1:
The system implements periodic processing cycles where data is aggregated and analyzed at specific intervals rather than continuously. Real-time monitoring maintains readiness, but intensive computational analysis occurs periodically when sufficient data has accumulated. This approach maintains prediction accuracy while reducing peak computational resource consumption.
Solution Approach 2:
The system maintains continuous data collection and preliminary processing pipelines that prepare data for analysis without requiring continuous intensive computation. Useful actions (data validation, aggregation, transformation) continue continuously at low computational cost, enabling rapid analysis when needed while minimizing overall resource consumption.
Data Source
AI summary
Systems and techniques for monitoring, predicting and/or alerting for census periods in medical inpatient units are presented. A system can include a grouping component that defines a group of beds at a medical facility based on at least one grouping factor, and a group stability component that determines a measure of occupancy variability for the group based on historical census data for respective beds in the group. The system can further include a model selection component that selects one or more census forecasting models for the group based on the measure of occupancy variability, and a patient census component that applies the one or more census forecasting models to current patient flow data for the medical facility to forecast an expected occupancy level for the group during one or more future periods of time.