Hospital Discharge Optimization Using Historical Flow Models
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Solution Overview
Problem
Hospitals face significant discharge delays due to complex modeling challenges in anticipating bed requirements, leading to inefficient patient discharge practices that increase emergency department boarding times, transfer waiting times, and overall patient length-of-stay, adversely impacting medical outcomes.
Innovation Solution
A discharge-optimization system that accesses historical patient flow data, converts it into standardized format, generates models to forecast optimal discharge timetables, and updates them in real-time based on deviations, providing informed discharge decisions to minimize bottlenecks and improve bed availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual discharge scheduling is used, then simplicity of operation is maintained, but patient discharge delays increase and bed availability decreases
Solution Approach 1:
The patent replaces manual discharge scheduling with an automated computer-based optimization system that processes patient data, forecasts bed requirements, and generates discharge timetables algorithmically, eliminating the inefficiencies of manual scheduling and significantly improving discharge rates while reducing delays
Solution Approach 2:
The system enables self-service through automated real-time adjustments where the optimization algorithm continuously monitors patient flow and discharge compliance, automatically updating timetables without requiring manual intervention from hospital staff, thereby maintaining high productivity while minimizing time loss
2Measurement precision
If complex forecasting models are implemented, then discharge timing accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the forecasting challenge into distinct components: historical data analysis, bed requirement prediction, discharge timing optimization, and real-time compliance monitoring. Each module handles a specific aspect independently, improving overall accuracy while managing complexity through modular design
Solution Approach 2:
The system introduces an intermediary optimization algorithm that translates complex forecasting data into actionable discharge timetables, acting as a bridge between raw data and clinical decision-making, thereby achieving high precision without directly exposing the complexity of underlying models to users
3Adaptability or versatility
If real-time timetable updates are implemented, then bed availability responsiveness improves, but operational complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors actual discharge times against predicted timetables, automatically detecting deviations and triggering real-time timetable adjustments. This closed-loop feedback enables responsive bed availability management while automating the complexity of real-time updates, reducing operational burden on staff
Solution Approach 2:
The system transitions from static discharge schedules to dynamic timetables that automatically adapt to changing patient flow conditions. The optimization algorithm continuously recalibrates discharge timing based on real-time data, enabling the system to respond flexibly to bed availability changes without requiring manual reconfiguration
Data Source
AI summary
Examples of the disclosure include a method of operating a patient-discharge-optimization system to improve medical outcomes, the method comprising: accessing, from memory and/or storage, historical patient flow information including a plurality of timestamped events for each of a plurality of patients; converting, by the discharge-optimization system, the historical patient flow information into standardized flow information having a standardized format defined by a plurality of standardized parameters; generating, by the discharge-optimization system, a plurality of models, each model indicating an effect of modifying at least one parameter of the standardized parameters for each of the patients; deriving, by the discharge-optimization system and based on the models, an optimal discharge histogram for one or more days; forecasting, based on the historical patient flow information, an anticipated future number of discharges for the day(s); deriving an optimal patient discharge timetable for the day(s); and providing the optimal patient discharge timetable to users.