Call Time Prediction System Using Machine Learning
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Solution Overview
Problem
Existing sample collection call time prediction systems lack accuracy due to operational changes, leading to uncertainty for patients waiting for sample collection, resulting in inefficient use of waiting time and potential congestion in waiting rooms.
Innovation Solution
A call time prediction system utilizing machine learning that considers reception time, sample type, patient classification, and number of waiting patients to provide accurate predicted call times, which are displayed to patients, allowing them to plan their waiting time effectively and reducing congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a typical sample collection assistance system is used without prediction functionality, then the system complexity remains low, but patient waiting time uncertainty increases and waiting room congestion occurs
Solution Approach 1:
The system performs preliminary calculation of predicted call times at patient reception, providing advance information before the patient enters the waiting room. This preliminary action reduces waiting time uncertainty without requiring complex real-time monitoring systems.
Solution Approach 2:
A prediction calculation unit acts as an intermediary between the reception management and patient information systems. This unit processes reception data and generates predicted call times, serving as a simple mediator that adds prediction functionality without significantly increasing overall system complexity.
2Measurement precision
If existing prediction systems are used, then some prediction capability is provided, but prediction accuracy decreases due to operational changes in sample collection tasks
Solution Approach 1:
The prediction calculation unit dynamically adjusts predictions based on current reception data and operational conditions. By continuously processing updated information about sample collection tasks and patient flow, the system adapts to operational changes while maintaining prediction accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where actual call times are compared with predicted times, and this information is used to refine future predictions. This feedback loop enables the system to adapt to operational changes and improve accuracy over time.
3Object-affected harmful factors
If patients wait in the waiting room without call time information, then no additional system resources are needed, but patient burden increases and infection risk rises
Solution Approach 1:
The system provides call time information in advance at the moment of patient reception, before the patient enters the waiting room. This preliminary provision of information allows patients to plan their wait and reduce time spent in crowded waiting areas, thereby reducing infection risk.
Solution Approach 2:
Patients receive personalized predicted call time information that enables them to self-manage their waiting experience. They can use this information to decide when to return to the waiting room or leave temporarily, reducing overall congestion and infection risk without requiring additional staff intervention.
Data Source
AI summary
Provided are a sample collection call time prediction system and a sample collection call time prediction method, with which it is possible to improve the accuracy of predicting the time at which a patient is called for sample collection. This call time prediction system includes a first processor that predicts, by machine learning, the time at which a patient is called for sample collection, the prediction being made on the basis of at least one of reception time information indicating the reception time for a patient from whom a sample is to be collected, sample type information indicating the type of the sample to be collected from the patient, a reception number indicating the order of reception of the patient, inpatient/outpatient classification information indicating whether the patient is an inpatient or an outpatient, and the number of waiting patients waiting to be called for sample collection at the reception time.