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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

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Published on: November 24, 2015

An adaptive scheduling model for a multi-agent based VEPR data collection actions.

Pedro Vieira-Marques¹, Jorge Jácome¹, José Hilário-Patriarca¹

¹CINTESIS - Center for Research in Health Technologies and Information Systems. Faculty of Medicine, University of Porto, Porto, Portugal.

Studies in Health Technology and Informatics

|May 21, 2015

Summary

This summary is machine-generated.

A new adaptive model improves access to hospital data by adjusting information retrieval frequency. This enhances the Virtual Electronic Patient Record (VEPR) system, reducing delays and minimizing system load.

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Area of Science:

Health Informatics
Computer Science
Medical Information Systems

Background:

Accessing legacy departmental information systems in hospitals is challenging.
Current systems often use static intervals for data retrieval, which is inefficient.
The Virtual Electronic Patient Record (VEPR) system aims to improve data accessibility.

Purpose of the Study:

To develop and test an adaptive model for optimizing data retrieval frequency in a VEPR system.
To reduce the average time for making new data available to users.
To minimize the number of requests made to departmental systems.

Main Methods:

Developed an adaptive model considering past report production profiles.
Tested the model in a simulated environment using real hospital data.
The model dynamically adjusts query frequency based on observed data production rates.

Main Results:

The adaptive model demonstrated the ability to adjust query frequency based on variable data production.
Simulations showed a reduction in the average time to make data available.
The adaptive approach minimized the number of departmental system requests compared to static intervals.

Conclusions:

An adaptive model can significantly improve the efficiency of VEPR systems.
Dynamic query frequency adjustment is effective in handling variable data production rates.
Optimized data retrieval enhances healthcare information accessibility and system performance.