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Nursing Clinical Information System

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Updated: Oct 4, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model.

Wenjing Lu¹, Wei Jiang¹, Na Zhang¹

¹Nursing Department, The Second Affiliated Hospital of Air Force Military Medical University, Xi'an 710038, China.

Journal of Healthcare Engineering

|February 7, 2022

Summary

This study introduces a new caregiver association method to improve nursing medical history accuracy and reduce errors. The proposed system optimizes caregiver selection, enhancing patient care quality and system efficiency.

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Area of Science:

Nursing Informatics
Decision Science
Healthcare Management

Background:

Inaccurate nursing medical histories contribute to rising medical errors.
Erroneous falsification of nursing records presents a significant challenge in healthcare.

Purpose of the Study:

To propose a novel caregiver association method for intelligent caregiving decision-making.
To address the issue of inaccurate nursing medical histories and reduce medical errors.

Main Methods:

A multi-attribute decision model-based caregiver association method was developed.
Key performance indicators were selected and modeled, with combined weights designed to prioritize attributes.
A comprehensive evaluation index was created for joint multi-attribute optimization.

Main Results:

The proposed nursing workstation effectively balances nursing staff experience and system performance.
The system achieves a balanced distribution of nursing work, reducing association delays and energy consumption.
Simulation results demonstrate the efficacy of the proposed method in improving nursing record accuracy.

Conclusions:

The developed caregiver association method offers a robust solution for intelligent caregiving.
This approach enhances the quality of nursing care by minimizing errors and optimizing resource allocation.
The system contributes to more efficient and reliable healthcare delivery through improved decision-making.