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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

Supporting medical planning by mitigating cognitive load.

D W Glasspool¹, A Oettinger, J H Smith-Spark

¹Advanced Computation Laboratory, Cancer Research UK, PO Box 123, London WC2A 3PX, United Kingdom. david.glasspool@cancer.org.uk

Methods of Information in Medicine

|December 11, 2007

Summary

This study introduces REACT, a software system designed to reduce cognitive load during patient care planning. Initial evaluations in genetic counseling show it effectively supports decision-making for complex care plans.

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Area of Science:

Medical Informatics
Cognitive Science
Health Systems Engineering

Background:

Patient care planning is complex, involving intricate procedure interactions and patient-specific outcomes.
Existing medical informatics tools often lack the comprehensive decision support needed for effective planning.
Understanding and mitigating cognitive load is crucial for optimizing the planning process.

Purpose of the Study:

To assess cognitive loads associated with patient care planning tasks.
To design software that directly supports and ameliorates these identified cognitive loads.
To develop a generalizable aid for visualizing, customizing, and evaluating patient care plans.

Main Methods:

Cognitive load assessment techniques were employed to identify key factors in planning tasks.
A planning support system, REACT, was developed based on these cognitive load assessments.
An initial evaluation study was conducted in the domain of genetic counseling.

Main Results:

Five distinct types of cognitive load were identified as significant during planning.
The REACT system was designed to provide targeted, dynamic feedback to reduce these loads.
The initial evaluation demonstrated the system's success in supporting genetic counseling planning.

Conclusions:

The developed approach offers a foundation for a versatile tool to aid in care plan management.
REACT's design principles can be applied to create generalizable decision support systems for healthcare planning.
The system facilitates visualization, customization, and evaluation of patient care plans.