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Updated: Oct 19, 2025

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion

Published on: January 20, 2019

A mobile app for delirium screening.

Brett Armstrong¹, Daniel Habtemariam², Erica Husser³

¹University of New England College of Osteopathic Medicine, Biddeford, Maine, USA.

|September 22, 2021

Summary

This summary is machine-generated.

This study presents a mobile app for efficient delirium screening using adaptive testing. The app, developed with XHTML and JavaScript, showed high user satisfaction and potential for clinical integration.

Keywords:

2-step delirium protocol API JavaScript REDCap XHTML app delirium diagnosis

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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

Area of Science:

Gerontology
Medical Informatics
Digital Health

Background:

Delirium is a common, serious condition in hospitalized older adults.
Early and accurate diagnosis of delirium is crucial for patient outcomes.
Existing screening methods can be time-consuming and resource-intensive.

Purpose of the Study:

To describe the algorithm and technical implementation of a mobile application for delirium diagnosis.
To evaluate the feasibility, effectiveness, and administration time of a 2-step delirium screening protocol using a mobile app.
To assess user experience and app usability among healthcare professionals.

Main Methods:

Developed a mobile app using XHTML and JavaScript for iOS devices.
Linked the app to REDCap (Research Electronic Data Capture) via an API for data management.
Assessed 535 hospitalized patients using the app by physicians, nurses, and CNAs.
Collected qualitative data on user experience and app usability from 50 physicians, 189 nurses, and 83 CNAs.

Main Results:

The app was successfully implemented, facilitating instant data retrieval and updates.
Clinicians performed 881 delirium assessments with no data transmission errors.
User feedback was overwhelmingly positive (82%), with suggestions for improvement (18%).
Delirium screening administration time was comparable between nurses and physicians.

Conclusions:

The developed mobile app effectively operationalizes an adaptive 2-step delirium screening protocol.
The app's cross-platform code (XHTML, JavaScript) allows for easy adaptation to other operating systems and platforms.
The app has the potential to be implemented in clinical settings for widespread delirium screening in hospitalized older adults.