The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea

Kyoung Ja Moon; Chang-Sik Son; Jong-Ha Lee; Mina Park

doi:10.1186/s12911-022-01966-8

The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea

BMC Med Inform Decis Mak. 2022 Aug 17;22(1):220. doi: 10.1186/s12911-022-01966-8.

Authors

Kyoung Ja Moon¹, Chang-Sik Son², Jong-Ha Lee³, Mina Park⁴

Affiliations

¹ College of Nursing, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu, 42601, South Korea. kjmoon2150@gmail.com.
² Division of Intelligent Robots, Daegu Gyeongbuk Institute of Science and Technology (DGIST), 333, Techno jungang-daero, Hyeonpung-eup, Dalseong-gun, Daegu, South Korea.
³ College of Medicine, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu, 42601, South Korea.
⁴ College of Nursing, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu, 42601, South Korea.

Abstract

Background: Long-term care facilities (LCFs) in South Korea have limited knowledge of and capability to care for patients with delirium. They also often lack an electronic medical record system. These barriers hinder systematic approaches to delirium monitoring and intervention. Therefore, this study aims to develop a web-based app for delirium prevention in LCFs and analyse its feasibility and usability.

Methods: The app was developed based on the validity of the AI prediction model algorithm. A total of 173 participants were selected from LCFs to participate in a study to determine the predictive risk factors for delerium. The app was developed in five phases: (1) the identification of risk factors and preventive intervention strategies from a review of evidence-based literature, (2) the iterative design of the app and components of delirium prevention, (3) the development of a delirium prediction algorithm and cloud platform, (4) a pilot test and validation conducted with 33 patients living in a LCF, and (5) an evaluation of the usability and feasibility of the app, completed by nurses (Main users).

Results: A web-based app was developed to predict high risk of delirium and apply preventive interventions accordingly. Moreover, its validity, usability, and feasibility were confirmed after app development. By employing machine learning, the app can predict the degree of delirium risk and issue a warning alarm. Therefore, it can be used to support clinical decision-making, help initiate the assessment of delirium, and assist in applying preventive interventions.

Conclusions: This web-based app is evidence-based and can be easily mobilised to support care for patients with delirium in LCFs. This app can improve the recognition of delirium and predict the degree of delirium risk, thereby helping develop initiatives for delirium prevention and providing interventions. Moreover, this app can be extended to predict various risk factors of LCF and apply preventive interventions. Its use can ultimately improve patient safety and quality of care.

Keywords: Clinical decision support system; Delirium; Long-term care facility; Mobile apps; Rule-based prediction.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Delirium* / diagnosis
Delirium* / prevention & control
Humans
Internet
Long-Term Care
Machine Learning
Mobile Applications*
Republic of Korea