Smart Work Injury Management (SWIM) System: Artificial Intelligence in Work Disability Management

Cheng, Andy S. K.; Ng, Peter H. F.; Sin, Zackary P. T.; Lai, Sun H. S.; Law, Sheung Wai

doi:10.1007/s10926-020-09886-y

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…The effectiveness of the BFL-GO method for disability management in information systems and the results achieved from the study are demonstrated in this section. The BFL-GO method was evaluated with various evaluation measures, namely specificity, recall, F1-score, accuracy, and precision, and the results were compared with existing methods such as SWIM [11], UM-PMS [12], FMCS [13], and FEMIS [14].…”

Section: Resultsmentioning

confidence: 99%

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Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Khan,

Alojail,

Al Moteri

2023

Mathematics

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Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as privacy concerns, high cost, and accessibility issues. To overcome these issues, this paper proposed a novel method named bidirectional federated learning-based Gradient Optimization (BFL-GO) for disability management in information systems. In this study, bidirectional long short-term memory (Bi-LSTM) was utilized to capture sequential disability data, and federated learning was employed to enable training in the BFL-GO method. Also, gradient-based optimization was used to adjust the proposed BFL-GO method’s parameters during the process of hyperparameter tuning. In this work, the experiments were conducted on the Disability Statistics United States 2018 dataset. The performance evaluation of the BFL-GO method involves analyzing its effectiveness based on evaluation metrics, namely, specificity, F1-score, recall, precision, AUC-ROC, computational time, and accuracy and comparing its performance against existing methods to assess its effectiveness. The experimental results illustrate the effectiveness of the BFL-GO method for disability management in information systems.

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Section: Resultsmentioning

confidence: 99%

“…Cheng et al [11] introduced AI in work disability management using a smart work injury management system (SWIM). SWIM was an established, safe cloud platform with some operational devices for data storage and machine learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Khan,

Alojail,

Al Moteri

2023

Mathematics

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“…In Hong Kong, several computer assisted therapy softwares have been developed and studied [15,16]. Among them, the Smart Work Injury Management (SWIM) system is one of the CDMS tools that uses arti cial intelligence to predict RTW trajectory and to provide advice on medical care and RTW interventions [17]. SWIM is a cloud platform containing a set of management tools for data storage, data analytics, and machine learning [18].…”

Section: Clinical Decision-making Support Toolsmentioning

confidence: 99%

Usability testing of the Smart Work Injury Management (SWIM) system to assist case managers in predicting the work disability of injured workers

Yeung,

Chen,

et al. 2024

Preprint

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Purpose Many countries have developed clinical decision-making support (CDMS) tools, such as the Smart Work Injury Management (SWIM) system in Hong Kong, to predict rehabilitation paths and address global issues related to work injury disability. This study aims to evaluate the accuracy of SWIM by comparing its predictions on real work injury cases to those made by human case managers, specifically with regard to the duration of sick leave and the percentage of permanent disability. Methods The study analysed a total of 442 work injury cases covering the period from 2012 to 2020, dividing them into non-litigated and litigated cases. The Krustal-Wallis post hoc test with Bonferroni adjustment was used to evaluate the differences between the actual data, the SWIM predictions, and the estimations made by three case managers. The intra-class correlation coefficient (ICC) was used to assess the inter-rater reliability of the raters. Results The Krustal-Wallis test revealed a statistically significant similarity between the predictions of SWIM and a case manager with about four years of case management experience on non-litigated cases. The predictions made by case managers with 9 and 20 years of experience were also significantly similar for all cases. However, SWIM’s prediction of the permanent disability percentage differed significantly from the predictions of the other groups. Conclusions The study confirmed that SWIM could effectively simulate the predictions of a case manager with roughly four years of case management experience regarding sick leave duration. It is recommended that future research considers the influence of psycho-social factors on predictions as these are often factored in by experienced case managers. Implications SWIM can aid case managers in estimating non-litigated cases, thereby addressing the shortage of human resources in Hong Kong and reducing inconsistencies in case estimations.

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“…IoT-based systems can be used to make the workplace more responsive and reduce hazards, as they provide quick information exchange and communication to achieve smart recognition, position and tracing (Patel and Keyur, 2016). Green technology coupled with digital technologies is further augmented by the adoption of artificial intelligence (AI), contributing to reduced workplace accidents and less human intervention in the system (Cheng et al , 2020; Kaswan et al , 2022). The adoption of other smart technologies, such as 3D printing, further supplements GLSS’s pursuits to realize a product with reduced environmental damage (Spears and Gold, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Green Lean Six Sigma for sustainability improvement: a systematic review and future research agenda

Yadav

Kaswan

Gahlot

et al. 2023

IJLSS

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Purpose The main purpose of this study is to explore different aspects of the Green Lean Six Sigma (GLSS) approach, application status and potential benefits from a comprehensive review of the literature and provide an avenue for future research work. This study also provides a conceptual framework for GLSS. Design/methodology/approach To do a systematic analysis of the literature, a systematic literature review methodology has been used in this research work. From the reputed databases, 140 articles were identified to explore hidden aspects of GLSS. Exploration of articles in different continents, year-wise, approach-wise and journal-wise was also done to find the execution status of GLSS. Findings This study depicts that GLSS implementation is increasing year by year, and it leads to considerable improvement in all dimensions of sustainability. Enablers, barriers, tools and potential benefits that foster the execution of GLSS in industrial organizations are also identified based on a systematic review of the literature. Originality/value The study’s uniqueness lies in that, to the best of the authors’ knowledge, this study is the first of its kind that depicts the execution status of GLSS, and its different facets, explores different available frameworks and provides avenues for potential research in this area for potential researchers and practitioners.

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Smart Work Injury Management (SWIM) System: Artificial Intelligence in Work Disability Management

Cited by 13 publications

References 19 publications

Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Usability testing of the Smart Work Injury Management (SWIM) system to assist case managers in predicting the work disability of injured workers

Green Lean Six Sigma for sustainability improvement: a systematic review and future research agenda

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