Evaluation of the Accuracy of the Smart Work Injury Management (SWIM) System to Assist Case Managers in Predicting the Work Disability of Injured Workers.

PURPOSE: Many countries have developed clinical decision-making support 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 analyzed a total of 442 work injury cases covering the period from 2012 to 2020, dividing them into non-litigated and litigated cases. The Kruskal-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 was used to assess the inter-rater reliability of the case managers. RESULTS: The study discovered that the predictions made by the SWIM model and a case manager possessing approximately 4 years of experience in case management exhibited moderate reliability in non-litigated cases. Nevertheless, there was no resemblance between SWIM's predictions regarding the percentage of permanent disability and those made by case managers. CONCLUSION: The findings indicate that SWIM is capable of replicating the sick leave estimations made by a case manager with an estimated 4 years of case management experience, albeit with limitations in generalizability owing to the small sample size of case managers involved in the study. IMPLICATIONS: These findings represent a significant advancement in enhancing the accuracy of CDMS for work injury cases in Hong Kong, signaling progress in the field.

Unmet needs of 1210 Chinese breast cancer survivors and associated factors: a multicentre cross-sectional study.

BACKGROUND: Breast cancer survivors (BCSs) often have potential unmet needs. Identification of the specific needs of BCSs is very significant for medical service provision. This study aimed to (1) investigate the unmet needs and quality of life (QoL) of BCSs in China, (2) explore the diverse factors associated with their unmet needs, and (3) assess the association between their unmet needs and QoL. METHODS: A multicentre, cross-sectional survey was administered to 1210 Chinese BCSs. The Cancer Survivor Profile-Breast Cancer and the Functional Assessment of Cancer Therapy-Breast scale were administered to survivors who gave informed consent to participate. Data were analysed using t-test, ANOVA, multiple regression analysis, and Pearson correlations. RESULTS: The 1192 participants completed questionnaires (response rate 98.51%). Our study reveals that the most prevalent unmet needs were in the 'symptom burden domain'. The unmet needs of BCSs depend on eleven factors; age, time since diagnosis, education level, occupation, payment, family income status, stage of cancer, treatment, family history of cancer, pain, and physical activities. To ensure the provision of high-quality survivorship care and a high satisfaction level, more attention should be paid to actively identifying and addressing the unmet needs of BCSs. The problem areas identified in the Cancer Survivor Profile for breast cancer were negatively associated with all subscales of QoL except the health behaviour domain, with the correlation coefficient ranging from - 0.815 to - 0.011. CONCLUSION: Chinese BCSs exhibit a high demand for unmet needs in this study, and the most prevalent unmet needs were in the 'symptom burden domain'. There was a significant association between patients' unmet needs (as defined in the Cancer Survivor Profile for breast cancer) and QoL. Future research should focus on enhancements to survivorship or follow-up care to address unmet needs and further improve QoL.

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

PURPOSE: This paper aims to illustrate an example of how to set up a work injury database: the Smart Work Injury Management (SWIM) system. It is a secure and centralized cloud platform containing a set of management tools for data storage, data analytics, and machine learning. It employs artificial intelligence to perform in-depth analysis via text-mining techniques in order to extract both dynamic and static data from work injury case files. When it is fully developed, this system can provide a more accurate prediction model for cost of work injuries. It can also predict return-to-work (RTW) trajectory and provide advice on medical care and RTW interventions to all RTW stakeholders. The project will comprise three stages. Stage one: to identify human factors in terms of both facilitators and barriers RTW through face-to-face interviews and focus group discussions with different RTW stakeholders in order to collect opinions related to facilitators, barriers, and essential interventions for RTW of injured workers; Stage two: to develop a machine learning model which employs artificial intelligence to perform in-depth analysis. The technologies used will include: 1. Text-mining techniques including English and Chinese work segmentation as well as N-Gram to extract both dynamic and static data from free-style text as well as sociodemographic information from work injury case files; 2. Principle component/independent component analysis to identify features of significant relationships with RTW outcomes or combine raw features into new features; 3. A machine learning model that combines Variational Autoencoder, Long and Short Term Memory, and Neural Turning Machines. Stage two will also include the development of an interactive dashboard and website to query the trained machine learning model. Stage three: to field test the SWIM system. CONCLUSION: SWIM ia secure and centralized cloud platform containing a set of management tools for data storage, data analytics, and machine learning. When it is fully developed, SWIM can provide a more accurate prediction model for the cost of work injuries and advice on medical care and RTW interventions to all RTW stakeholders. ETHICS: The project has been approved by the Ethics Committee for Human Subjects at the Hong Kong Polytechnic University and is funded by the Innovation and Technology Commission (Grant # ITS/249/18FX).

Smart Work Injury Management (SWIM) System: A Machine Learning Approach for the Prediction of Sick Leave and Rehabilitation Plan.

As occupational rehabilitation services are part of the public medical and health services in Hong Kong, work-injured workers are treated along with other patients and are not considered a high priority for occupational rehabilitation services. The idea of a work trial arrangement in the private market occurred to meet the need for a more coordinated occupational rehabilitation practice. However, there is no clear service standard in private occupational rehabilitation services nor concrete suggestions on how to offer rehabilitation plans to injured workers. Electronic Health Records (EHRs) data can provide a foundation for developing a model to improve this situation. This project aims at using a machine-learning-based approach to enhance the traditional prediction of disability duration and rehabilitation plans for work-related injury and illness. To help patients and therapists to understand the machine learning result, we also developed an interactive dashboard to visualize machine learning results. The outcome is promising. Using the variational autoencoder, our system performed better in predicting disability duration. We have around 30% improvement compared with the human prediction error. We also proposed further development to construct a better system to manage the work injury case.

Effectiveness of virtual reality-based interventions in rehabilitation management of breast cancer survivors: protocol of a systematic review and meta-analysis.

INTRODUCTION: Breast cancer is the leading cause of global cancer incidence and represents 11.7% of all new cancer cases. However, breast cancer survivors (BCS) suffer from many intense physical and psychological symptoms, functional deficits and adverse effects during and after treatment, significantly affecting their quality of life. Virtual reality (VR) technology uses computer technology to create an interactive three-dimensional world by visual, audio and touch simulation and is being used in breast cancer rehabilitation management. This paper reports on the protocol for a systematic review and meta-analysis exploring the efficacy of VR-based interventions in the rehabilitation management of BCS. METHODS AND ANALYSIS: This protocol for conducting a systematic review and meta-analysis was prepared according to the recommendations of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 statement. Web of Science, PubMed, Embase, CINAHL, Cochrane Central Register of Controlled Trials, CNKI, Wanfang, VIP and SinoMed will be used in the search. The search will include randomised controlled trials, quasi-experimental studies and case-controlled trials published in English and Chinese. Further, the risk of bias of the studies included in the systematic review and meta-analysis will be assessed using the Cochrane risk-of-bias tool. The statistical program Review Manager V.5.3 will be used in the meta-analysis. The I² test will be used to determine statistical heterogeneity among the included studies. ETHICS AND DISSEMINATION: Ethics approval will not be needed because the data to be used in this systematic review and meta-analysis will be extracted from published studies. The systematic review and meta-analysis will focus on whether VR-based interventions are effective in the rehabilitation management of BCS. It will be disseminated by publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42021250727.

Breast Cancer Stigma Scale: A Reliable and Valid Stigma Measure for Patients With Breast Cancer.

Purpose: This study aims to develop and validate a stigma scale for Chinese patients with breast cancer. Methods: Patients admitted to the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, for breast cancer treatment participated in this study. Development of the Breast Cancer Stigma Scale involved the following procedures: literature review, interview, and applying a theoretical model to generate items; the Breast Cancer Stigma Scale's content validity was assessed by a Delphi study (n = 15) and feedback from patients with breast cancer (n = 10); exploratory factor analysis (n = 200) was used to assess the construct validity; convergent validity was assessed with the Social Impact Scale (n = 50); internal consistency Cronbach's α (n = 200), split-half reliability (n = 200), and test-retest reliability (N = 50) were used to identify the reliability of the scale. Results: The final version of the Breast Cancer Stigma Scale consisted of 15 items and showed positive correlations with the Social Impact Scale (ρ = 0.641, P < 0.001). Exploratory factor analysis (EFA) revealed four components of the Breast Cancer Stigma Scale: self-image impairment, social isolation, discrimination, and internalized stigma, which were strongly related to our perceived breast cancer stigma model and accounted for 69.443% of the total variance. Cronbach's α for the total scale was 0.86, and each subscale was 0.75-0.882. The test-retest reliability with intra-class correlation coefficients of the total scale was 0.947 (P < 0.001), and split-half reliability with intra-class correlation coefficients of the total scale was 0.911 (P < 0.001). The content validity index (CVI) was 0.73-1.0. Conclusion: The newly developed Breast Cancer Stigma Scale offers a valid and reliable instrument for assessing the perceived stigma of patients with breast cancer in clinical and research settings. It may be helpful for stigma prevention in China.

Breast cancer application protocol: a randomised controlled trial to evaluate a self-management app for breast cancer survivors.

INTRODUCTION: The eHealth technologies that are being designed for chronic disease constitute a global trend towards health assessment and self-management. However, most of these approaches tend to focus on a single symptom or problem rather than on the multiple problems that are characteristic of many of these chronic illnesses. The aim of this study is to examine the effectiveness of and adherence to a self-management application (app) that identifies multiple problem areas related to surviving breast cancer as the targeted chronic illness. METHODS AND ANALYSIS: This is a randomised controlled study. Eligible participants will be allocated randomly into either an intervention group or a control group at a 1:1 ratio. The intervention group will be assigned to the self-management app ('Be-with-You'), while the control group will use a general health app ('Sham' app). The primary outcomes will include the differences between the two groups in their health literacy, problem-solving skills and self-management skills. The secondary outcomes will include group differences in self-efficacy, readiness for change and health-related quality of life. All of these outcomes will be measured at baseline and at 4 weeks and 12 weeks after intervention. In addition, usability of these two mobile apps will be measured at 4 weeks and 12 weeks after intervention. The planned sample size is 476. ETHICS AND DISSEMINATION: The Human Subjects Ethics Sub-committee of The Hong Kong Polytechnic University approved the study (HSEARS20190922001, 24 September 2019). Dissemination of findings will occur at the local, national and international levels. TRIAL REGISTRATION NUMBER: ChiCTR1900026244.