The use of healthcare data analytics is anticipated to play a significant role in future public health policy formulation. Therefore, this study examines how big data analytics (BDA) may be methodically incorporated into various phases of the health policy cycle for fact-based and precise health policy decision-making. So, this study explores the potential of BDA for accurate and rapid policy-making processes in the healthcare industry. A systematic review of literature spanning 22 years (from January 2001 to January 2023) has been conducted using the PRISMA approach to develop a conceptual framework. The study introduces the emerging topic of BDA in healthcare policy, goes over the advantages, presents a framework, advances instances from the literature, reveals difficulties and provides recommendations. This study argues that BDA has the ability to transform the conventional policy-making process into data-driven process, which helps to make accurate health policy decision. In addition, this study contends that BDA is applicable to the different stages of health policy cycle, namely policy identification, agenda setting as well as policy formulation, implementation and evaluation. Currently, descriptive, predictive and prescriptive analytics are used for public health policy decisions on data obtained from several common health-related big data sources like electronic health reports, public health records, patient and clinical data, and government and social networking sites. To effectively utilize all of the data, it is necessary to overcome the computational, algorithmic and technological obstacles that define today's extremely heterogeneous data landscape, as well as a variety of legal, normative, governance and policy limitations. Big data can only fulfill its full potential if data are made available and shared. This enables public health institutions and policymakers to evaluate the impact and risk of policy changes at the population level.

Background: Mental illness is a disease that affects millions of people every year. It not only causes stress to the mentally ill patients, but also for the family members who provide them the care. The family caregivers, therefore need some form of coping strategies in dealing with their mentally ill family members. Aims: This qualitative study aims at identifying and analysing the coping strategies adopted by the family caregivers in dealing with their mentally ill family members. Method: A total of 15 family caregivers from the state of Kedah, Malaysia participated in the face-to-face semi structured interview. Results: The study findings identified an array of coping strategies used by the family caregivers, including religious coping, emotional coping, acceptance, becoming engaged in leisure activities, and the use of traditional healing to help them cope with their mentally ill members. Suggestions and conclusions: Study suggests that the family caregivers should engage themselves in social support groups to learn about and obtain the positive coping strategies used by other caregivers who have similar experiences in caring for the mentally ill. Study also suggests that they should get appropriate training from the mental health professionals in order to enhance the caregivers’ coping skills.

The impact of mental illness is profound. This qualitative study aims to explore the impact experienced by family caregivers who provide care to relatives with mental illness. A total of 15 family caregivers from the state of Kedah in Malaysia participated in this study. The findings indicated that the family caregivers experienced four types of negative impact, including financial burdens and social, psychological and physical health. This study suggests that the family caregivers should engage in support groups to obtain emotional support and the required information from other caregivers who have similar experiences in caring for individuals with mental illness.

Purpose: Advanced medical technology has reduced the mortality rate among traumatic brain injury (TBI) patients. This, however, has led to an increasing number of surviving patients with a major disability. As a consequence, these patients need attentive care which becomes an important issue for the society, particularly family members. Thus, this paper aims to review some of the salient roles, challenges and needs of the family caregivers in caring or nursing for their family members diagnosed with TBI.Design/methodology/approach: An inclusive search of the literature was undertaken to identify the family roles, challenges and needs in supporting and nursing TBI patients.Findings: Previous studies have shown that the family needs to address two important aspects of taking care of TBI patients, which involve emotional and physical affairs. Hence, it is essential for the family members to have adequate information on healing treatment, nursing and care methods, financial support, support groups, managing self-care and, more importantly, emotional and social support.Originality/value: This paper is not currently under consideration, in press or published elsewhere. In Malaysian culture, nursing disabled patients have always been a family responsibility. The role of nursing the patients has been done domestically and is considered a private affair. In order to execute the role, some put the patient needs as their priority and leave aside their needs and matters.

A sudden blow or jolt to the human brain called traumatic brain injury (TBI) is one of the most common injuries recorded in the health insurance claim. Generally, computed tomography (CT) or magnetic resonance imaging (MRI) is required to identify the trauma's severity. Unfortunately, CT and MRI equipment are bulky, expensive, and not always available, limiting their use in TBI detection. Therefore, as an alternative, this study presents a novel classification architecture that can classify non-severe TBI patients from healthy subjects by using resting-state electroencephalogram (EEG) as the input. The proposed architecture employs a convolutional neural network (CNN), and error-correcting output codes support vector machine (ECOC-SVM) to perform automated feature extraction and multi-class classification. In this architecture, complex feature selection and extraction steps are avoided. The proposed architecture attained a high-performance classification accuracy of 99.76%, potentially being used as a classification approach to preventing healthcare insurance fraud. The proposed method is compared to existing studies in the literature. The outcome from the comparisons indicates that the proposed method has outperformed the benchmarked methods by presenting the highest classification accuracy and precision.

The global lockdown due to COVID-19 is a major concern as all higher educational institutions face disruption in teaching, learning and assessment. Social work educators in Malaysia’s higher educational institutions are not spared of this disruption. Conventional teaching methods are now being replaced by non-conventional modes of teaching, which include online teaching and assessment using various platforms such as Zoom, WebEx and others. In embarking on online methods of teaching, social work educators will have to undergo many changes. It is particularly so as social work has a practice component that involves field training, which will be a different challenge to educators and students in this new and unexpected environment. This paper aims to discuss the implications of COVID-19 on the changes that have taken place in social work teaching and learning in Malaysia and potential responses.