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Exposure to isocyanates was the leading cause of occupational asthma in the UK. Motor vehicle repair (MVR) bodyshop paint sprayers were at greatest risk, despite widespread use of air-fed breathing apparatus and ventilated booths. Most paint sprayers work in small and medium enterprises (SMEs). The purpose of the Health and Safety Executive (HSE) project, described in this paper, is to improve exposure control measures in at least 20 per cent of MVR bodyshops, and reduce the risk of occupational asthma. The paper aims to discuss this issue.

A three-stranded plan consisted of: Safety and Health Awareness Days (SHADs); workplace inspections; and third-party stakeholder communications. The impact of various parts of the project were evaluated.

Approximately 18 per cent of bodyshops in the UK attended one of 32 SHADs, following which over 90 per cent of delegates expressed an “intention to act” to improve exposure control measures. A local assessment showed that at least 50 per cent of bodyshops improved exposure control measures. An evaluation of 109 inspections found that enforcement action was taken at 40 per cent of visits. Third-party engagement produced a joint HSE-industry designed poster, new agreed guidance on spray booths and dissemination of SHAD material. Knowledge of booth clearance time has become widespread, and 85 per cent of booths now have pressure gauges. Biological monitoring data show that, post-SHAD, exposures were lower.

A sustained national project using clear, relevant, tested messages delivered via different routes, had a sector-wide impact in bodyshops. It is probable that the project has improved isocyanate exposure control in at least 20 per cent of bodyshops. The generic lessons could be applied to other widespread SME businesses.

This case study paper aims to explore the complexities and challenges of epidemic response and public health surveillance in Native American and Indigenous American communities in the United States and find viable solutions. This paper explores these topics through the emergence and impact of the hantavirus pulmonary syndrome (HPS) within the Navajo Nation in the United States using critical incident analysis and best practices.

This project is a case study paper based on a topical review of the literature. A topical review of the literature is a comprehensive exploration of the current body of knowledge within a particular research field. It is an important tool used by scholars and practitioners to further the development of existing knowledge as well as to identify potential directions for future research (Fourie, 2020). Such a paper can provide a useful insight into the various aspects of the process that the researcher may have overlooked, as well as highlighting potential areas of improvement (Gall et al., 2020). It can also provide a useful source of ideas and inspiration for the researcher as it can provide an overview of the various approaches used by other researchers in the field (Göpferich, 2009). Case study papers using a topical review of the literature have been used to help frame and inform research topics, problems and best practices for some time. They are typically used to explore a topic in greater depth and to provide an overview of the literature to improve the world of practice to provide a foundation for future comprehensive empirical research. Case study papers can provide research value by helping to identify gaps in the literature and by providing a general direction for further research. They can also be used to provide a starting point for research questions and hypotheses and to help identify potential areas of inquiry.

This study explores best practices in public health surveillance and epidemic response that can help strengthen public health infrastructure by informing the development of effective surveillance systems and emergency response plans, as well as improving data collection and analysis capabilities within Native American and Indigenous American communities in the United States that also have the option to include new technologies like artificial intelligence (AI) with similar outbreaks in the future.

The literature review did not include any primary data collection, so the existing available research may have limited the findings. The scope of the study was limited to published literature, which may not have reported all relevant findings. For example, unpublished studies, field studies and industry reports may have provided additional insights not included in the literature review. This research has significant value based on the limited amount of studies on how infectious diseases can severely impact Native American communities in the United States, leading to unnecessary and preventable suffering and death. As a result, research on viable best practices is needed on the best practices in public health surveillance and epidemic response in Native American and Indigenous American communities through historical events and critical incident analysis.

Research on public health surveillance and epidemic response in Native American communities can provide insights into the challenges faced by these communities and help identify potential solutions to improve their capacity to detect, respond to and prevent infectious diseases using innovative approaches and new technologies like AI.

More research on public health surveillance and epidemic response can inform policies and interventions to improve access to healthcare for Native American populations, such as increasing availability of healthcare services, providing culturally appropriate health education and improving communication between providers and patients. By providing better public health surveillance and response capacity, research can help reduce the burden of infectious diseases in Native American communities and ultimately lead to improved public health outcomes.

A policy of surveillance which interferes with the fundamental right to a private life requires credible justification and a supportive evidence base. The authority for such interference should be clearly detailed in law, overseen by a transparent process and not left to the vagaries of administrative discretion. If a state surveils those it governs and claims the interference to be in the public interest, then the evidence base on which that claim stands and the operative conception of public interest should be subject to critical examination. Unfortunately, there is an inconsistency in the regulatory burden associated with access to confidential patient information for non-health-related surveillance purposes and access for health-related surveillance or research purposes. This inconsistency represents a systemic weakness to inform or challenge an evidence-based policy of non-health-related surveillance. This inconsistency is unjustified and undermines the qualities recognised to be necessary to maintain a trustworthy confidential public health service. Taking the withdrawn Memorandum of Understanding (MoU) between NHS Digital and the Home Office as a worked example, this chapter demonstrates how the capacity of the law to constrain the arbitrary or unwarranted exercise of power through judicial review is not sufficient to level the playing field. The authors recommend ‘levelling up’ in procedural oversight, and adopting independent mechanisms equivalent to those adopted for establishing the operative conceptions of public interest in the context of health research to non-health-related surveillance purposes.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

The Chulalongkorn-Hasanuddin Rifampicin-Resistant Tuberculosis Screening Tool (CUHAS-ROBUST) is an artificial intelligence–based (AI–based) application for rifampicin-resistant tuberculosis (RR-TB) screening. This study aims to elaborate on the drug-resistant TB (DR-TB) problem and the impact of CUHAS-ROBUST implementation on RR-TB screening.

A qualitative approach with content analysis was performed from September 2020 to October 2020. Medical staff from the primary care center were invited online for application trials and in-depth video call interviews. Transcripts were derived as a data source. An inductive thematic data saturation technique was conducted. Descriptive data of participants, user experience and the impact on the health service were summarized

A total of 33 participants were selected from eight major islands in Indonesia. The findings show that DR-TB is a new threat, and its diagnosis faces obstacles particularly prolonged waiting time and inevitable delayed treatment. Despite overcoming the RR-TB screening problems with fast prediction, the dubious screening performance, and the reliability of data collection for input parameters were the main concerns of CUHAS-ROBUST. Nevertheless, this application increases the confidence in decision-making, promotes medical procedure compliance, active surveillance and enhancing a low-cost screening approach.

The CUHAS-ROBUST achieved its purpose as a tool for clinical decision-making in RR-TB screening. Moreover, this study demonstrates AI roles in enhancing health-care quality and boost public health efforts against tuberculosis.

Large-scale data analytics have raised a number of ethical concerns. Many of these were introduced in a seminal paper by boyd and Crawford and have been developed since by others (boyd & Crawford, 2012; Lagoze, 2014; Martin, 2015; Mittelstadt, Allo, Taddeo, Wachter, & Floridi, 2016). One such concern which is frequently recognised but under-analysed is the focus on correlation of data rather than on the causative relationship between data and results. Advocates of this approach dismiss the need for an understanding of causation, holding instead that the correlation of data is sufficient to meet our needs. In crude terms, this position holds that we no longer need to know why X+Y=Z. Merely acknowledging that the pattern exists is enough.

In this chapter, the author explores the ethical implications and challenges surrounding a focus on correlation over causation. In particular, the author focusses on questions of legitimacy of data collection, the embedding of persistent bias, and the implications of future predictions. Such concerns are vital for understanding the ethical implications of, for example, the collection and use of ‘big data’ or the covert access to ‘secondary’ information ostensibly ‘publicly available’. The author’s conclusion is that by failing to consider causation, the short-term benefits of speed and cost may be countered by ethically problematic scenarios in both the short and long term.

The purpose of this paper is to investigate epidemiology and control strategies of the four priority vector-borne diseases (VBDs) in Nepal, i.e. malaria, Kala-azar (visceral leishmaniasis), lymphatic filariasis (LF) and dengue fever/dengue hemorrhagic fever.

The study was a retrospective design to collect data during 1998–2016 from VBDs endemic districts of Nepal. All data were reviewed and epidemiological information of the four VBDs were analyzed.

The number of malaria cases during 1998–2016 of the 13 affected districts was declined from 8,498 to 991 cases with no record of deaths since 2012. The number of cases and deaths in the 12 kala-azar (visceral leishmaniasis) affected districts in 1998 was 1,409 and 42 cases, respectively, but was dramatically decreased in 2016 to 213 and 2 cases, respectively. LF cases of the 61 affected districts in 2011, 2014 and 2016 were 28,855, 30,000 and 33,517 cases, respectively. In total, 25 districts achieved elimination target and the remaining are expected to complete the needful cycles by 2018. Dengue incidence of the 31 affected districts during 2006–2015 was under controlled with reported cases of 642, 356 and 136 cases in 2013, 2014 and 2015, respectively, and only one death in 2015. Implementation of control strategies particularly disease management and community peoples’ awareness significantly reduced the cases and deaths of the target VBDs.

The results of this study clearly suggest that the current control strategies have been worked effectively. However, in particular of the VBDs, health education in communities in the endemic areas should be adopted for better community participation in the context of the primary health care approach and increase the effectiveness of disease control.

VBDs, i.e., malaria, kala-azar (visceral leishmaniasis), LF and dengue fever/dengue hemorrhagic fever, are major causes of morbidity and mortality in the least developed countries which include Nepal. Globalization of travel and trading, unplanned urbanization, environmental and climate change are having a significant impact on disease transmission. Therefore, the Ministry of Health of Nepal had brought some changes in strategies based on activities for disease control, vector control, preventive and preparedness for outbreak response. Consequently, the cases and deaths due to malaria, kala-azar (visceral leishmaniasis), lymphatic filaiasis and dengue fever/dengue hemorrhagic fever have been brought down markedly.

Personalized medicine (PM) encompasses a set of procedures, technologies and medications; the term became more prominent from the 2000s onwards and stems from the mapping of the human genome. The purposes of this study were to analyse the development stage of the process of technological innovation for PM and the obstacles that prevent PM from being adopted in the public health system in Brazil.

As a research method, this paper opts for a case study carried out at the Hospital das Clínicas, which belongs to São Paulo Medical School. In total, 22 in-depth interviews were carried out at the hospital to identify current practices in PM, future prospects and barriers imposed to the adoption of PM technologies in public health.

Personalized or precision medicine is already a reality for a small portion of the Brazilian population and is gradually gaining ground in public health care. One finding is that such changes are occurring in a disjointed manner in an incomplete and under development health innovation system. The analysis pointed out that the obstacles identified in Brazil are the same as those faced by high-income countries such as regulation, lack of clinical studies and need to adapt clinical studies to PM. They appear in all stages of the innovation cycle, from research to widespread use.

The research method was a case study, so the findings cannot be extrapolated to other contexts. A limited number of professionals were interviewed, their opinions may not reflect those of their organizations.

There are several studies that discuss how health-care systems in high-income countries could incorporate these new technologies, but only a few focuses on low or middle-income countries such as Brazil.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.