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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 Taking the withdrawn Memorandum of Understanding (MoU) between NHS Digital and the Home Office as a worked example, this chapter demonstrates how the capacity of the law to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.

This paper aims to examine lean thinking in medical commodities supply chains by considering its applications and success factors. It determines the drivers and wastes of medical commodity supply chain, and the existing lean tools and practices together with their application in the supply chain processes. The paper also examined the challenges and success factors for effective lean application in the medical commodities supply chains.

The study used qualitative approaches, in-depth interviews and focus group discussions with key informants to form the basis for data collection. Through thematic analysis, the collected data were analyzed by developing themes reflecting the objectives of the study.

The main drivers for waste associated with the supply chain were demand management, supplier development, institutional framework and governance. The wastes were observed at the level of inventory, operation costs, transaction costs, delays in terms of service, commodity delivery time and quality. Digitalization, information technology and standardization were the tools for medical supply chain. Poor infrastructure, unreliable internet supply, environmental uncertainty and poor management support were challenges to realizing an effective supply chain.

Although the qualitative approach used in the study provides detailed information, a quantitative study covers a larger sample for generalization.

Capacity building and professionalism should be given a priority because the philosophy of lean focuses on waste removal and continuous improvement, which highly depends on the quality of human resource (Brito et al., 2020). Limited human resource capabilities in supply chain management will, therefore, result into poor operational efficiency, which are wasted. Moreover, systems interoperability is key waste minimization and, therefore, demands interventions.

The government under the Ministry of Health and other key sector ministries such as local and regional governments should better understand the role of the waste drivers and adopt system-wide reforms to support improvements to remove waste in the medical supply chain. For example, the current institutional framework creates an administrative block and hence leads to wastes. This bureaucratic procedure should be removed to minimize wastes along the chain.

This study is among the first studies to determine applicability and implementation of lean in a resource-constrained context. The paper identifies contextual factors for lean implementation. This paper focused on a holistic view of the entire supply chains to enhance a well-functioning supply chain in delivering health commodities.

Bureaucracy to a large extent entrenches orderliness and productive means of achieving goals in both public and private organisations across the world. However, bureaucracy is not suitable in the management of hospitals due to its peculiar nature of operations. This study investigates the conundrum of bureaucratic processes and health-care service delivery in government hospitals in Nigeria.

The study surveyed 600 outpatients and attendees visiting tertiary and government hospitals in Nigeria using descriptive design to obtained data from the respondents. A research instrument, questionnaire, was used to gather data. Out of the 600 outpatients visiting the 20 hospitals in government and tertiary hospitals, 494 responses were returned from the attendees. The study employed random sampling strategy to collect the information.

The findings of this study were that service delivery in government hospitals were in adverse position on all the four constructs of bureaucratic dimensions as against quality of service delivery in hospitals in Nigeria. It discovered that bureaucratic impersonality cannot impact on the quality of service delivery in government hospitals in Nigeria. Separation and division of labour among health workers have no significant effect on quality service delivery in government hospitals. Formal rules and regulations (administrative procedure, rules, and policies) prevent quality service delivery in government hospitals in Nigeria. Also, patient’s waiting time was not significant to the quality of service delivery in government hospitals.

The results are constrained with dimensions of bureaucratic processes. Thus, the implication of this study is that bureaucracy in the Nigerian public hospitals is an unnecessary marriage which should be carefully separated and de-emphasised for quality service delivery in the hospitals to thrive.

Largely, this study is practical essential as it unearths the irrelevant operations procedure that hinder progress in Nigerian hospitals.

The study accomplishes recognised importance to survey how bureaucracy impedes quality service delivery in government hospitals. This study has provided a vital clue to elements that will bring rapid attention to patients’outcome in Nigerian hospitals and health-care facilities which hitherto has not been emphasised. The study has contributed to the existing body of knowledge associated to healthcare service quality in developing country.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.

Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.

The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.

This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

This paper aims to explore effective supply chain principles, through the theory of transaction cost economics, as measures to improve current contingency pharmaceutical item shortfalls in the Air Force Medical Service (AFMS) Contingency Pharmaceutical Programme.

In this research, AFMS contingency pharmaceutical data was collected from various databases, including the Joint Medical Asset Repository, Medical Contingency Requirements Workflow and the Medical Requirements List. Through the methodology of cost-benefit analysis, alternative sourcing and fulfilment practices are evaluated.

The findings of this research indicate that the application of centralized purchasing principles, in an effort to leverage prime vendor contract fill rates for shortage items, can lead to 12%–17% increases in pharmaceutical material availability across the programme.

This research clearly shows that consolidating demand for shortage items across Active Duty War Reserve Material assemblages, though applications of centralized purchasing principles that leverage prime vendor contract fill rates, can lead to substantial increases in material availability at costs that justify the calculated benefits.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

This article analyzes the cognitive microfoundations, conflicting institutional logics and professional hybridization in a case characterized by conflict.

In contrast to the majority of earlier studies focusing on special health care, the study was conducted in a Finnish basic health care organization. The empirical data include 36 interviews, accounting reports, budgets, newspaper articles and meeting notes collected 2013–2018.

The use of accounting techniques in this case did not offer professionals sufficient support under conditions of conflict. The authors suggest that this perceived lack of support intensified the negative emotions toward accounting techniques. These negative emotions aggregated into incompatible professional-level institutional logics, which contributed to the lack of hybridization between such logics. The authors highlight the importance of the cognitive microfoundations, that is, the individual-level interpretations and emotional responses, in the analysis of conflicting institutional logics.

Managerial attention needs to be directed to accounting practices perceived as frustrating or threatening, a perception that can prevent the use of accounting techniques in the creation of professional hybrids. The Finnish basic health care context involves inconsistent political decision-making, multiple tasks, three institutional logics and individual interpretations and emotions in various decision-making situations.

This study develops microfoundational accounting research by illustrating how individual-level cognitive microfoundations such as dissatisfaction with budgeting, aggregate into professional-level institutional logics, and in our case, prevent professional hybridization in a basic health care setting characterized by conflict and three separate institutional logics.