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The main objective of the study is to address the lack of sustainability assessments of smart connected health systems in the academic literature by presenting an assessment model to determine the alignment of these systems with the 17 Sustainable Development Goals (SDGs) proposed in the 2030 Agenda.

An evaluation model based on decision analysis is proposed that includes three phases: alignment framework, information gathering and assessment. This model measures the alignment of the connected health system with each of the 17 SDGs, identifying the goals and criteria associated with each SDG that the system achieves to satisfy.

The analysis reveals that the system has achieved more than 24% of the targets among the 17 SDGs. In addition, it identifies four sustainability challenges that the system potentially addresses in relation to the SDGs, providing valuable guidance for researchers and practitioners interested in sustainable health technology development.

The study's results have significant implications for policymakers and stakeholders in the health and technology sectors.

The originality of this study lies in its comprehensive approach to assessing the sustainability of connected health systems in the context of the SDGs, filling an important gap in the existing literature.

The process of performance measurement provides support to company management to achieve the objectives established in strategic planning. Through the definition of critical success factors and related key performance indicators, performance measurement verifies the gap between planned objectives and the results achieved, informing the responsible bodies to enable them to evaluate performance and, if necessary, implement improvement actions. Although many types of companies adopt performance measurement, this process is challenging when applied to national health services. This paper aims to identify the evolution of performance measurement and the critical success factors of national health services.

The authors conducted an explorative case study of a leading national health service to delineate the evolutionary path of performance measurement and the main critical success factors.

The results indicate a significant increase in the maturity of performance measurement of a national health service that has been motivated by international reforms and national regulations. This research highlights performance measurement features such as a balanced set of metrics, targets, and incentives linked to strategic objectives and regular and frequent performance reviews. Furthermore, it identifies the performance measurement model of a leading national health service.

The evolution of performance measurement and numerous critical success factors of national health services are described; the critical success factors cover a wide range of financial to operational aspects such as patient safety, organizational appropriateness, and clinical appropriateness.

This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces.

The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor.

All 46 factors were assessed as “Very Important” through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are “AI ability to learn from patient data”, “insufficient data privacy measures for patients”, “availability of technical support and maintenance services”, “physicians’ acceptance of AI in healthcare”, “reliability and uptime of AI systems” and “ability to reduce medical errors”.

Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices.

This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders’ comprehension of the factors challenging the integration of AI technologies.

This study investigates the pathways for adopting IoTs and BDA technologies to improve healthcare management.

The study relied on 445 healthcare professionals' perspectives to explore different causal pathways to IoTs and BDA adoption and usage for daily healthcare management. The Fussy-set Qualitative Comparative Analysis was adopted to explore the underlying pathways for healthcare management.

The empirical analysis revealed six different configural paths influencing the acceptance and use of IoTs and BDA for healthcare improvement. Two key user topologies from the six configural paths, digital literacy and ease of use and social influence and behavioural intentions, mostly affect the paths for using digital health technologies by healthcare physicians.

Despite this study's novel contributions, limitations include the fsQCA methodology, perceptual data and the context of the study. The fsQCA methodology is still evolving with different interpretations, although it reveals new insights and as such further studies are required to explain the configural paths of social phenomena. Additionally, future research should consider other constructs beyond the UTAUT and digital literacy to illustrate configural paths to healthcare technology acceptance and usage. Again, the views of healthcare professionals are perceptual data. Hence future research on operational data will support significant contributions towards pathways to accept and use emerging technologies for healthcare improvement. Lastly, this study is from a developing country perspective where emerging digital healthcare technology is still emerging to support healthcare management. Hence, more investigation from other cross-country analyses of configural paths for digital technology deployment in healthcare will enhance the conversation with IoTs and BDA for healthcare management.

Holistically, the acceptance and use of healthcare technologies and platforms is not solely on their capabilities, but a combination of distinct factors driven by users' perspectives. This offers healthcare administrators and institutions to essentially reflect on the distinct combinations of conditions favourable to health professionals who can use IoTs and BDA for healthcare improvement.

This study is among the few scholarly works to empirically investigate the configural paths to support healthcare improvement with emerging technologies. Using fsQCA is a unique contribution to existing information system literature for configural paths for healthcare improvement with emerging digital technologies.

Patient safety is a top priority globally. A robust healthcare system requires strategic collaboration between research and development. The author analysed over 300 cases from seven hospitals using the failure modes, effects, and criticality analysis (FMECA) tool to understand the underlying causes of medical errors.

The author studied seven hospitals and 300 cases using FMECA to prioritise activities. The findings showed that high-priority events occurred less frequently but had the potential to cause the most harm. Team members evaluated independently to ensure unbiased evaluations. This approach is useful for setting priorities or assessing difficulties.

Poor communication and lack of coordination among staff in a healthcare organisation caused misunderstandings, ineffective decision-making, delays in patient care, and medical errors. Implementation of effective communication and coordination protocols can help avoid these problems.

The study recommends using FMECA to identify and prioritise failures and conducting in-depth analyses to understand their root causes. It also highlights the importance of interdisciplinary knowledge and soft skills for healthcare staff.

This study reveals the significance of FMECA in healthcare risk management and benchmarking. FMECA helps identify system failures, develop prevention strategies, and evaluate effectiveness against industry benchmarks. It offers healthcare professionals a valuable tool to enhance patient safety and improve healthcare quality.

The challenges to healthcare caused by the COVID-19 pandemic forced hospital librarians to develop their abilities to cope with change and crises, both on a social level and an organisational level. The aim of this study is to contribute to knowledge about how hospital librarians developed library services during the pandemic and how these changes contributed to building information resilience in the healthcare organisation. This paper also seeks to explore how resilience theory, and specifically the concept information resilience, can be used within library and information science (in LIS) to investigate resilience in the library sector.

Nine semi-structured interviews with librarians were conducted at four different hospital libraries in four different regions in Sweden between March and May 2022. The empirical material was analysed through an interaction between the tzheoretical perspective and the empirical material through a thematic analysis. In each theme, specific resilience resources are identified and analysed as components of the information resilience developed by hospital librarians.

The results show that hospital librarians contribute to several different information resilience resources, which support information resilience in the healthcare organisation. Three aspects characterize the qualities of resilience resources: access, flexibility, and collaboration. The findings suggest that the framework for analysing information resilience used in this study is well suited for studying the resilience of libraries from both organisational and informational aspects.

The analysis of information resilience on an organisational level presents a novel way to study resilience in the library sector.

The purpose of the study is to address concerns regarding the subjectivity and imprecision of decision-making in table tennis refereeing by developing and enhancing a sensor node system. This system is designed to accurately detect the points on the table tennis table where balls collide. The study introduces the twined-reinforcement chimp optimization (TRCO) framework, which combines two novel approaches to optimize the distribution of sensor nodes. The main goal is to reduce the number of sensor units required while maintaining high accuracy in determining the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through complex optimization procedures, the study aims to improve the efficiency and reliability of decision-making in table tennis refereeing by leveraging sensor technology.

The study employs a design methodology focused on developing a sensor array system to enhance decision-making in table tennis refereeing. It introduces the twined-reinforcement chimp optimization (TRCO) framework, combining dual adaptive weighting strategies and a stochastic approach for optimization. By meticulously engineering the sensor array and utilizing complex optimization procedures, the study aims to improve the accuracy of detecting ball collisions on the table tennis table. The methodology aims to reduce the number of sensor units required while maintaining high precision, ultimately enhancing the reliability of decision-making in the sport.

The optimization research study yielded promising outcomes, showcasing a substantial reduction in the number of sensor units required from the initial count of 60 to a more practical 49. The sensor array system demonstrated excellent accuracy in identifying the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through the implementation of the twined-reinforcement chimp optimization (TRCO) framework, which integrates dual adaptive weighting strategies and a stochastic approach, the study achieved its goal of enhancing the efficiency and reliability of decision-making in table tennis refereeing.

This study introduces novel contributions to the field of table tennis refereeing by pioneering the development and optimization of a sensor array system. The innovative twined-reinforcement chimp optimization (TRCO) framework, integrating dual adaptive weighting strategies and a stochastic approach, sets a new standard for sensor node distribution in sports technology. By substantially reducing the number of sensor units required while maintaining high accuracy in detecting ball collisions, this research offers practical solutions to address the inherent subjectivity and imprecision in decision-making processes. The study’s originality lies in its meticulous design methodology and complex optimization procedures, offering significant value to the field of sports technology and officiating.

A healthcare unit's effectiveness largely depends on how well its interprofessional teams work together. Unfortunately, the strategies used to improve these teams often lack substance. This study analyzed these strategies and found a performance gap.

This study took a unique mixed-method approach, systematically reviewing both qualitative and quantitative studies that identified strategies to enhance interprofessional teams in healthcare units. To gauge the effectiveness of these strategies, the researcher utilized an Importance-Performance Analysis (IPA) in four specialized clinical training centers in Hamadan province, Iran. The analysis of the IPA involved 35 experts from these centers as the statistical population.

Based on a systematic review, there are seven categories: contextual, strategic, communication, organizational, individual, Human Resources Management (HRM), and environmental for promoting interprofessional teams with a total of 36 sub-indicator. Based on the IPA, the HRM aspect shows the most extensive performance gap. The individual and organizational aspects fall under resource wastage, and the environmental aspect is within the indifferent zone. Also, some critical sub-indicators, such as incentives/rewards, roles and responsibilities, financial resources, team-initiated innovation, the culture of respect, partner resources, humility, data availability, set expectations, and team availability, are in the weak areas.

This research has identified critical areas for improvement in promoting teamwork in clinical training centers through a comprehensive gap analysis. It also presents practical policy solutions to address these weak points, providing a clear roadmap for enhancing interprofessional teams in healthcare units.

Improving teamwork in healthcare can be challenging, but it is possible with proper strategies and tools. One of the highlights of the recent study was the combination of systematic review studies with IPA to identify areas for improving interprofessional teamwork in clinical training centers.