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The pandemic has reinforced the need for revamping the healthcare service delivery systems around the world to meet the increased challenges of modern-day illnesses. The use of medical cyber–physical system (MCPS) in the healthcare is one of the means of transforming the landscape of the traditional healthcare service delivery system. The purpose of this study is to critically examine the impact of MCPS on the quality of healthcare service delivery.

This paper uses an evidence-based approach, the authors have conducted a systematic literature review to study the impact of MCPS on healthcare service delivery. Fifty-four articles were thematically examined to study the impact of MCPS on eight characteristics of the healthcare service delivery proposed by the world health organisation.

The study proposes support that MCPS will positively impact (1) comprehensiveness, (2) accessibility, (3) coverage, (4) continuity, (5) quality, (6) person-centredness, (7) coordination, (8) accountability and (9) efficiency dimension of the healthcare service delivery. The study further draws nine propositions to support the impact of MCPS on the healthcare service delivery.

This study can be used by stakeholders as a guide point while using MCPS in healthcare service delivery systems. Besides, healthcare managers can use this study to understand the performance of their healthcare system. This study can further be used for designing effective strategies for deploying MCPS to be effective and efficient in each of the dimensions of healthcare service delivery.

The previous studies have focussed on technology aspects of MCPS and none of them critically analysed the impact on healthcare service delivery. This is the first literature review carried out to understand the impact of MCPS on the nine dimensions of healthcare service delivery proposed by WHO. This study provides improved thematic awareness of the resulting body of knowledge, allowing the field of MCPS and healthcare service delivery to progress in a more informed and multidisciplinary manner.

At the outbreak of the COVID-19 pandemic, the absence of pharmaceutical agents meant that policy institutions had to intervene by providing nonpharmaceutical interventions (NPIs). To satisfy this need, the World Health Organization (WHO) issued policy guidelines, such as NPIs, and the government of Pakistan released its own policy document that included social distancing (SD) as a containment measure. This study explores the policy actors and their role in implementing SD as an NPI in the context of the COVID-19 pandemic.

The study adopted the constructs of Normalization Process Theory (NPT) to explore the implementation of SD as a complex and novel healthcare intervention under a qualitative study design. Data were collected through document analysis and interviews, and analysed under framework analysis protocols.

The intervention actors (IAs), including healthcare providers, district management agents, and staff from other departments, were active in implementation in the local context. It was observed that healthcare providers integrated SD into their professional lives through a higher level of collective action and reflexive monitoring. However, the results suggest that more coherence and cognitive participation are required for integration.

This novel research offers original and exclusive scenario narratives that satisfy the recent calls of the neo-implementation paradigm, and provides suggestions for managing the implementation impediments during the pandemic. The paper fills the implementation literature gap by exploring the normalisation process and designing a contextual framework for developing countries to implement guidelines for pandemics and healthcare crises.

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.

This paper aims to examine the factors that affect the quality of healthcare services in the implementation of the National Health Insurance Scheme (NHIS) at the local level in Ghana from the perspectives of health policy implementers and beneficiaries in public-private organisations.

This paper has adopted a mixed research method with both qualitative and quantitative data, with in-depth interviews, document analysis and focus groups discussions. A total of 107 participants took part in the interviews and the questionnaire survey.

The study found that these factors greatly affect the quality of healthcare services from the implementers’ perspectives — referrals, effectiveness in monitoring, timeliness, efficiency, reimbursement, compliance with standard guidelines of Ghana Health Service (GHS) and accreditation process. For the beneficiaries, three healthcare services factors are important, including medical consultations, diagnostic services and the supply of drugs and medicines. Some other factors are found to be the least prioritized healthcare services, namely the issuance of prescription forms, verification of identification (ID) cards and staff attitude. However, the study found that implementers and beneficiaries exhibited a mixed reaction (perspectives) on accessing some healthcare services. In some healthcare services where the implementers perceived that beneficiaries have more access to such services, the beneficiaries think otherwise, an irony in the perspectives of the two actors.

This paper adds to the extant literature on the perspectives of policy implementers and beneficiaries on factors that affect the quality of healthcare services in general and specifically on the implementation of NHIS in Ghana with the public-private dimension.

The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.

Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.

The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.

Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.

At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

The healthcare sector is experiencing a major paradigm shift toward a people-centered approach. The key issue with transitioning to a people-centered approach is a lack of understanding of the ever-increasing role of technology in blended human-technology healthcare interactions and the impacts on healthcare actors' well-being. The purpose of the paper is to identify the key mechanisms and influencing factors through which blended service realities affect engaged actors' well-being in a healthcare context.

This conceptual paper takes a human-centric perspective and a value co-creation lens and uses theory synthesis and adaptation to investigate blended human-technology service realities in healthcare services.

The authors conceptualize three blended human-technology service realities – human-dominant, balanced and technology-dominant – and identify two key mechanisms – shared control and emotional-social and cognitive complexity – and three influencing factors – meaningful human-technology experiences, agency and DART (dialogue, access, risk, transparency) – that affect the well-being outcome of engaged actors in these blended human-technology service realities.

Managerially, the framework provides a useful tool for the design and management of blended human-technology realities. The paper explains how healthcare services should pay attention to management and interventions of different services realities and their impact on engaged actors. Blended human-technology reality examples – telehealth, virtual reality (VR) and service robots in healthcare – are used to support and contextualize the study’s conceptual work. A future research agenda is provided.

This study contributes to service literature by developing a new conceptual framework that underpins the mechanisms and factors that influence the relationships between blended human-technology service realities and engaged actors' well-being.

The main aim of the study was to measure and assess the efficiency of the healthcare system in Poland.

An output-oriented Data Envelopment Analysis model with a 2-years window analysis extension was used between 2013 and 2018. The analysis was completed with a determination of the sources of productivity changes (between the first and last year of the study period) and factors that influence efficiency.

Efficient regions have been identified and the spatial diversity in their efficiency was confirmed. The study identified individual efficiency trends together with “all-windows” best and worst performers. Using panel modeling, it was confirmed that the efficiency of health protection is influenced by, among others, accreditation certificates, the length of the waiting list or the number of medical personnel.

Although the analysis was conducted at the voivodeship level (NUTS2), which was fully justified, it would be equally important to analyze data with a lower aggregation level. It would be extremely valuable from the perspective of difficulties faced by the healthcare system in Poland.

The identification of areas and problems affecting the efficiency of the healthcare system in Poland may also be a hint for other countries with similar system solutions that also struggle with the same problems.

The paper explains the efficiency of the country's healthcare system while also paying attention to changes in its level, factors influencing it, spatial diversity and impact on the sector functioning.