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This paper aims to discover and analyze the challenges hampering blockchain technology’s (BT’s) implementation in the Indian health-care sector. A total of 18 challenges have been prioritized and modeled based on an extensive literature search and professional views.

An integrated multi-criteria decision-making approach has been used in two phases. Best worst method (BWM) is used in the first phase to prioritize the challenges with sensitivity analysis to validate the findings and eliminate a few challenges. In the second phase, interpretive structural modeling is applied to the remaining 15 challenges to obtain relative relationships among them with cross-impact matrix multiplication applied to classification analysis for their categorization.

The study’s results reveal that limited knowledge and expertise, cost and risk involved, technical issues, lack of clear regulations, resistance to change and lack of top management support are the top-ranked or high-intensity challenges according to the BWM. Interpretive structural modelling findings suggest that the lack of government initiatives has been driving other challenges with the highest driving power.

This work has been conducted in the Indian context, so careful generalization of the results is needed.

This work will give health-care stakeholders a better perspective regarding blockchain’s adoption. It will help health-care stakeholders, service providers, researchers and policymakers get a glimpse of the strategies for eradicating mentioned challenges. The analysis will help reduce the challenges’ impact on blockchain’s adoption in the Indian health-care sector.

The adoption of BT is a novel concept, especially in developing countries such as India. This is one of the few works addressing the challenges to BT adoption in the Indian health-care sector.

COVID-19 was indeed a global epidemic that revolutionized the way of life, especially health-care services. The way health care will be delivered will undergo a dramatic change in the future. The aim is to analyse the increasing usage of health care systems along with digital technology and IoT especially during pandemic.

This research paper deals with users’ perception and their recommendation status of IoT-based smart health-care monitoring devices based on their perception, experience and level of importance to enhance the quality of life. An effective artificial neural networking (ANN)-based predictive model is designed to classify the user’s perception of usage of IoT-based smart health-care monitoring wearables based on their experience and knowledge.

The model developed has 96.7% accuracy. Among the various predictors chosen as inputs for the model, the findings indicate that self-comfort and trusted data from the device are of high priority. The present study focused only on some common factors derived from previous studies.

Although the performance of the proposed system was noticed to be good, the size of the sample is also limited to a few responses. Implications for future research and practices are discussed.

This is a novel study that aims to develop an ANN model on analyzing the user’s perception of IoT-based smart health-care wearables with the effect of COVID-19 pandemic. This paper elaborates on the ongoing efforts to restart the health-care services for survivability in the new normal situations.

This special “Anbar Abstracts” issue of the Journal of Management in Medicine is split into six sections covering abstracts under the following headings: General Management; Personnel and Training; Quality in Health Care; Health Care Marketing; Financial Management; Information Technology.

The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital.

AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN.

Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN.

AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN.

The essence of blockchain governance is a far departure from the cryptocurrency or Bitcoin that has led to innovation and changing the outline of medical services. The major challenge in medical services is the lack of accessibility of medical services and lack of awareness. A large group of the population belonging to an ethnic minority has a high rate of complications, re-operation and graft rejection. To connect with a minority group and address privacy and safety issues, blockchain-based e-health-care services have massive potential in the medical industry, especially from the perspective of the social aspect.

The study proposed a framework that describes the complex interplay of different stated factors, including perceived ease of use, trust, perceived usefulness and perceived security and privacy. The paper uses structural equation modeling to understand the ethnic minority group’s readiness to adopt blockchain-based e-health-care services.

It was found that all the direct relationships between variables are supported by the findings and have a significant positive relationship with the adoption intention. The tested framework will help regulatory bodies and marketers to develop support health-care service mechanisms for ethnic minority groups by addressing their issues related to security and privacy.

Blockchain-based e-health-care services have massive potential in the medical industry, although, its actual diffusion has not been explored much, with particular reference to an ethnic minority group. This study will explore the diffusion of smart health-care services with respect to ethnic minority group.

This paper aims to focus on developing a theoretical framework for the analysis of factors influencing additive manufacturing (AM) in the health-care domain.

A total of 18 factors are considered through extensive literature review and the relationship between each factor is studied using total interpretive structural modeling (TISM) and the model is logically developed. TISM model is developed using appropriate expert inputs. In addition, cross-impact matrix multiplication applied to classification (MICMAC) analysis is conducted to group the factors.

It was found that “ease of design” and “research and development” are the two most important factors with the highest driving power and dependencies. Through MICMAC analysis, the significance of factors is studied.

The study has been done based on inputs from academic experts and industry practitioners. The inferences from the study have practical relevance.

The development of a structural model for the analysis of factors influencing AM in the health-care domain is the original contribution of the authors.

The purpose of this paper is to outline some key aspects such as material systems used, phenomenological and statistical process modeling, techniques applied to monitor the process and optimization approaches reported. All these need to be taken into account for the ongoing development of the SLM technique, particularly in health care applications. The outcomes from this review allow not only to summarize the main features of the process but also to collect a considerable amount of investigation effort so far achieved by the researcher community.

This paper reviews four significant areas of the selective laser melting (SLM) process of metallic systems within the scope of medical devices as follows: established and novel materials used, process modeling, process tracking and quality evaluation, and finally, the attempts for optimizing some process features such as surface roughness, porosity and mechanical properties. All the consulted literature has been highly detailed and discussed to understand the current and existing research gaps.

With this review, there is a prevailing need for further investigation on copper alloys, particularly when conformal cooling, antibacterial and antiviral properties are sought after. Moreover, artificial intelligence techniques for modeling and optimizing the SLM process parameters are still at a poor application level in this field. Furthermore, plenty of research work needs to be done to improve the existent online monitoring techniques.

This review is limited only to the materials, models, monitoring methods, and optimization approaches reported on the SLM process for metallic systems, particularly those found in the health care arena.

SLM is a widely used metal additive manufacturing process due to the possibility of elaborating complex and customized tridimensional parts or components. It is corroborated that SLM produces minimal amounts of waste and enables optimal designs that allow considerable environmental advantages and promotes sustainability.

The key perspectives about the applications of novel materials in the field of medicine are proposed.

The investigations about SLM contain an increasing amount of knowledge, motivated by the growing interest of the scientific community in this relatively young manufacturing process. This study can be seen as a compilation of relevant researches and findings in the field of the metal printing process.

The purpose of this paper is to show the value of open-ended narrative patient reviews on social media for elucidating aspects of hospital patient satisfaction.

Mixed methods analyses using qualitative (manual content analyses using grounded theory and algorithmic analyses using the Natural Language Toolkit) followed by quantitative analyses (negative binomial regression).

Health-care team communication, health-care team action orientation and patient hospital room environment are positively related to patient hospital satisfaction. Patients form their hospital satisfaction perceptions based on the three facets of their hospital stay experience.

In the spirit of continuous quality improvement, periodically analyzing patient social media comments could help health-care teams understand the patient satisfaction inhibitors that they need to avoid to offer patient-centric care.

By periodically analyzing patient social media comments hospital leaders can quickly identify the gaps in their health service delivery and plug them, which could ultimately give the hospital a competitive advantage.

To the best of the authors’ knowledge, this is one of the first studies to apply mixed methods to patient hospital review comments given freely on social media to critically understand what drives patient hospital satisfaction ratings.

InfoMall is a program led by the Northeast Parallel Architectures Center featuring a partnership of approximately twenty‐four organizations with a plan for accelerating development of the High‐Performance Computing and Communications (HPCC) software and systems industry. HPCC is a critical technology where the United States has clear international leadership and which will have unprecedented impact on industry, education, society, and defense. The communications component of HPCC is critical to developing HPCC products. Acceptance of HPCC by these real‐world sectors has been delayed by the extremely hard problem of HPCC software development. InfoMall employs a novel technology development strategy involving closely linked programs in technology extraction and certification, software development, marketing, education, and training, economic development, and small business support. The process is constructured and explained by analogy to a full‐service set of stores in a shopping mall.