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The purpose of this study is to describe the design and validation of a three-dimensional (3D)-printed phantom of a uterus to support the development of uterine balloon tamponade devices conceived to stop post-partum haemorrhages (PPHs).

The phantom 3D model is generated by analysing the main requirements for validating uterine balloon tamponade devices. A modular approach is implemented to guarantee that the phantom allows testing these devices under multiple working conditions. Once finalised the design, the phantom effectiveness is validated experimentally.

The modular phantom allows performing the required measurements for testing the performance of devices designed to stop PPH.

PPH is the leading obstetric cause of maternal death worldwide, mainly in low- and middle-income countries. The proposed phantom could speed up and optimise the design and validation of devices for PPH treatment, reducing the maternal mortality ratio.

To the best of the authors’ knowledge, the 3D-printed phantom represents the first example of a modular, flexible and transparent uterus model. It can be used to validate and perform usability tests of medical devices.

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

Telemedicine, similarly to social media, accelerates information exchange, enriches information, provides better access to information and, furthermore, has an impact on mobilizing resources in business-to-business relationships. This paper aims to contribute to the understanding of the changes brought about by telemedicine, as a new technology, in patient routes.

This case study method was applied to examine five health-care protocols through their patient routes (series of activities) with and without telemedicine technology. The ARA model was applied to examine the changes telemedicine engendered in relation to activities, resources and actors. The strategy of visual mapping was applied for the comparative analysis.

The analyzed cases show that the new resources applied through telemedicine technology modified the number and substance of relevant activities and the set and role of actors who were involved. The quantity or the availability of output information increased in patient routes when new resources were added by telemedicine technology. When technology change occurred, any change in data or information systems – the two building blocks of information – could result in new or modified activities. If data that is used or produced while undertaking an activity change simultaneously along with the information system used for encrypting this data, then this “joint change” will certainly entail some kind of change in the set of activities, resources or actors that are involved. If not, then the activities continued the same as with the face-to-face protocol (without the new technology).

The novelty of the paper is that the results highlight the role of information in the extent of change in interactions induced by new technology. Findings about such changes show how information influenced by activities, resources and actors can help decision-makers in relation to the use of telemedicine.

During COVID-19, this study aims to evaluate the crisis communication strategies (CCS) of Fortune 500 medical device businesses. These companies’ CCS adoption is evaluated using data from the microblogging site Twitter.

A total of 11,569 tweets were collected over the course of a year, from 31 December 2019 to 31 December 2020, and analysed using COVID-19’s pre-crisis, crisis and new normal stages. The data acquired from Twitter is assessed using latent Dirichlet allocation-based topic modelling, valence aware dictionary for sentiment reasoning sentiment analysis and emotion recognition analysis and then further examined using fuzzy set qualitative comparative analysis to build a configurational model. The findings were compared to Cheng’s (2018, 2020) integrated strategy toolkit for organisational CCS, which included 28 strategies.

With positive sentiments across stages, companies chose “information providing”, “monitoring” and “good intentions” as the CCS. In the crisis and new normal stages of COVID, the emotion of “depression” was observed.

Researchers would be able to assess the CCS used through visual aids in the future by conducting a cross-industry examination using image analytics. Furthermore, by prolonging the study’s duration, long-term changes in the CCS can be investigated.

Companies should send real-time information to their stakeholders via social media during a pandemic, conveying good intentions and positive sentiments while remaining neutral.

To the best of the authors’ knowledge, this is one of the first studies to investigate the CCS patterns used by medical device businesses to communicate via social media during a pandemic.

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.

Medical devices are undergoing rapid changes because of the increasing affordability of advanced technologies like additive manufacturing (AM) and three-dimensional scanning. New avenues are available for providing solutions and comfort that were not previously conceivable. The purpose of this paper is to provide a comprehensive review of the research on developing prostheses using AM to understand the opportunities and challenges in the domain. Various studies on prosthesis development using AM are investigated to explore the scope of integration of AM in prostheses development.

A review of key publications from the past two decades was conducted. Integration of AM and prostheses development is reviewed from the technologies, materials and functionality point of view to identify challenges, opportunities and future scope.

AM in prostheses provides superior physical and cognitive ergonomics and reduced cost and delivery time. Patient-specific, lightweight solutions for complex designs improve comfort, functionality and clinical outcomes. Compared to existing procedures and methodologies, using AM technologies in prosthetics could benefit a large population.

This paper helps investigate the impact of AM and related technology in the field of prosthetics and can also be viewed as a collection of relevant medical research and findings.

While attempting to persuade surgeons to accept their health technology, sales representatives for medical devices face daily challenges in the operating room. Surgeons exhibit cognitive complexity (abstractness vs. concreteness) when accepting any form of health technology. Surgeons choose technologies on behalf of their patients, taking patient priorities and expectations into account. Prior research has focused on cognitive complexity in the context of health technology adoption, but the issue of technology acceptance has not been addressed. The purpose of this study to use the construal level (CL) theory to determine the role of behavioural abstraction levels in the acceptance of surgical health technology.

On the basis of 556 min of seminar-based data and semi-directive interviews, the surgeons’ experiences regarding the acceptance of health technology were analysed. A non-directive observational method was used to permit the spontaneous emergence of CL dimensions in a natural environment. A categorization model was used for data coding, and MAXQDA, in addition to traditional multidimensional scaling and hierarchical cluster analysis, was used to generate results with joint displays.

Effort expectancy, learning curve, performance risk, habit, patient clinical condition, clinical outcome expectancy, technology setting and social influence were construed at a low construal level (LCL). On the other hand, patient paying capacity, technology cost, price value, financial risk and patient performance expectation were construed at a high construal level (HCL). The study also reveals duality-based factors which showed proximity to HCL but intersected at LCL, and vice versa. Duality-based factors such as effort expectancy, surgical technique, trust and perceived risk intersected at HCL, whereas performance expectancy, relative advantage, time expectancy, perceived value, physical risk and peer group influence intersected at LCL.

This is one of the early studies that presented the impact of behavioural abstraction on behavioural intention to accept health technology for surgeries.

This study aims to investigate the supply chain gaps during the COVID-19 pandemic in Tanzania amid the pandemic era.

This study adopted a mixed approach, using both structured questionnaires and individual interviews to gather raw data (quantitative and qualitative data). To assess quantitative data for statistical (descriptive and inferential) analysis, this study used a sample of 57 wholesale and retail pharmacies and 10 hospital pharmacies, which were randomly selected. The thematic analysis was applied to the gathered qualitative data to authenticate the quantitative findings and conclusions which were the outcome of the descriptive and inferential analysis (binary logistics regression) analysed by the SPSS.

The results revealed the presence of various supply chain gaps in terms of target gaps, time gaps and coverage gaps. This study highlighted the disruption on demand and uncertainty in business environment as ways that the COVID-19 pandemic contributed to the gaps and revealed the negative effects of the supply chain gaps on the effectiveness of medical supplies in Tanzania.

The results revealed the presence of various supply chain gaps (in terms of target gaps, time gaps and coverage gaps), underlined the disruption in demand and uncertainty in the business environment as ways that the COVID-19 pandemic contributed to the gaps and revealed the negative effects of the supply chain gaps on the effectiveness of medical supplies in Tanzania.

Data generated and used in this study is from participants from one country only (Tanzania), despite the supply chain gaps being common to many developing countries in general.

This study provides a novel framework in medical supply chain literature by identifying numerous disruptions in the medical supply chain which emerged during the COVID-19 emergence and serves as a basis for future studies on how to counter the gaps and rebuild a resilient and sustainable medical supply chain in developing countries.