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This study aims to identify 3D-printed medical model (3DPMM) supply chain barriers that affect the supply chain of 3DPMM in the Indian context and investigate the interdependencies between the barriers to establish hierarchical relations between them to improve the supply chain.

The methodology used interpretive structural modeling (ISM) and a decision-making trial and evaluation laboratory (DEMATEL) to identify the hierarchical and contextual relations among the barriers to the 3DPMM supply chain.

A total of 15 3DPMM supply chain barriers were identified in this study. The analysis identified limited materials options, slow production speed, manual post-processing, high-skilled data analyst, design and customization expert and simulation accuracy as the significant driving barriers for the medical models supply chain for hospitals. In addition, the authors identified linkage and dependent barriers. The present study findings would help to improve the 3DPMM supply chain.

There were no experts from other nations, so this study might have missed a few 3DPMM supply chain barriers that would have been significant from another nation’s perspective.

ISM would help practitioners minimize 3DPMM supply chain barriers, while DEMATEL allows practitioners to emphasize the causal effects of 3DPMM supply chain barriers.

This study minimizes the 3DPMM supply chain barriers for medical applications through a hybrid ISM and DEMATEL methodology that has not been investigated in the literature.

Medical diagnosis and treatment processes exhibit a high degree of variability, as during the process execution, healthcare professionals can decide on additional steps, change the execution order or skip a task. Process models can help to document and to discuss such processes. However, depicting variability in graphical process models using standardized languages, such as Business Process Model and Notation (BPMN), can lead to large and complicated diagrams that medical staff who do not have formal training in modeling languages have difficulty understanding. This study proposes a pattern-based process visualization that medical doctors can understand without extensive training. The process descriptions using this pattern-based visualization can later be transformed into formal business process models in languages such as BPMN.

The authors derived patterns for expressing variability in healthcare processes from the literature and medical guidelines. Then, the authors evaluated and revised these patterns based on interviews with physicians in a Danish hospital.

A set of business process variability patterns was proposed to express situations with variability in hospital treatment and diagnosis processes. The interviewed medical doctors could translate the patterns into their daily work practice, and the patterns were used to model a hospital process.

When communicating with medical personnel, the patterns can be used as building blocks for documenting and discussing variable processes.

The patterns can reduce complexity in process visualization. This study provides the first validation of these patterns in a hospital.

Researchers recommend a reorganization of the medical profession into larger groups with a multispecialty mix. We analyze whether there is evidence for the superiority of these models and if this organizational transformation is underway.

We summarize the evidence on scale and scope economies in physician group practice, and then review the trends in physician group size and specialty mix to conduct survivorship tests of the most efficient models.

The distribution of physician groups exhibits two interesting tails. In the lower tail, a large percentage of physicians continue to practice in small, physician-owned practices. In the upper tail, there is a small but rapidly growing percentage of large groups that have been organized primarily by non-physician owners.

While our analysis includes no original data, it does collate all known surveys of physician practice characteristics and group practice formation to provide a consistent picture of physician organization.

Our review suggests that scale and scope economies in physician practice are limited. This may explain why most physicians have retained their small practices.

Larger, multispecialty groups have been primarily organized by non-physician owners in vertically integrated arrangements. There is little evidence supporting the efficiencies of such models and some concern they may pose anticompetitive threats.

This is the first comprehensive review of the scale and scope economies of physician practice in nearly two decades. The research results do not appear to have changed much; nor has much changed in physician practice organization.

Aims to investigate medical rapid prototyping (medical RP) technology applications and methods based on reverse engineering (RE) and medical imaging data.

Medical image processing and RE are applied to construct three‐dimensional models of anatomical structures, from which custom‐made (personalized) medical applications are developed.

The investigated methods were successfully used for design and manufacturing of biomodels, surgical aid tools, implants, medical devices and surgical training models. More than 40 medical RP applications were implemented in Europe and Asia since 1999.

Medical RP is a multi‐discipline area. It involves in many human resources and requires high skills and know‐how in both engineering and medicine. In addition, medical RP applications are expensive, especially for low‐income countries. These practically limit its benefits and applications in hospitals.

In order to transfer medical RP into hospitals successfully, a good link and close collaboration between medical and engineering sites should be established. Moreover, new medical applications should be developed in the way that does not change the traditional approaches that medical doctors (MD) were trained, but provides solutions to improve the diagnosis and treatment quality.

The presented state‐of‐the‐art medical RP is applied for diagnosis and treatment in the following medical areas: cranio‐maxillofacial and dental surgery, neurosurgery, orthopedics, orthosis and tissue engineering. The paper is useful for MD (radiologists and surgeons), biomedical and RP/CAD/CAM engineers.

The purpose of this paper is to present a new method and model for constructing a new decision-making paradigm of Medicare, which can not only satisfy the needs of the sick people but also reduce the possibility of people slipping back to poverty due to diseases under the policy of Targeted Poverty Alleviation (TPA) of China.

This paper uses the traditional supply chain theory to analyze the Medicare of impoverished people with the policy of TPA of China and transforms it into a multi-layer supply chain optimization decision-making problem. First, a nonlinear integer programming model for poor people’s Medicare decision with opportunity constraints is constructed. To facilitate the solution of the optimal decision scheme, the abovementioned model is transformed into a linear integer programming model with opportunity constraints by using the Newsvendor model for reference. Meanwhile, the scope of the inventory model is discussed, for it can be combined with the construction of the medical insurance system better. Second, the theoretical model is applied to the practical problem. Finally, based on the results of the theoretical model applying the practical problem, we give further improvement and modification of the theoretical model applies it to the actual situation further.

This paper presents a theoretical model about determine the optimal the inventory, under the framework of traditional supply chain decision-making, for it can be combined with the construction of the medical insurance system better. The theoretical model is applied to the practical problem of the fight against poverty in XX County, China. By using the actual data and MATLAB, optimal decision scheme is obtained.

There are two aspects of value. On the one hand, this paper provides a new way to construct a Medicare system of impoverished people with TPA of China. On the other hand, this paper tries making a new way to handle the storage of medicines and related medical devices at basic standard clinics decision-making problems based on above mentioned Medicare system.

This paper aims to illustrate a number of instances where RP and associated technology has been successfully used for medical applications.

A number of medical case studies are presented, illustrating different uses of RP technology. These studies have been analysed in terms of how the technology has been applied in order to solve related medical problems.

It was found that RP has been helpful in a number of ways to solve medical problems. However, the technology has numerous limitations that have been analysed in order to establish how the technology should develop in the future.

RP can help solve medical problems, but must evolve if it is to be used more widespread in this field.

This paper has shown a number of new applications for RP, providing a holistic understanding how the technology can solve medical problems. It also identifies a number of ways in which the technology can improve in order to better solve such problems.

The purposes of this paper are two-fold: first, to introduce a new concept of primary care consultation system at a mainland Chinese hospital in response to healthcare reform; and second, to explore the factors associated with change resistance and acceptance from both patients’ and medical staff’s perspectives.

A survey design study, with two questionnaires developed and distributed to patients and medical staff. Convenience and stratified random sampling methods were applied to patient and medical staff samples.

A 5-dimension, 21-item patient questionnaire and a 4-dimension, 16-item staff questionnaire were identified and confirmed, with 1020 patients (91.07 percent) and 202 staff (90.18 percent) as effective survey participants. The results revealed that patient resistance mainly stems from a lack of personal experiences with visiting general practice (GP) and being educated or having lived overseas; while staff resistance came from occupation, education, GP training certificate, and knowledge and experience with specialists. Living in overseas and knowledge of GP concepts, gender and education are associated with resistance of accepting the new practice model for both patients and staff.

There are few Chinese studies on process reengineering in the medical sector; this is the first study to adopt this medical consultation model and change in patients’ consultation culture in Mainland China. Applying organizational change and process reengineering theories to medical and healthcare services not only extends and expands hospital management theory but also allows investigation of modern hospital management practice. The experience from this study can serve as a reference to promote this new consultation model in Chinese healthcare reform.

This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment.

Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions.

This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution.

Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.

The purpose of this study is to develop an efficient prediction model using vital signs and standard medical score systems, which predicts the clinical severity level of the patient in advance based on the quick sequential organ failure assessment (qSOFA) medical score method.

To predict the clinical severity level of the patient in advance, the authors have formulated a training dataset that is constructed based on the qSOFA medical score method. Further, along with the multiple vital signs, different standard medical scores and their correlation features are used to build and improve the accuracy of the prediction model. It is made sure that the constructed training set is suitable for the severity level prediction because the formulated dataset has different clusters each corresponding to different severity levels according to qSOFA score.

From the experimental result, it is found that the inclusion of the standard medical scores and their correlation along with multiple vital signs improves the accuracy of the clinical severity level prediction model. In addition, the authors showed that the training dataset formulated from the temporal data (which includes vital signs and medical scores) based on the qSOFA medical scoring system has the clusters which correspond to each severity level in qSOFA score. Finally, it is found that RAndom k-labELsets multi-label classification performs better prediction of severity level compared to neural network-based multi-label classification.

This paper helps in identifying patient' clinical status.