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The purpose of this paper is to design a future value stream map of the system and patient pathway with lean health care application tools in terms of quality improvement (QI) in order to decrease the non-value-added activities for patients, doctors, and nurses for a radiology unit in a public hospital based in Istanbul, Turkey.

Data are collected from personal observations, information technologies unit, and authorized employees. In order to measure subprocesses, some qualitative data are transformed into quantitative data via authorized employees’ experiences. Moreover, data tracking and keeping systems are too poor for revealing the current situation.

The current value stream map of the system of the radiology department of a public education and research hospital was analyzed and the future value stream map of the system and patient pathway was redesigned with lean health care application tools in terms of QI.

The greatest limitation is the resistant to change and fear of talking on mistakes and low performances.

This paper states an example of a current and future value stream map showing step by step where the bottlenecks are and how these can be improved and what specific benefits will this analysis bring to the health care system. It will be useful for both academicians and practitioners on how to apply lean to health care.

The purpose of this paper is to improve the mammography and ultrasound screening processes to increase the healthcare system quality and patients’ pleasantness while decreasing costs.

The data storage system is very insufficient so that data are collected from hospital staff and by personal observations. A lean management tool, value stream mapping is used to see the general view of the radiology department with its problems at a glance. Data and the map are used as a guide to conduct a quality house to make a prioritization between the problems, to decide which one to solve first, of patients at the hospital.

Problems of the radiology department related to mammography and ultrasound and suggestions to these problems according to the quality house conducted.

The paper consequently focuses on the value creation process and on how to revisit managing for quality in the systems perspective.

One of the main reasons for increase in mortality rate in woman is breast cancer. Accurate early detection of breast cancer seems to be the only solution for diagnosis. In the field of breast cancer research, many new computer-aided diagnosis systems have been developed to reduce the diagnostic test false positives because of the subtle appearance of breast cancer tissues. The purpose of this study is to develop the diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model.

The proposed diagnosis methodology initiates with the pre-processing procedure. Subsequently, feature extraction is performed. In feature extraction, the image features which preserve the characteristics of the breast tissues are extracted. Consequently, feature selection is performed by the proposed least-mean-square (LMS)-Cuckoo search feature selection (LCFS) algorithm. The feature selection from the vast range of the features extracted from the images is performed with the help of the optimal cut point provided by the LCS algorithm. Then, the image transaction database table is developed using the keywords of the training images and feature vectors. The transaction resembles the itemset and the association rules are generated from the transaction representation based on a priori algorithm with high conviction ratio and lift. After association rule generation, the proposed TreeHiCARe classifier model emanates in the diagnosis methodology. In TreeHICARe classifier, a new feature index is developed for the selection of a central feature for the decision tree centered on which the classification of images into normal or abnormal is performed.

The performance of the proposed method is validated over existing works using accuracy, sensitivity and specificity measures. The experimentation of proposed method on Mammographic Image Analysis Society database resulted in classification of normal and abnormal cancerous mammogram images with an accuracy of 0.8289, sensitivity of 0.9333 and specificity of 0.7273.

This paper proposes a new approach for the breast cancer diagnosis system by using mammogram images. The proposed method uses two new algorithms: LCFS and TreeHiCARe. LCFS is used to select optimal feature split points, and TreeHiCARe is the decision tree classifier model based on association rule agreements.

The purpose of this paper is to describe a novel, nanomaterial‐based X‐ray imaging technology, developed at the University of North Carolina.

The paper describes a unique X‐ray source, based on field emission from a carbon nanotube (CNT) cold cathode and discusses its application to computer tomography (CT).

CNT‐based X‐ray sources are shown to offer improved performance over conventional thermionic devices and allow the design of gantry‐free, stationary CT systems with faster scanning speeds and better image quality. The field emission technology has been commercialised by Xintek and a joint venture with Siemens, XinRay Systems, aims to commercialise CT imagers based on the technology.

The paper describes a novel approach to the generation of X‐rays and its use in medical CT imaging systems.

The social marketing literature tends to focus on upstream marketing (policy) and downstream (individual behaviour change) and has a limited view on midstream (working with partners and community groups) social marketing. The paper proposes midstream social marketing should also include an understanding of how services and service employees influence and support individual behaviour change goals. The paper presents four key services marketing principles – derived from services theory and thinking – which the paper believes to be essential for implementing effective midstream social marketing.

This is a conceptual paper that uses service theory and case-examples to show how service thinking can be used as a midstream social marketing approach.

For effective uptake and impact of social marketing services amongst people and populations, social marketers need to design programs that consider the service experience, the service employee, service quality/customer value and the active role of the customer in value creation.

Services marketing is a well-established sub-discipline of marketing which, until recently, has not interacted with social marketing. The extension and application of services theory for social marketing can enrich and propel the social marketing discipline forward. Further research is recommended to evaluate how service principles can be applied in practice.

Given that social marketing services tend not to be accessed in sufficient numbers by the people who most need them, social marketers need to think beyond the technical, cognitive, and organisational-focused goals when designing social services.

This paper identifies key service theories that social marketers should understand and use and is thus a source of fresh ideas for theory and practice.

This paper aims to apply the Six Sigma methodology to improve workflow by eliminating the causes of failure in the medical imaging department of a private Turkish hospital.

Implementation of the design, measure, analyse, improve and control (DMAIC) improvement cycle, workflow chart, fishbone diagrams and Pareto charts were employed, together with rigorous data collection in the department. The identification of root causes of repeat sessions and delays was followed by failure, mode and effect analysis, hazard analysis and decision tree analysis.

The most frequent causes of failure were malfunction of the RIS/PACS system and improper positioning of patients. Subsequent to extensive training of professionals, the sigma level was increased from 3.5 to 4.2.

The data were collected over only four months.

Six Sigma's data measurement and process improvement methodology is the impetus for health care organisations to rethink their workflow and reduce malpractice. It involves measuring, recording and reporting data on a regular basis. This enables the administration to monitor workflow continuously.

The improvements in the workflow under study, made by determining the failures and potential risks associated with radiologic care, will have a positive impact on society in terms of patient safety. Having eliminated repeat examinations, the risk of being exposed to more radiation was also minimised.

This paper supports the need to apply Six Sigma and present an evaluation of the process in an imaging department.

Pressure from competition; inflexible third-party reimbursements; greater demand from government, regulatory and certifying agencies; discerning patients; and the quest of healthcare entities for greater profitably place demands and high expectations for service quality impacting overall patient experience. Extending a prior multivariate, single-period model of varied medical practices predicting patient experience to a three-year time period to understand whether there was a change in overall assessment using data analytics. The paper aims to discuss these issues.

SEM was employed on a per year and aggregated, three-year basis to gain insights into qualitative psychometric constructs predicting overall patient experience and strength of the relationships.

Statistically significant differences were uncovered between years indicating the strength of the relationships of latent variables on overall performance.

Study focused on data gathered from a questionnaire mailed to patients who visited various outpatient medical clinics in a rural community with over 4,000 responses during the three-year study period. A higher percentage of female respondents over the age of 45 may limit the generalizability of the findings.

Practitioners can gain a broader understanding of different factors influencing overall patient experience. Administrative processes associated with the primary care provider are inconsequential. Patients are not as concerned with patient flow as they are with patient safety and health.

This research informs healthcare quality management of psychometrics and analytics to improve the overall patient experience in outpatient medical clinics.

It is the responsibility of the national governments to deliver healthcare services that are both effective and affordable to everyone. There are still gaps in this supply, which is extremely demanding. In this sense, companies are attempting to reach neglected markets and disrupt the marketplace with novel solutions. Although there are still anecdotal examples, a thorough literature evaluation is lacking. This study aims to provide a synthesis of the future of healthcare start-ups.

Papers that included the term “healthcare start-ups,” “health-tech start-ups,” “start-up,” “Artificial intelligence in healthcare,” and “Health tech start-ups in India” were considered for the analysis. The Biblioshiny package under the R programming tool was considered for a detailed analysis of the papers.

A total of 854 documents were related to healthcare start-ups, from which only 14 papers are related to health-tech start-ups and four papers are related to artificial intelligence in healthcare start-ups. It has been found from the past works of literature that the effectiveness of technology for information and communication in healthcare has significantly increased in recent years. Technology has already begun to permeate the healthcare market from other fields and industries. One way that the internet will help the industry evolve is by integrating digital health into daily life.

The study is not using other databases but is limited to Google Scholar and Scopus. A significant constraint of this study is the paucity of relevant literature in reputable publications on health and information systems. Another restriction was that gray literature, such as any journal or newspaper written by members of the health community about health-tech start-ups, was not taken into account.

Healthcare players should exhibit a fundamental openness to novel solutions to facilitate the digitalization of the healthcare system. Developing technology is widely used, and from an innovation perspective, a start-up should focus on innovation by employing technology and offering revolutionary healthcare solutions.

The novelty of this research is based on its presentation of an organized and thorough literature evaluation, which defines the current state of the art concerning green start-ups. To create a sustainable start-up, a thorough study of the information gained in respect of its healthcare start-up is presented.

The purpose of this paper is to present near set theory using the perceptual indiscernibility and tolerance relations, to demonstrate the practical application of near set theory to the image correspondence problem, and to compare this method with existing image similarity measures.

Image‐correspondence methodologies are present in many systems that are depended on daily. In these systems, the discovery of sets of similar objects (aka, tolerance classes) stems from human perception of the objects being classified. This view of perception of image‐correspondence springs directly from Poincaré's work on visual spaces during 1890s and Zeeman's work on tolerance spaces and visual acuity during 1960s. Thus, in solving the image‐correspondence problem, it is important to have systems that accurately model human perception. Near set theory provides a framework for measuring the similarity of digital images (and perceptual objects, in general) based on features that describe them in much the same way that humans perceive objects.

The contribution of this paper is a perception‐based classification of images using near sets.

The method presented in this paper represents a new approach to solving problems in which the goal is to match human perceptual groupings. While the results presented in the paper are based on measuring the resemblance between images, the approach can be applied to any application that can be formulated in terms of sets such that the objects in the sets can be described by feature vectors.