Explore the vast range of titles available.

With the healthcare sector accounting for a sizeable proportion of national expenditures, the pursuit of efficiency has become a central objective of policymakers within most health systems. However, the analysis and measurement of efficiency is a complex undertaking, not least due to the multiple objectives of health care organizations and the many gaps in information systems. In response to this complexity, research in organizational efficiency analysis has flourished. This 2006 book examines some of the most important techniques currently available to measure the efficiency of systems and organizations, including data envelopment analysis and stochastic frontier analysis, and also presents some promising new methodological approaches. Such techniques offer the prospect of many new and fruitful insights into health care performance. Nevertheless, they also pose many practical and methodological challenges. This is an important critical assessment of the strengths and limitations of efficiency analysis applied to health and health care.

Data is revolutionizing the healthcare industry. With more data available than ever before, and applying the right analytics you can spur growth. Benefits extend to patients, providers, and board members, and the technology can make centralized patient management a reality. Despite the potential for growth, many in the industry and government are questioning the value of data in health care, wondering if it's worth the investment. This book tackles the issue and proves why BI is not only worth it, but necessary for industry advancement. Madsen challenges the notion that data has little value in healthcare, and shows how BI can ease regulatory reporting pressures and streamline the entire system as it evolves. She illustrates how a data-driven organization is created, and how it can transform the industry. --

Quality in a lean health care setting has one ultimate goal--to improve care delivery and value for the patient. The purpose of this book is to provide a blueprint to hospitals, healthcare organizations, leaders, and patient-facing workers with tools, training, and ideas to address quality within their organization. Examples from health care and other industries are provided to illustrate lean methodology, and its application in quality. The reader will learn how other organizations can improve their quality, know what their roles are, and know what they do daily. By the end of the book, you will have learned actionable concepts and have the tools and resources to start improving quality.

This study examines the current state of artificial intelligence (AI)-based technology applications and their impact on the healthcare industry. In addition to a thorough review of the literature, this study analyzed several real-world examples of AI applications in healthcare. The results indicate that major hospitals are, at present, using AI-enabled systems to augment medical staff in patient diagnosis and treatment activities for a wide range of diseases. In addition, AI systems are making an impact on improving the efficiency of nursing and managerial activities of hospitals. While AI is being embraced positively by healthcare providers, its applications provide both the utopian perspective (new opportunities) and the dystopian view (challenges to overcome). We discuss the details of those opportunities and challenges to provide a balanced view of the value of AI applications in healthcare. It is clear that rapid advances of AI and related technologies will help care providers create new value for their patients and improve the efficiency of their operational processes. Nevertheless, effective applications of AI will require effective planning and strategies to transform the entire care service and operations to reap the benefits of what technologies offer.

This study is to explore a state of the art in metaverse service that is an emerging issue in applying it to the healthcare industry. The purpose of this study is to provide applicable strategic scenarios for effective metaverse service planning and implementation in healthcare settings. This study is focused on metaverse service as a business model. Thus, related literatures of metaverse service are reviewed in various aspects in healthcare industry. An exploratory approach is used to analyze current qualitative data characterizing healthcare metaverse service business positions and derive applicable strategies from business trends of current metaverse services. Several cases are examined based on the data obtained from various sources of healthcare and other related industries. This study synthesizes finding results and suggests applicable strategies of metaverse service in the healthcare industry. This study will facilitate strategic decision-making and policy-making processes to pursue a business opportunity development through an application of a metaverse service in healthcare and similar settings.

Purpose This study aims to access, analyze and highlight opportunities and problems of the Indian pharmaceutical sector in the broader national health-care industry. The recent changes in the field, at the institutional and corporate levels, have placed India in the spotlight of the global pharmaceutical market, but several threats and weaknesses could limit this expansion. Design/methodology/approach Descriptive and inferential analyses have been based on empirical data extracted from authenticated data sources. Subsequently, a narrative strengths, weaknesses, opportunities and threats analysis was performed based on the results of prior investigations and on qualitative data that were retrieved from a marketing intelligence examination to generate an overall scenario analysis. Findings Indian pharmaceutical companies have faced several challenges on various fronts. In the home market, drug prices are controlled by the drug price control order; therefore, there is strong pressure on revenues and subsequently on costs. In the international market, threats derived from pharmaceutical multinational companies are emerging as tough obstacles to overcome. Practical implications More focus on patents for innovative drugs is required, instead of concentrating primarily on generic drugs. There is a need for policymakers to work on the sustainability and development of the industry, while the companies must redesign their orientation toward enhancing innovation capabilities. In addition, at the level of corporate strategy, firms should establish collaborations and alliances and expand their industrial marketing vision. Originality/value This study provides a global overview of the potential growth and development of the Indian pharmaceutical sector, comparing it with internal trends and external competition. The most relevant contribution of the research relies on the shift to innovative production that Indian companies must adopt (after years of focusing only on generic drugs), and in this vein, appropriate industrial marketing solutions are indispensable.

Health care insurance fraud is on the rise in many ways, such as falsifying information and hiding third-party liability. This can result in significant losses for the medical health insurance industry. Consequently, fraud detection is crucial. Currently, companies employ auditors who manually evaluate records and pinpoint fraud. However, an automated and effective method is needed to detect fraud with the continually increasing number of patients seeking health insurance. Blockchain is an emerging technology and is constantly evolving to meet business needs. With its characteristics of immutability, transparency, traceability, and smart contracts, it demonstrates its potential in the health care domain. In particular, self-executable smart contracts are essential to reduce the costs associated with traditional paradigms, which are mostly manual, while preserving privacy and building trust among health care stakeholders, including the patient and the health insurance networks. However, with the proliferation of blockchain development platform options, selecting the right one for health care insurance can be difficult. This study addressed this void and developed an automated decision map recommender system to select the most effective blockchain platform for insurance fraud detection. This study aims to develop smart contracts for detecting health care insurance fraud efficiently. Therefore, we provided a taxonomy of fraud scenarios and implemented their detection using a blockchain platform that was suitable for health care insurance fraud detection. To automatically and efficiently select the best platform, we proposed and implemented a decision map-based recommender system. For developing the decision-map, we proposed a taxonomy of 102 blockchain platforms. We developed smart contracts for 12 fraud scenarios that we identified in the literature. We used the top 2 blockchain platforms selected by our proposed decision-making map-based recommender system, which is tailored for health care insurance fraud. The map used our taxonomy of 102 blockchain platforms classified according to their application domains. The recommender system demonstrated that Hyperledger Fabric was the best blockchain platform for identifying health care insurance fraud. We validated our recommender system by comparing the performance of the top 2 platforms selected by our system. The blockchain platform taxonomy that we created revealed that 59 blockchain platforms are suitable for all application domains, 25 are suitable for financial services, and 18 are suitable for various application domains. We implemented fraud detection based on smart contracts. Our decision map recommender system, which was based on our proposed taxonomy of 102 platforms, automatically selected the top 2 platforms, which were Hyperledger Fabric and Neo, for the implementation of health care insurance fraud detection. Our performance evaluation of the 2 platforms indicated that Fabric surpassed Neo in all performance metrics, as depicted by our recommender system. We provided an implementation of fraud detection based on smart contracts.

[This corrects the article DOI: 10.1371/journal.pone.0331453.].