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Theory of Constraints (TOC), though a well-established process improvement methodology in manufacturing, is still a novel philosophy for healthcare and an exhaustive review of literature is needed to summarize the key findings of various researchers. Such a review can provide a direction to the researchers and academicians interested in exploring the application of TOC in the healthcare sector. This paper aims to review the existing literature of TOC tools and techniques applied to the healthcare environment, and to investigate motivating factors, benefits and key gaps for identifying directions for future research in the domain of healthcare.

In this paper, different electronic repositories were searched using multiple keywords. The current study identified 36 articles published between January 1999 to mid-2021 to conceptualize and summarize the research questions used in the study. Descriptive analysis along with pictorial representations have been used for better visualization of work.

This paper presents a thorough literature review of TOC in healthcare and identifies the evolution, current trends, tools used, nature of services chosen for application and research gaps and recommends future direction for research. A variety of motivating factors and benefits of TOC in healthcare are identified. Another key finding of this study is that almost all implementations listed in literature reported positive outcomes and substantial improvements in the performance of the healthcare unit chosen for study.

This paper provides valuable insight to researchers, practitioners and policymakers on the potential of TOC to improve quality of services, flow of patients, revenues, process efficiency and cost reduction in different health care settings. A number of findings and suggestions compiled in the paper from literature study can be used for diagnosing, learning and making substantial changes in healthcare. The methodologies used by different researchers were analysed and combined to propose a generic step by step procedure to apply TOC. This methodology will guide the practising managers about the appropriate tools of TOC for their specific need.

Good health is always the first desire of all men and women around the globe. The global aim of healthcare is to quickly cure more patients and ensure healthier population both today and in future. This article will work as a foundation for future applications of TOC in healthcare and guide upcoming applications in the booming healthcare sector. The paper will help the healthcare managers in serving a greater number of patients with limited available resources.

This paper provides original collaborative work compiled by the authors. Since no comprehensive systematic review of TOC in healthcare has been reported earlier, this study would be a valuable asset for researchers in this field. A model has been presented that links various benefits with one another and clarifies the need to focus on process improvement which naturally results in these benefits. Similarly, a model has been presented to guide the users in implementation of TOC in healthcare.

The purpose of this paper is to conduct a comprehensive review and assessment of the extant Six Sigma healthcare literature, focusing on: application, process changes initiated and outcomes, including improvements in process metrics, cost and revenue.

Data were obtained from an extensive literature search. Healthcare Six Sigma applications were categorized by functional area and department, key process metric, cost savings and revenue generation (if any) and other key implementation characteristics.

Several inpatient care areas have seen most applications, including admission, discharge, medication administration, operating room (OR), cardiac and intensive care. About 42.1 percent of the applications have error rate as their driving metric, with the remainder focusing on process time (38 percent) and productivity (18.9 percent). While 67 percent had initial improvement in the key process metric, only 10 percent reported sustained improvement. Only 28 percent reported cost savings and 8 percent offered revenue enhancement. These results do not favorably assess Six Sigma's overall effectiveness and the value it offers healthcare. Results are based on reported applications. Future research can include directly surveying healthcare organizations to provide additional data for assessment.

Future application should emphasize obtaining improvements that lead to significant and sustainable value. Healthcare staff can use the results to target promising areas.

This article comprehensively assesses Six Sigma healthcare applications and impact.

The purposes of this paper are (1) to explore the overall development of AI technologies and applications that have been demonstrated to be fundamentally important in the healthcare industry, and their related commercialized products and (2) to identify technologies with promise as the basis of useful applications and profitable products in the AI-healthcare domain.

This study adopts a technology-driven technology roadmap approach, combined with natural language processing (NLP)-based patents analysis, to identify promising and potentially profitable existing AI technologies and products in the domain of AI healthcare.

Robotics technology exhibits huge potential in surgical and diagnostics applications. Intuitive Surgical Inc., manufacturer of the Da Vinci robotic system and Ion robotic lung-biopsy system, dominates the robotics-assisted surgical and diagnostic fields. Diagnostics and medical imaging are particularly active fields for the application of AI, not only for analysis of CT and MRI scans, but also for image archiving and communications.

This study is a pioneering attempt to clarify the interrelationships of particular promising technologies for application and related products in the AI-healthcare domain. Its findings provide critical information about the patent activities of key incumbent actors, and thus offer important insights into recent and current technological and product developments in the emergent AI-healthcare sector.

The purpose of this paper is to identify the existing literature on the wide range of operations research (OR) studies applied to healthcare, and to classify studies based on application type and on the OR technique employed. The scope of the review is limited to studies which have been undertaken in the UK, and to papers published since the year 2000.

In total, 142 high‐quality journal and conference papers have been identified from ISI Web of Knowledge data base for review and analysis.

The findings categorise the OR techniques employed, and analyse the application type, publication trends, funding, and software packages used in the twenty‐first century in UK healthcare. Publication trends indicate an increasing use of OR techniques in UK healthcare. The findings show that, interestingly, the distribution of the OR techniques employed is not uniform; the majority of studies focus on simulation, either as the only technique employed or as one element of a multi‐method approach.

Several studies have focused on the use of simulation in healthcare modelling, but none has methodologically reviewed the use of the full range of OR techniques. This research is likely to benefit healthcare decision makers since it will provide them with an overview of the different studies that have utilised multiple OR techniques for investigating problems in the stated domain.

The objective of this paper is to propose a systematic application of Lean Healthcare in the hospitalization activity in the medical clinic entry process.

The methodology used is established in three stages: the first aims to map the process in which the focus activity is inserted, using lean tools, as well as integrating the employees involved in the application. The second is the proposal to apply the systematic, together with the employees, using the A3 tool step by step. Finally, the third stage confirms the applied systematic, collecting the results and analyzing the initial situation with those reached.

As a result, improvements were made in the medical clinic entry process, such as reduced waiting time for patients, at approximately 53.8%, with a decrease in the standard deviation of the times – of approximately 79.14%, and displacement of those involved, of 72%, in addition to eliminating unnecessary activities for the process. Furthermore, the empirical results on the efficiency of this systemic application in medical clinic enable the replication of this proposal, generating a systematic.

Despite establishing a systematic proposal with real results, it is focused on only one application, due to time limitations, may generate a subjective evaluation of the systematic. Thus, for future research, it is recommended to expand this systemic application in other activities of different processes.

The practical implications of this paper are precisely related to the data obtained with the application made, developing a Lean Healthcare systematic not previously seen, which is strategic, systemic and has a roadmap to assist in its application and, in addition, brings with it practical results that prove their efficiency.

The social implications of this paper are presented in its empirical results, considering that the study hospital serves, in addition to its host city, 28 other smaller municipalities around it, improving the flow of processes, ensuring better management of the clinic doctor. In addition, the results can assist the processes flow of other medical clinics in hospitals around the world, especially at critical moments, such as pandemics or epidemics.

Due to the positive results obtained in the systematic application, this paper fills a gap identified in the literature, proposing a systematic application of Lean Healthcare that is systemic and strategic, in addition to including a roadmap and analysis of data applied in a medium-sized Brazilian hospital, presenting positive practical results exposed in the paper.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.

By applying a systematic literature review, this paper aims to identify the major healthcare problem domains (i.e. target areas) for lean supply chain management (LSCM) and to provide a list of the most common techniques for implementing LSCM in healthcare. Moreover, this study intends to investigate various contingency factors that may have influenced the selection of LSCM target areas or the application of LSCM techniques by healthcare organizations.

A systematic literature review was carried out following the method presented by Tranfield et al. (2003). Thereby, 280 peer-reviewed journal articles, published between 1995 and 2018, were selected, profiled and reviewed. In total, 75 papers were also selected for a qualitative analysis, known as meta-study, on the basis of high relevancy to the research objectives.

This work extracts, from previous research, a set of target areas for improving supply chain in healthcare by applying lean approaches. The work also unifies the language of lean thinking and supply chain in healthcare by defining metaphors in circumstances under which healthcare organizations pursue similar objectives from their supply chain management and lean programs (Schmitt, 2005). This paper also outlines a list of applications of lean for supply chain improvement in healthcare. Finally, a set of contingency factors in the field of lean supply chain in healthcare is found via the published literature.

This paper provides insights for decision-makers in the healthcare industry regarding the benefits of implementing LSCM, and it identifies contingency factors affecting the implementation of LSCM principles for healthcare. Implementing LSCM can help healthcare organizations improve the following domains: internal interaction between employees, supply chain cost management, medication distribution systems, patient safety and instrument utilization.

The research shows potential synthesis of LSCM with the healthcare industry’s objectives, and, thus, the outcome of this research is likely to have positive influence on the quality and cost of healthcare services. The objectives of the healthcare industry are cost reduction and providing better service quality, and LSCM implementation could be an effective solution to help healthcare to achieve these objectives.

The prime value of this paper lies in conducting a systematic literature review using a meta-study to identify the major factors of implementing LSCM in healthcare. Only a few other studies have been published in the literature about LSCM in healthcare.

Introduction: Healthcare facilities have witnessed deterioration, limited employee engagement, and communication gaps due to a lack of wireless technology. The Internet makes work and life quicker and more intelligent. The Internet of Things (IoT) is a scheme of interconnection equipped with unique identifiers in recent years. Artificial intelligence (AI) and IoT advancement allow employees to develop competent and predictive services and solutions in human resource (HR) practices. This chapter has been formulated to summarise and classify the existing research and better understand the past, present, and future of employee engagement by improving IoT interrelated devices in the healthcare industry.

Purpose: This study aims to categorise and overcome the challenges involved in HR practices. Effectively embracing IoT application-connected devices in the healthcare industry can enhance human resources management’s (HRM) role and measure performance assessment to improve employee engagement and productivity.

Methodology: In this study, the authors develop propositions dependent on a theory-based review. A systematic analysis was applied to minimise the challenges of HRM. The subject-related articles from different journal sources, like Scopus, Emerald, Web of Science, Springer, etc., were analysed based on engagement criteria. It was graphically recorded in a collective and informative way to emphasise the review outcomes. The study has presented the positive impacts of AI and IoT on engagement in health care.

Summary: This chapter accumulated theory-based knowledge about healthcare employee engagement and how IoT-based technology like AI can optimise employees’ engagement effectively. Further, it draws comparative benefits for a workforce to execute performance advancements and create future progressive aspects for healthcare employees.

Technological developments have shed optimistic light on the future of telecommunications in healthcare. However, problems still prevail in the healthcare industry and the need for an effective solution in a rapidly evolving technological environment is imperative in the coming years. This paper defines the problem within healthcare delivery worldwide and theoretically explores a typical medical scenario in Kuwait, utilising the grounded theory method. It traces the social processes within medical work and network and attempts to understand the underlying relationships between the two. Analysis of the scenario leads to an understanding of the concepts and categories, enabling the interpretation of a theory that forms the basis of an architectural model, resulting in the proposition of a new telehealth paradigm, the pay‐per‐use concept. The research question focuses on the appropriateness of such a concept for the healthcare industry. Anticipates that the proposed new conceptual framework will be the evolving IT solution in healthcare delivery.