The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author\'s keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community\'s top keywords include \'ML\', \'Deep Learning\', and \'Artificial Intelligence\'.

The application of artificial intelligence to improve the access of cancer patients to high-quality medical care is one of the goals of modern medicine. Pathology constitutes the foundation of modern oncologic treatment, and its role has expanded far beyond diagnosis into predicting treatment response and overall survival. However, the funding of pathology is often an afterthought in resource-scarce medical systems. The increased digitalization of pathology has paved the way towards the potential use of artificial intelligence tools for improving pathologist efficiency and extracting more information from tissues. In this review, we provide an overview of the main research directions intersecting with artificial intelligence and pathology in relation to oncology, such as tumor classification, the prediction of molecular alterations, and biomarker quantification. We then discuss examples of tools that have matured into clinical products and gained regulatory approval for clinical use. Finally, we highlight the main hurdles that stand in the way of the digitalization of pathology and the application of artificial intelligence in pathology while also discussing possible solutions.

Healthcare delivery systems are widely accepted as socio-technical systems. Unlike other socio-technical systems, healthcare systems leave very little decision-making to technical automation and control. Instead, the healthcare delivery system relies on human healthcare resources for decision-making. Human decision-making is imperative to the clinical delivery of care to patients and to the operational processes that support care delivery, quality improvement, and other organizational management activities. For these clinical and operational activities, human resources make healthcare decisions using healthcare data typically housed in electronic medical records (EMRs). Unfortunately, EMR systems were first designed with the functional capability to store data, and, second to a lesser degree, to retrieve data. The literature recognizes the need to improve the retrieval of information from EMR systems. More specifically, there remains the need to directly view a patient\'s holistic health and healthcare trajectory. At this time, decision-makers are left to mentally build this holistic picture in their mind by sequentially clicking through many sections of the EMR. Therefore, in this paper, we develop a visualization tool to organize and present an individual\'s health and healthcare trajectory by describing a patient record holistically from a system architecture perspective. This approach is based on a previously developed system model for healthcare delivery and individual health outcomes.

Health care is ever more important with the aging population and with the increased awareness of the importance of the medical systems due to the corona crisis that showed the capacity of the health care infrastructure, especially in terms of numbers of health care personnel such as doctors, was not sufficient. Assuming that the number of doctors per patient is one of the determinants of patient satisfaction, optimal investments in new doctors, specialist doctors and foreign doctors are analyzed. Optimal Control Theory is employed to determine the optimal investment strategy for new doctors (new graduates), specialists and foreign doctors to maximize the net (of costs) patient satisfaction over a fixed time horizon. It is found that a nation with an insufficient number of total doctors and specialist doctors at the beginning of the planning horizon should increase the investment in new doctors as a quadratic function of time, increase the local specialist doctors linearly, while employing foreign doctors as to equate their cost to the marginal satisfaction of patients.

Primary Health Care (PHC), based on the Alma Ata declaration, calls for the movement of responsibility, resources, and control away from medical systems and curative measures toward health promotion. However, PHC implementation in practice appears to be heavily influenced by medical systems with its own attendant effects on the attainment of PHC goals. This study therefore examines the extent and effects of medical systems influence on PHC implementation in Ghana. The study uses the thematic framework approach to qualitative data analysis to analyze data collected from PHC managers through interviews. Ethical clearance for the study was obtained from the Noguchi Memorial Institute for Medical Research. Findings suggest that PHC in practice is tied to the apron-strings of medical systems. While this has catalyzed successes in disease control programs and other medicine-based interventions, it has swayed PHC from its intended shift toward health promotion. Community ownership, participation, and empowerment in PHC is therefore lost in the maze of medical systems which reserves power over PHC decision making and implementation to medical professionals while focusing attention on treatment and curative services. Ultimately, PHC has gradually metamorphosed into mini-clinics instead of the revolutionary community-driven promotive services espoused by Alma Ata with concomitant effects on the attainment of Universal Health Coverage. Further, findings show how gradually, the primary in PHC is being used as a descriptor of the first or basic level of hospital-based care instead of a first point of addressing existing health problems using preventive, promotive, and other community driven approaches. Without a reorientation of health systems, significant efforts and resources are channeled toward empowering health workers instead of local communities with significant effects on the long term sustainability of health efforts and the attainment of UHC. The study recommends further studies toward practical means of reducing the influence of medical systems.

The novel SARS-CoV-2 (COVID-19) disrupted many facets of the healthcare industry throughout the pandemic and has likely permanently altered modern healthcare delivery. It has been shown that existing healthcare infrastructure influenced national responses to COVID-19, but the current implications and resultant sequelae of the pandemic on the organizational framework of healthcare remains largely unknown. This paper aims to review how aspects of contemporary medical systems - the physical environment of care delivery, global healthcare supply chains, workforce structures, information and communication systems, scientific collaboration, as well as policy frameworks - evolved in the initial response to the COVID-19 pandemic.

Introduction: Developing a new and innovative acupuncture technique to deal with acute and chronic pain is a considerable undertaking in itself as many barriers must be overcome. Designing the technique and gaining acceptance is even more daunting and challenging across a large system of medical facilities such as the Department of Defense and the Veterans Administration. Such was the case of Battlefield Acupuncture (BFA), a 5-needle placement in each external ear into specific auricular points to treat acute and chronic pain. Discussion: This technique geared for military deployment is rapid, safe, and easily reproducible without side effects because it uses a choice acupuncture microsystem, namely the ear. Acquiring acupuncture practical skills is the prerequisite before designing the technique and then taking into consideration, neurophysiology data, and actual clinical experience to finally focus development is demanding. Conclusions: The benefits of BFA that was promulgated in a Joint Incentive Fund for $5.4 million resulted in thousands of trained health care providers and eventually many clinical trials to help validate the technique. Process improvement data were beneficial and important for its sustainment. There are 5 steps recommended for overcoming the barriers.

The novel coronavirus disease-19 (COVID-19) infection has altered the society, economy, and entire healthcare system. Whilst this pandemic has presented the healthcare system with unprecedented challenges, it has rapidly promoted the adoption of telemedicine to deliver healthcare at a distance. Telemedicine is the use of Information and Communication Technology (ICT) for collecting, organizing, storing, retrieving, and exchanging medical information. But it is faced with the limitations of conventional IP-based protocols which makes it challenging to provide Quality of Service (QoS) for telemedicine due to issues arising from network congestion. Likewise, medical professionals adopting telemedicine are affected with low QoS during health consultations with outpatients due to increased internet usage. Therefore, this study proposes a Software-Defined Networking (SDN) based telemedicine architecture to provide QoS during telemedicine health consultations. This study utilizes secondary data from existing research works in the literature to provide a roadmap for the application of SDN to improve QoS in telemedicine during and after the COVID-19 pandemic. Findings from this study present a practical approach for applying SDN in telemedicine to provide appropriate bandwidth and facilitate real time transmission of medical data.

The current coronavirus disease 2019 (COVID-19) pandemic has caused significant strain on medical centers resources. Thus, concerns about the reducing and management of COVID-19 are on the rise, as there is need to provide diagnosis, treatment, monitoring, and follow-ups during the pandemic. Therefore, the COVID-19 pandemic has radically and quickly altered how medical practitioners provide care to patients. Medical centers are now responding to COVID-19 through rapid adoption of digital tools and technologies such as telemedicine and virtual care which refer to the delivery of healthcare services digital or at a distance using Information and Communications Technology (ICT) for treatment of patients. Telemedicine is expected to deliver timely care while minimizing exposure to protect medical practitioners and patients. Accordingly, a rapid literature review was conducted, and 35 research studies published from 2019 to May 2020 were employed to provide theoretical and practical evidence on the significance of using telemedicine and virtual care for remote treatment of patients during the COVID-19 pandemic. This article provides practical guide based on how to use telemedicine and virtual care during the COVID-19 pandemic. This study provides implication on the potentials of consolidating virtual care solutions in the near future towards contributing to integrate digital technologies into healthcare.

This article explores the performance optimizations of an embedded database memory management system to ensure high responsiveness of real-time healthcare data frameworks. SQLite is a popular embedded database engine extensively used in medical and healthcare data storage systems. However, SQLite is essentially built around lightweight applications in mobile devices, and it significantly deteriorates when a large transaction is issued such as high resolution medical images or massive health dataset, which is unlikely to occur in embedded systems but is quite common in other systems. Such transactions do not fit in the in-memory buffer of SQLite, and SQLite enforces memory reclamation as they are processed. The problem is that the current SQLite buffer management scheme does not effectively manage these cases, and the naïve reclamation scheme used significantly increases the user-perceived latency. Motivated by this limitation, this paper identifies the causes of high latency during processing of a large transaction, and overcomes the limitation via proactive and coarse-grained memory cleaning in SQLite.The proposed memory reclamation scheme was implemented in SQLite 3.29, and measurement studies with a prototype implementation demonstrated that the SQLite operation latency decreases by 13% on an average and up to 17.3% with our memory reclamation scheme as compared to that of the original version.