Abstract Rural health systems are networks, which are geographically disseminated and resource limited, in which inefficient inter-hospital coordination has a strong influence on patient outcomes, operational stability and surgical resilience. Regardless of the development of smart hospital technologies, such as 5G-enabled communication opportunities, the integration of digital coordination centers, and telemedicine, the current frameworks are more focused on streamlining intra-hospital processes instead of the inter-hospital distribution of resources. This structural disintegration leads to slow shifts, poor use of bed space, inaccessibility of specialists, and poor responsiveness to surges. This paper suggests Smart Inter-Hospital Representation Network (SIHCN) to be a rural hospital ecosystem distributed systems architecture. The framework combines a granted blockchain based resource registry, real-time capacity monitoring strategies, specialist allocation registries, and adaptive routing logic into a coordination infrastructure. The proposed architecture will be able to guarantee decentralized system control against centralized command models, fault tolerance, and scalable interoperability among autonomous hospital nodes. The paper introduces a conceptual systems model that specifies the network topology, operational data flow, distributed resource synchronization and performance evaluation metrics. The simulation modeling is based on a scenario simulation that assesses the system performance when under routine and emergency surge conditions, showing that the transfer latency, resource balancing, and coordination efficiency is improved. The results make distributed ledger-based coordination a potential engineering technique in enhancing the resilience of rural health networks. This study also addresses the Healthcare Systems Engineering field by re-conceptualizing rural hospital coordination as a distributed resource optimization problem and suggesting an architecture-layer solution that can be applied to low-density, high-variability healthcare settings.
Ravi Naik, Kaizhe Jin, Alexandros Kogkas, Hutan Ashrafian · 6 authors
The operating room represents a high-risk environment centred around the safe and efficient delivery of patient care. It is a complex ecosystem that encompasses many factors including communication within the multidisciplinary surgical team often led by the operating surgeon as well as execution of precise technical surgical skill. These factors are associated with the mental or cognitive workload (CWL) of the surgeon. CWL, also described as the mental effort exerted while undertaking a task, is a construct derived from the cognitive load theory first described in the eighties during problem solving exercises [1]. There has been a growing emphasis on the measurement of CWL since then on individuals working in high-stake environments such as aviation [2]. Measurement of CWL in surgery is moving from solitary traditional subjective measures such as the Surgical Task Load Index (SURG-TLX) to objective measurement of physiological parameters secondary to changes in the CWL of the surgeon which are less exposed to subjective bias [3]. These have included heart rate variability (HRV), pupil metrics, electromyography (EMG), electroencephalography (EEG), skin conductance and functional near-infrared spectroscopy (fNIRS). More recently, there is increasing evidence to demonstrate the use of multiple sensors, or a multimodal sensor system designed to measure CWL with greater accuracy [4]. The aim of this paper is to demonstrate the use of a pilot synchronised system of multiple sensors to measure the real-time cognitive workload of surgeons in a simulated setting to demonstrate a proof of concept and to discuss the early findings.
Shared decision making has become a crucial solution to build a consolidated healthcare system. While there is some research in the healthcare literature discussing the advantages and disadvantages of shared decision making, its efficiency has not been addressed quantitatively. In this paper, we propose a Decentralized Patients Assignment System (DPAS) as a universal decentralized decision making architecture. It utilizes the blockchain technology, machine learning, and integer programing to enhance coordination among healthcare providers and patients in consolidated hospital systems. To test the efficiency of the proposed DPAS, a prototype system is developed using an Agent-based model and Ethereum and is compared to the current practice of central referral systems in consolidated hospital systems. The agent-based model consists of four agents including patients, physicians, hospitals, and miners interacting within a decentralized system. The proposed system highlights the importance of interoperability and consensus among healthcare agents in the decision making process. The results demonstrate the DPAS efficiency in decreasing computational time and rejection rates for patients transfer.
The healthcare system in the United States is comparatively superior to healthcare systems of other countries in terms of advanced and modern technology, drugs used, services offered, and required care. However, hospital management has to face the challenges in the managing of care and addressing safety issues for its patients due to the multiple stakeholders involved such as medical doctors, residents, nurses, diagnostic tool providers, as well as patients. Every year many people lose their lives due to medical errors caused by new employees in hospitals, errors that can be prevented by âmistake-proofing.â Similarly, teaching hospitals face an increase in medical errors in the month of July due to cohort turnover, which occurs when trained residents graduate and new ones begin their residency, resulting in increased fatalities and mishaps; this phenomenon is called the âJuly effect.â This sudden changeover of workforce puts the quality of healthcare in teaching hospitals at stake. In this paper, we discuss various reasons behind the July effect and several tools of quality that can be implemented to improve healthcare and increase patientsâ safety.
<strong>Background</strong> Regional-based Integrated Healthcare Networks (IHNs) have been promoted in Brazil to overcome the fragmentation due to the health system decentralization to the municipal level; however, evaluations are scarce. The aim of this article is to analyse the content of IHN policies in force in Brazil, and the factors that influence policy implementation from the policymakersâ perspective. <strong>Methods</strong> A two-fold, exploratory and descriptive qualitative study was carried out based on (1) content analysis of policy documents selected to meet the following criteria: legislative documents dealing with regional-based IHNs; enacted by federal government; and in force, (2) semi-structured individual interviews were conducted to a theoretical sample of policymakers at federal (eight), state (five) and municipal levels (four). Final sample size was reached by saturation of information. An inductive thematic analysis was conducted. <strong>Results</strong> The results show difficulties in the implementation of IHN policies due to weaknesses that arise from the policy design and the performance of the three levels of government. There is a lack of specificity as to the criteria and tools for configuring and financing IHNs that need to be agreed upon between involved governments. For their part, policymakers emphasize the difficulty of establishing agreements in a health system with disincentives for collaboration between municipalities. The allocation of responsibilities that are too complex for the capacity and size of the municipalities, the abandonment of essential functions such as network planning by states and the strategic role by the Ministry, the âinvasionâ of competences among levels of government and high political turnover are also highlighted. <strong>Conclusions</strong> The implementation of regional-based IHN policy in Brazil is hampered by the decentralized organization of the health system to the municipal level, suggesting the need to centralize certain functions to regional structures or states and to define better the role of the government levels involved.