Health Systems最新文献

The dynamic nature of epidemic diseases presents significant challenges for containment and healthcare resource allocation, particularly as viral strains evolve and outbreak conditions shift over time. While interventions such as testing, vaccination, and quarantine have been widely implemented, most models assess these strategies in isolation. However, we evaluate the combined impact of all aforementioned interventions and optimize resource allocation for maximum effectiveness. This study introduces an adaptive compartmental epidemiological model (SEIR) that integrates dynamic vaccination accessibility and diagnostic surveillance testing strategies, allowing for optimized intervention strategies in response to real-time outbreak progression and demographic variations. Simulation results demonstrate that vaccination effectively reduces infection peaks, while adaptive testing strategies delay peak occurrences and mitigate severity by continuously adjusting to outbreak dynamics and available healthcare resources. By integrating real-time surveillance, strategic testing allocation, and vaccination planning, this model provides a scalable and flexible framework for epidemic preparedness. These findings offer actionable insights for policymakers, guiding the development of robust surveillance systems, optimized resource distribution, and predictive epidemic control measures to mitigate future outbreaks.

In the pursuit of revolutionising health monitoring, this study introduces an IoT-based smart health monitoring system coupled with a machine learning classification framework. This innovative system tracks five crucial health parameters - Temperature, SPO2, Glucose level, Pulse rate, and Heart rate - providing a comprehensive overview of an individual's health status in real-time. Leveraging these parameters, a dataset is constructed, facilitating the application of four distinct machine learning algorithms: Support Vector Machine (SVM), Decision Tree, Random Forest, and CN2 rule induction. Remarkably, the classification accuracy achieved by these models demonstrates their efficacy, with SVM scoring 0.859, Tree achieving 0.996, Random Forest attaining 0.984, and CN2 rule induction reaching 0.902, respectively. Notably, among these algorithms, the Tree model emerges as the most superior, showcasing its potential for effectively analysing this type of dataset and enhancing the performance of health monitoring systems. Further, ThingSpeak has been utilised as IoT platform within our health monitoring system that facilitates the seamless collection of real-time data from diverse medical devices such as heart rate monitors and glucose metres. With applications in healthcare, home monitoring, sports, fitness, and industrial safety, the system offers versatile solutions for proactive health management and improved well-being.

Modelling and simulation studies have been used to inform the choices and development of quality improvement (QI) initiatives in health care, for example, by helping refine the intervention to be implemented or support decisions around the management of demand and capacity. We do not know whether a modelling study can itself be informed by a QI project and what are the associated benefits and challenges. In this research, we sought to investigate the opportunities and challenges associated with an ongoing health service-led QI project in informing the development of a stochastic simulation-based decision support tool to inform decisions around the commissioning of anticoagulation services for patients with atrial fibrillation. We found that the positive synergies offered by the QI project included good access to stakeholders and envisaged end users, co-producing relevant and impactful scenarios for experimentation, as well as access to good quality individual patient level data. On the other hand, substantial effort was required to populate input parameters with values that pertain to the natural history of the disease and the effectiveness of the different treatments. Our findings indicate that, if stakeholders require modelling results to inform aspects of a QI project, upfront investment is needed to ensure timely interaction between the two studies.

Healthcare is facing a crisis globally, with rising demand and technological advances escalating costs and outpacing supply. The healthcare supply chain (HCSC) encompasses various links, from primary and specialty care to hospitals, which often fail to function quickly, seamlessly, or cost-effectively individually or together. Indeed, the complexities of healthcare make this a "wicked problem" without easy solutions. Research has typically focused on individual links in the supply chain oversimplifying and neglecting their interdependence. Key characteristics - such as the system's hierarchical structure, diverse stakeholder involvement, interdependencies among links, the importance of timeliness, and the need to cope with complexity, change, and uncertainty - are frequently overlooked. Addressing the healthcare crisis requires a pragmatic approach to improving service delivery. This paper advocates for a systems perspective, allowing a breakdown of the problem into manageable units of analysis based on the system hierarchy, viewing each link in the HCSC as integral to the whole. We outline a multimethodology that capitalises on HCSC characteristics to enhance patient flow and provide timely, high-quality, and cost-effective care. It emphasises classifying, prioritising, and synchronising treatment based on urgency. The paper also discusses existing solutions to the system's components and presents a comprehensive strategy for the overall issue.

Home healthcare capacity is under great pressure due to demographic developments. Existing literature has exclusively focused on the planning, scheduling, and routing of non-acute care activities. However, similar to other healthcare settings, home healthcare also experiences acute care activities that disrupt operational performance. We study the planning and control of an acute care team for dealing with unplanned and urgent home healthcare activities. Particularly, we focus on determining the number of nurses per care level and their standby locations. The primary aim of this study is to introduce this novel problem, which we define as the acute care team location problem. We formulate this problem as a chance-constrained program. We solve the single location problem to optimality, and the multi-location problem with sample average approximation. The results show that our approach enables decision makers to optimally configure their acute care team, to respond quickly to acute care incidents. From a managerial perspective, our research provides a model that supports tactical capacity planning in HHC organisations and presents a benchmark for acute care management policies.

Frailty is common in older adults and has a substantial impact on patient outcomes and service use. Information to support service planning, including prevalence in middle-aged adults and patterns of frailty progression at population level, is scarce. This paper presents a system dynamics model describing the dynamics of frailty and ageing within a population of patients aged ≥50, based on linked data for 2.2 million patients from primary care practices in England. The purpose of the model is to estimate the incidence and prevalence of frailty in an ageing population over time. The model was developed in consultation with stakeholders (patients, carers, clinicians, and commissioners) and validated against another large dataset (1.38 million patients) from Wales. It was then scaled up to the population of England, using Office for National Statistics projections (to 2027). The baseline results, subject to the assumption that the frailty transition parameters remain constant over this period, suggest that the number of people living with frailty will increase as the population ages, and that those with mild-moderate frailty are likely to have the greatest impact on demand. This paper focuses on model development and validation, highlighting the benefits and challenges of using large routine health datasets.

The aim of this study is to investigate the smoking cessation tendencies of people with different levels of smoking addiction during the COVID-19 epidemic process and to investigate the variables that most affect the risk perception of being Covid-19. Data were collected between November 8 and December 20 2021. A total of 898 participants living in the Turkey aged 18 years or older were recruited from Google online form panel. In general, it can be said that the higher the education level and the higher the income, the better the people adapt to the epidemic conditions. During the epidemic, it is seen that people generally (88.2%) get the news about the process from official sources. It is seen that the participants trust the ministry of health and the scientific committee at a rate of 67.1% in the epidemic management. It is possible that those who have chronic diseases and are addicted to cigarettes will experience the COVID-19 process more heavily. However, in this study, it could not be said that those who have a chronic disease and are addicted to smoking are more inclined to quit smoking.

We examine the information acquisition process regarding a patient's status under emergency department (ED) congestion conditions. We focus on two key information channels: 1) Electronic Health Record (EHR) that provide the patient's medical history and 2) Medical tests conducted in real-time. Whereas the EHR provides the physician with easily accessible information with little delay, real-time medical tests can provide more current information, but are time-consuming. We examine physicians' decisions in cases of ED congestion, using a dataset that includes more than 1.4 million visits. When congestion is low, the information channels are complementary - acquiring information from the EHR is positively correlated with information acquisition from the medical tests channel, representing an incentive for the physician to acquire all possible information before providing diagnosis. However, as the congestion increases, there is less reliance on medical tests; this effect is amplified when EHR information is used. To avoid excessive congestion, physicians apparently refrain from sending patients for medical tests, and compensate for loss of information using EHR information. The impact of high system workload on the quality of medical service is an essential concern for managers; we show the indirect benefit of investment in EHRs through reduced blood-tests without increasing revisit rates.

Preeclampsia, a life-threatening condition in late pregnancy, has unclear causes and risk factors. Machine learning (ML) offers a promising approach for early prediction. This systematic review analyzes state-of-the-art studies on preeclampsia prediction using ML approaches. We reviewed articles published between January 1 2013 and December 31 2023, from Google Scholar and PubMed. Of 183 identified studies, 35 were selected based on inclusion and exclusion criteria. Our findings reveal that key predictive features commonly used in machine learning models include age, number of pregnancies, body mass index, diabetes, hypertension, and blood pressure. In contrast, factors such as medications, genetic data, and clinical imaging were considered less frequently. Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Naïve Bayes were the most commonly used algorithms. Most studies were conducted in China and the USA, indicating geographic concentration. The field has seen a notable rise in research, especially in the past two years, though many studies rely on small datasets from single hospitals. This review highlights the need for more diverse and comprehensive research to enhance early detection and management of preeclampsia.

"Hurt people hurt people" is a phrase which summarises the cyclical nature of painful experiences and harmful actions. Arguably, this cycle of hurt and harm applies to the UK's National Health Service (NHS), where employees are reporting record low levels of physical and mental wellbeing, while experiencing a climate of hostility, bullying and harassment, and pressures to meet targets. Such working environments carry several risks, not only for the employees themselves but also in terms of clinical outcomes for patients. As a result, a range of systemic and targeted infrastructure interventions have been trialled in several NHS hospitals (managed in the UK by independent Trusts), seeking to promote a culture of compassion, and improve the psychological safety of workers. However, the effectiveness of such measures in achieving ethical working environments and preventing unethical behaviours, has been questioned. We join the ongoing debate by proposing the need to go beyond ethical infrastructures and instead consider ethical environments in their systemic complexity. We conclude, by putting forward a model of a complex and ethical health system, which incorporates workplace networks, policy frameworks, and accounts for regional demographics.