Health Systems最新文献

The increasing prevalence of the chronic disease is of considerable concern to health-care organisations. Prevention programmes to patients with early chronic disease have the potential to improve individual health and quality of life through disease avoidance or delay and to save the medical cost of the health care system. Due to the limited budget in healthcare this study seeks to analyse the feasibility of a programme prior to implementation. A mathematical model is developed to determine incidence reduction rate at which the underlying cost break-even can be achieved; consequently, the programme would be feasible. We show the existence and uniqueness of the underlying incidence reduction and establish the feasibility frontier concerning the trade-offs between intervention effective period and incidence reduction rate. We use a diabetes prevention programme to demonstrate the efficiency and advantage of the model. The proposed model would inform decision-makers scientific principles in determining an intervention for implementation.

Despite massive progress in vaccine coverage globally, the region of sub-Saharan Africa is lagging behind for Sustainable Development Goal 3 by 2030. Sub-national under-immunisation is part of the problem. In order to reverse the current immunisation system's (IMS) underperformance, a conceptual model is proposed that captures the complexity of IMSs in low- and middle-income countries (LMICs) and offers directions for sustainable redesign. The IMS model was constructed based on literature and stakeholder interaction in Rwanda and Kenya. The model assembles the paradigms of planned and emergency immunisation in one system and emphasises the synchronised flows of vaccinee, vaccinator and vaccine. Six feedback loops capture the main mechanisms governing the system. Sustainability and resilience are assessed based on loop dominance and dependency on exogenous factors. The diagram invites stakeholders to share their mental models and. The framework provides a systems approach for problem structuring and policy design.

Without timely assessments of the number of COVID-19 cases requiring hospitalisation, healthcare providers will struggle to ensure an appropriate number of beds are made available. Too few could cause excess deaths while too many could result in additional waits for elective treatment. As well as supporting capacity considerations, reliably projecting future "waves" is important to inform the nature, timing and magnitude of any localised restrictions to reduce transmission. In making the case for locally owned and locally configurable models, this paper details the approach taken by one major healthcare system in founding a multi-disciplinary "Scenario Review Working Group", comprising commissioners, public health officials and academic epidemiologists. The role of this group, which met weekly during the pandemic, was to define and maintain an evolving library of plausible scenarios to underpin projections obtained through an SEIR-based compartmental model. Outputs have informed decision-making at the system's major incident Bronze, Silver and Gold Commands. This paper presents illustrated examples of use and offers practical considerations for other healthcare systems that may benefit from such a framework.

The paper proposes a hybrid metaheuristic algorithm known as harmony search and simulated annealing (HS-SA) for accurate and precise breast malignancy disclosure by integrating harmony search (HS) and simulated annealing (SA) optimisation methods. An enhanced wavelet-based contourlet transform (WBCT) procedure for mining the highlights of the region of interest (ROI) is explored, that allows execution upgradation over other standard procedures. The anticipated HS-SA algorithm aims to reduce the feature dimensions and assemble at the unparalleled optimal feature subset. The SVM classifier fed with the picke.d feature subsets and assisted by varied kernel functions upheld its classification capacities in contrast with the conformist machine learning classification and optimisation methods. The portrayed computer-aided diagnosis (CAD) model is confronted by evaluating its learning capability on two different breast mammographic datasets i) benchmark BCDR-F03 dataset and ii) local mammographic dataset. Preliminary propagations, experimental outcomes, and quantifiable assessments likewise demonstrate that the proposed model is pragmatic and favourable for the automated breast malignancy findings with optimal performance and fewer overheads. The discoveries show that the proposed CAD system (HS-SA+Kernel SVM) is superior to various characterisation accuracy techniques with an accuracy of 99.89% for the local mammographic dataset and 99.76% for benchmark BCDR-F03 dataset, AUC of 99.41% for the local mammographic dataset and 99.21% for reference BCDR-F03 dataset while keeping the element space restricted to only seven feature subsets and computational prerequisites as low as is judicious.

Falls are one of the most common cause of nonfatal and fatal injuries in the U.S. costing over an estimated $54 billion annually. A significant percentage of patients presenting to hospital emergency departments (ED) for falls are hospitalised. This paper analyzes a regional hospital data pertaining to adults presenting to the ED because of falls. We use patient demographics and medical conditions to help identify patients at risk for immediate undesirable outcomes after a fall. Furthermore, we determine the relative risk of patient hospitalisation and surgery and their characteristics. Our results indicate that older patient's, patients arriving by ambulance, patients with higher severity levels and patients with pre-existing comorbidities were at a higher relative risk of hospitalisation and surgery. Furthermore, patients with medical conditions pertaining to femur and tibia fractures, pelvis, renal failure, ambulatory dysfunction, and cellulitis, among others, and non-Hispanic whites were at a much higher relative risk of hospitalisation and surgery.

Home testing is an emerging innovation that can enable nations and health care systems to safely and efficiently test large numbers of patients to manage COVID-19 and other viral outbreaks.  In this position paper, we explore the process of moving home testing across the translational continuum from labs to households, and ultimately into practice and communities for optimal public health impact. We focus on the four translational science drivers to accelerate the implementation of systems-wide home testing programmes 1) collaboration and team science, 2) technology, 3) multilevel interventions, and 4) knowledge integration. We use the Socio Ecological Model (SEM) as a framework to illustrate our vision for the ideal future state of a comprehensive system of stakeholders utilising tech-enabled home testing for COVID-19 and other virus outbreaks, and we suggest SEM as a tool to address key translational readiness and response questions.

It has been established that high no-show rates of publicly supported health systems in economically depressed areas are largely due to a lack of inexpensive, reliable transportation. The purpose of this paper is to determine the financial feasibility of offering transportation and investigate the net cost savings by reducing no-show rates. The approach starts with a data analysis on 636 patients at the Family Health Center (FHC) in San Antonio, Texas, followed by logistic regression to determine the impact of various transportation factors on cancellations/no-shows and late arrivals. We then investigate the costs savings that could be realised by reducing the no-show rate from 24.3% by up to 60%. Finally, we analyse the expenses that would be incurred should the FHC provide transportation. The full analysis indicates a cost reduction of more than $15,000 per month can be achieved when the no-show rate is reduced by 25% down to 18.2%.