Health Systems最新文献

Social Determinant of Health (SDOH) data are important targets for research and innovation in Health Information Systems (HIS). The ways we envision SDOH in "smart" information systems will play a considerable role in shaping future population health landscapes. Current methods for data collection can capture wide ranges of SDOH factors, in standardised and non-standardised formats, from both primary and secondary sources. Advances in automating data linkage and text classification show particular promise for enhancing SDOH in HIS. One challenge is that social communication processes embedded in data collection are directly related to the inequalities that HIS attempt to measure and redress. To advance equity, it is imperative thatcare-providers, researchers, technicians, and administrators attend to power dynamics in HIS standards and practices. We recommend: 1. Investing in interdisciplinary and intersectoral knowledge generation and translation. 2. Developing novel methods for data discovery, linkage and analysis through participatory research. 3. Channelling information into upstream evidence-informed policy.

Background: In this paper we focus on medical device development (MDD) in Industrial Design Engineering (IDE) academia. We want to find which methods our MDD-students currently use, where our guidance has shortcomings and where it brings added value.

Methods: We have analysed 19 master and 3 doctoral MDD-theses in our IDE curriculum. The evaluation focusses around four main themes: 1) regulatory 2) testing 3) patient-centricity and 4) systemic design.

Results: Regulatory aspects and medical testing procedures seem to be disregarded frequently. We assume this is because of a lack of MDD experience and the small thesis timeframe. Furthermore, many students applied medical-oriented systemic tools, which enhances multiperspectivism. However, we found an important lack in the translation to the List of Specifications and to business models of these medical devices. Finally, students introduced various participatory techniques, but seem to struggle with implementing this in the setting of evidence-based medicine.

The global crisis generated by COVID-19 has heightened awareness of pandemic outbreaks. From a public health preparedness standpoint, it is essential to assess the impact of a pandemic and also the resilience of the affected communities, which is the ability to withstand and recover quickly after a pandemic outbreak. The infection attack rate has been the common metric to assess community response to a pandemic outbreak, while it focuses on the number of infected it does not capture other dimensions such as the recovery time. The aim of this research is to develop community resilience measures and demonstrate their estimation using a simulated pandemic outbreak in a region in the USA. Three scenarios are analysed with different combinations of virus transmissibility rates and non-pharmaceutical interventions. I The inclusion of the resilience framework in the pandemics outbreak analysis will enable decision makers to capture the multi dimensional nature of community response.

The biopsychosocial model is among the most influential frameworks for human-centered health improvement but has faced significant criticism- both conceptual and pragmatic. This paper extends and fundamentally re-structures the biopsychosocial model by combining it with sociotechnical systems theory. The resulting biopsychosociotechnical model addresses key critiques of the biopsychosocial model, providing a more "practical theory" for human-centered health improvement. It depicts the determinants of health as complex adaptive system of systems; includes the the artificial world (technology); and provides a roadmap for systems improvement by: differentiating between "health status" and "health and needs assessment", [promoting problem framing]; explaining health as an emergent property of the biopsychosociotechnical context [imposing a systems orientation]; focusing on "interventions" vs. "treatments" to modify the biopsychosociotechnical determinants of health, [expanding the solution space]; calling for a participatory design process [supporting systems awareness and goal-orientation]; and including intervention management to support the full lifecycle of health improvement.

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.