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A Universal Right to Healthcare of Variable Geometry 1
Published in Rui Nunes, Healthcare as a Universal Human Right, 2022
In this perspective, healthcare delivery has two main components clearly separated by a specific moment: point EO* in the (EO)F. On the upper side of the Y-axis, the financial burden of the system is publicly supported by the taxpayer. On the lower side, a mix between the needs criteria (i.e., the lower-level needs) and the financial constraints of the healthcare system implicate a personal, not societal, level of commitment to one’s own health. Therefore, the public provision of healthcare is no longer mandatory. Most healthcare systems in developed countries are segmented, with at least two tiers. Further, alternative coverage schemes do exist, including private health insurance or out-of-pocket payments. This point EO* can vary depending on different variables, namely citizen’s democratic choices. However, it can be situated schematically in the graphic area corresponding to what Maslow defined as esteem needs.
The discipline of strategic thinking in healthcare
Published in Robert Jones, Fiona Jenkins, Managing and Leading in the Allied Health Professions, 2021
Market segmentation defines a distinct group of consumers who require special products or services. Segments can be based on needs and are evaluated both in terms of financial attractiveness and in a group of consumers identified by one or more characteristics that allows the organisation to design a product or service to meet their needs. Having a ‘targeted’ market is important because a service should not pretend to serve every need for every type of patient.61 Patient segments can have both demographic and psychographic — actions prompted by thoughts and feelings of fear, pleasure, boredom, vanity and so on — dimensions in common. In healthcare there are disease segments: diabetes, cancer, asthma, sports medicine and care of the elderly, for example.
Smart Software for Real-Time Image Analysis
Published in Abdel-Badeeh M. Salem, Innovative Smart Healthcare and Bio-Medical Systems, 2020
Marius Popescu, Antoanela Naaji
Although segmented images are used in many medical applications (diagnosis, pathological lesion localization, structure analysis, etc.), image segmentation is difficult due to variations in the shape of the objects and the quality of the acquired images. Most medical images are taken with sampling artifacts, noise that leads to errors when strict image-processing techniques are used. Several segmentation methods have been proposed for medical images. Noise and other image artifacts can cause the occurrence of incorrect regions and contours, or discontinuity of the objects obtained through these methods.
The pulsatile 3D-Hemodynamics in a doubly afflicted human descending abdominal artery with iliac branching
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Sumit Kumar, S. K. Rai, B.V. Rathish Kumar, Om Shankar
A new methodology for accurate 3D geometrical modelling of patient-specific geometry using CT-scan in DICOM image format, meshing, and CFD simulation is designed and illustrated in Figure 1. The details of each methodology are described in the following subsections. Figure 1 depicts a step-by-step procedure for numerical simulation of complex geometry with patient-specific data. The first step includes pre-processing, i.e., three-dimensional geometry modelling using CT-scan DICOM data of a patient. It includes (i) contrasting, i.e., adjustment of image intensity for clear visualization of soft tissue in the dataset, (ii) segmentation, i.e., partitioning an image into various segments to locate objects (soft and hard tissue), (iii) boundaries in the image, (iv) morphological operation, i.e., dilation (adding a pixel to boundaries of objects in the image), (v) erosion, i.e., (removing small objects and retaining substantive objects) and (vi) some smoothing operation across bifurcations. The next step after three-dimensional modelling is (a) meshing which is followed by (b) selection of suitable material, (c) boundary conditions setup, and (d) solver physics setup for simulation. The last step is post-processing which includes (i) result extraction, (ii)analysis of simulation at various time steps of the cardiac cycle, and (iii)validation.
Development of a navigable 3D virtual model of temporal bone anatomy
Published in Journal of Visual Communication in Medicine, 2023
The plan was to develop an accurate 3D virtual model of the temporal bone with its complete surface anatomy, which would be easy to use by the students without any major cost. For this, a helical computed tomographic (CT) scan was used to acquire high-resolution images of cadaveric temporal bone in the standard DICOM (Digital imaging and communications in medicine) file format. These images served as building blocks for the construction of a three-dimensional model using the volume-rendering capabilities of 3D Slicer®. The process involved volume data management, cropping of the data set, and threshold painting to segment anatomical structures based on the intensity captured from different regions. Finally, the volumetric data was exported into .stl (an abbreviation for ‘stereolithography’) file format, which is one of the most common and widely supported methods of storing and viewing three-dimensional data in a compact form. To make it more accessible and interactive, the 3D virtual model was embedded into an HTML web page using Blender®.
Efficacy of quantifying marker-cluster rigidity in a multi-segment foot model: a Monte-Carlo based global sensitivity analysis and regression model
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Po-Hsiang Chan, Julie Stebbins, Amy B. Zavatsky
Stereophotogrammetry-based clinical gait analysis has been implemented in biomechanical laboratories and hospitals all over the world to analyse the condition of various pathologies, including foot deformities such as those seen in cerebral palsy and clubfoot (Theologis and Stebbins, 2010), arthritis (Turner and Woodburn, 2008), diabetic gait (Sawacha et al., 2009), and many more (Leardini et al., 2019). In a clinical gait laboratory, multi-segment foot models (MSFMs) have been used to capture quantitative information about human motion to help guide diagnosis, treatment planning, and the assessment of surgical outcome (Rankine et al., 2008; Deschamps et al., 2011; Leardini et al., 2019). The methodology generally involves dividing the lower-limb into multiple rigid body segments, typically the pelvis, thigh, shank, and foot, and tracking them using markers (or markers on rigid bases) attached to the skin. Kinematics of the joints can then be computed by describing the orientation of a distal segment with reference to a proximal segment. Markers are also used to track bony landmarks to facilitate the computation of biomechanical parameters or clinically relevant information, such as the foot arch height or the first metatarsophalangeal angle. Both the segment definition (segmentation and marker placement) and the kinematic description of each model differ largely depending on the clinical need, technology limitation, and the designer’s criteria.