Nursing discharge planning for older medical inpatients in Switzerland: A cross-sectional study.

Nursing discharge planning for elderly medical inpatients is an essential element of care to ensure optimal transition to home and to reduce post-discharge adverse events. The objectives of this cross-sectional study were to investigate the association between nursing discharge planning components in older medical inpatients, patients' readiness for hospital discharge and unplanned health care utilization during the following 30 days. Results indicated that no patients benefited from comprehensive discharge planning but most benefited from less than half of the discharge planning components. The most frequent intervention recorded was coordination, and the least common was patients' participation in decisions regarding discharge. Patients who received more nursing discharge components felt significantly less ready to go home and had significantly more readmissions during the 30-day follow-up period. This study highlights large gaps in the nursing discharge planning process in older medical inpatients and identifies specific areas where improvements are most needed.

The knowledge about diagnostic imaging methods among primary care and medical emergency physicians

Objective To evaluate the knowledge about diagnostic imaging methods among primary care and medical emergency physicians. Materials and Methods Study developed with 119 primary care and medical emergency physicians in Montes Claros, MG, Brazil, by means of a structured questionnaire about general knowledge and indications of imaging methods in common clinical settings. A rate of correct responses corresponding to ≥ 80% was considered as satisfactory. The Poisson regression (PR) model was utilized in the data analysis. Results Among the 81 individuals who responded the questionnaire, 65% (n = 53) demonstrated to have satisfactory general knowledge and 44% (n = 36) gave correct responses regarding indications of imaging methods. Respectively, 65% (n = 53) and 51% (n = 41) of the respondents consider that radiography and computed tomography do not use ionizing radiation. The prevalence of a satisfactory general knowledge about imaging methods was associated with medical residency in the respondents' work field (PR = 4.55; IC 95%: 1.18-16.67; p-value: 0.03), while the prevalence of correct responses regarding indication of imaging methods was associated with the professional practice in primary health care (PR = 1.79; IC 95%: 1.16-2.70; p-value: 0.01). Conclusion Major deficiencies were observed as regards the knowledge about imaging methods among physicians, with better results obtained by those involved in primary health care and by residents.

Treatment of cancer with oral drugs: a position statement by the Spanish Society of Medical Oncology

Cancer treatment involves the participation of multiple medical specialties and, as our knowledge of the disease increases, this fact becomes even more apparent. The degree of multidisciplinarity is determined by several factors, which include the severity and type of disease, the increasing diversity in the available pharmacological and non-pharmacological therapies, and the range of specialists involved in cancer therapy, such as medical oncologists, radiotherapists, gynecologists, gastroenterologists, urologists, surgeons, and pneumologists, among others. Across Europe, the situation of cancer care can be variable due to the diversity of health systems, differences in drug reimbursement, and the degree of establishment of Medical Oncology as a medical specialty in the European Union states.

Medical nutrition in oncology : potential solution for managing malnutrition in cancer patients in Switzerland

The objective of this study is to: - Describe the cancer related complications, prevalence and economic burden of cancer; - Provide the review of the studies that have been done until now proving that specialized nutrition; can improve quality of life (QoL), shorten the length of hospital stay and reduce overall cost of patients care; - Describe different types of specialized nutritional support and tools/ guidelines used for nutritional screening; - Justify the use of specialized nutrition as an integral part of cancer treatment [Author, p. 6] [Contents] 3. General overview of cancer. 4. Specialized nutritional support and nutritional screening. 4.4 European guidelines for nutritional screening [Screening tools: Malnutrition Universal Screening Tool (MUST); Nutritional Risk Screening (NRS-2002); Mini Nutritional Assessment (MNA)]. 5. Implementation of nutritional support in Swiss hospitals as an integral part of oncology treatment. 5.1 Nutritional guidelines used in Switzerland. 5.2 Status of prevention of malnutrition in cancer patients in Swiss hospitals. 5.3 Malnutrition in Swiss hospitals: medical costs and potential economies. 5.4 Recommendations for implementation of nutritional guidelines and nutritional support in Swiss hospitals.

Au sortir de l'hôpital, comment renforcer la continuité de la prise en charge médicale [Discharge from hospital: how to improve continuity of medical care?].

Early readmission is the major success indicator of the transition between hospital and home. Patients admitted with heart failure reach a 20% rate. Potentially avoidable readmissions, defined as unpredictable and related to a known condition during index hospitalization, represent the improvement margin. For these latter, implementation of specific interventions can be effective. Complex interventions on transition, including several modalities and seeking to encourage patient autonomy seem more effective than others. We describe two models: a pragmatic one developed in a regional hospital, and a more complex one developed in a university hospital during the LEAR-HF study. In both cases, it is imperative to work on "medical liability": should it extend beyond discharge up to the threshold of the private practice?

Microcalcification evaluation in computer assisted diagnosis for digital mammography

In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is oftenassociated with a high incidence of breast cancers. Accurate classification into benign and malignant groups would help improve diagnostic sensitivity as well as reduce the number of unnecessa y biopsies. The challenge here is the selection of the useful features to distinguish benign from malignant micro calcifications. Our purpose in this work is to analyse a microcalcification evaluation method based on a set of shapebased features extracted from the digitised mammography. The segmentation of the microcalcificationsis performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes of clustered microcalcificationshave been associated with a high risk of carcinoma based on digerent subjective measures, such as whether or not the calcifications are irregular, linear, vermiform, branched, rounded or ring like, our efforts were addressed to obtain a feature set related to the shape. The identification of the pammeters concerning the malignant character of the microcalcifications was performed on a set of 146 mammograms with their real diagnosis known in advance from biopsies. This allowed identifying the following shape-based parameters as the relevant ones: Number of clusters, Number of holes, Area, Feret elongation, Roughness, and Elongation. Further experiments on a set of 70 new mammogmms showed that the performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in the senseof adding new features not only related to the shape

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

A Multiagent System for Coordinating Ambulances for Emergency Medical Services

To coordinate ambulances for emergency medical services, a multiagent system uses an auction mechanism based on trust. Results of tests using real data show that this system can efficiently assign ambulances to patients, thereby reducing transportation time. Emergency transportation on specialized vehicles is needed when a person's health is in risk of irreparable damage. A patient can't benefit from sophisticated medical treatments and technologies if she or he isn't placed in a proper healthcare center with the appropriate medical team. For example, strokes are neurological emergencies involving a limited amount of time in which treatment measures are effective

Swarm Intelligent Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices

Current technology trends in medical device industry calls for fabrication of massive arrays of microfeatures such as microchannels on to nonsilicon material substrates with high accuracy, superior precision, and high throughput. Microchannels are typical features used in medical devices for medication dosing into the human body, analyzing DNA arrays or cell cultures. In this study, the capabilities of machining systems for micro-end milling have been evaluated by conducting experiments, regression modeling, and response surface methodology. In machining experiments by using micromilling, arrays of microchannels are fabricated on aluminium and titanium plates, and the feature size and accuracy (width and depth) and surface roughness are measured. Multicriteria decision making for material and process parameters selection for desired accuracy is investigated by using particle swarm optimization (PSO) method, which is an evolutionary computation method inspired by genetic algorithms (GA). Appropriate regression models are utilized within the PSO and optimum selection of micromilling parameters; microchannel feature accuracy and surface roughness are performed. An analysis for optimal micromachining parameters in decision variable space is also conducted. This study demonstrates the advantages of evolutionary computing algorithms in micromilling decision making and process optimization investigations and can be expanded to other applications

Image Segmentation Using Information Bottleneck Method

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.