Lean management for improving hospital waiting times-Case study of a Vietnamese public/general hospital emergency department.

Introduction: Emergency departments (EDs) at public hospitals in Vietnam typically face problems with overcrowding, as well as being populated by a wide variety of illnesses, resulting in increasing dissatisfaction from patients. To alleviate these problems, we used the increasingly popular value-stream mapping (VSM) and lean strategy approaches to (1) evaluate the current patient flow in EDs; (2) identify and eliminate the non-valued-added components; and (3) modify the existing process in order to improve waiting times.

Methods: Data from a total of 742 patients who presented at the ED of 108 Military Central Hospital in Hanoi, Vietnam, were collected.

Effects of Pulsed Electromagnetic Fields on Breast Cancer Cell Line MCF 7 Using Absorption Spectroscopy.

We present an analysis of the effects of pulsed electromagnetic fields (PEMF) with 3.3 MHz carrier frequency and modulated by audio resonant frequencies on the MCF-7 breast cancer cell line in vitro using absorption spectroscopy. This involves a fluorescence dye called PrestoBlue™ Cell Viability Reagent and a spectrophotometry to test the viability of MCF-7 breast cancer cells under different PEMF treatment conditions in terms of the cell absorption values.

Neural Analyses Validate and Emphasize the Role of Progesterone Receptor in Breast Cancer Progression and Prognosis.

Oestrogen receptor (ER) expression is routinely measured in breast cancer management, but the clinical merits of measuring progesterone receptor (PR) expression have remained controversial. Hence the major objective of this study was to assess the potential of PR as a predictor of response to endocrine therapy. We report on analyses of the relative importance of ER and PR for predicting prognosis using robust multilayer perceptron artificial neural networks.

Real-time evaluation of breast self-examination using computer vision.

Breast cancer is the most common cancer among women worldwide and breast self-examination (BSE) is considered as the most cost-effective approach for early breast cancer detection. The general objective of this paper is to design and develop a computer vision algorithm to evaluate the BSE performance in real-time. The first stage of the algorithm presents a method for detecting and tracking the nipples in frames while a woman performs BSE; the second stage presents a method for localizing the breast region and blocks of pixels related to palpation of the breast, and the third stage focuses on detecting the palpated blocks in the breast region.

Logic-centered architecture for ubiquitous health monitoring.

One of the key points to maintain and boost research and development in the area of smart wearable systems (SWS) is the development of integrated architectures for intelligent services, as well as wearable systems and devices for health and wellness management. This paper presents such a generic architecture for multiparametric, intelligent and ubiquitous wireless sensing platforms. It is a transparent, smartphone-based sensing framework with customizable wireless interfaces and plug'n'play capability to easily interconnect third party sensor devices.

Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms.

This paper focuses on breast self-examination (BSE) stroke position and palpation level classification for the development of a computer vision-based BSE training and guidance system. In this study, image frames are extracted from a BSE video and processed considering the color information, shape, and texture by wavelet transform and first order color moment. The new approach using artificial neural network and wavelet transform can identify BSE stroke positions and palpation levels, i.

Managing information and knowledge within maternity services: Privacy and consent issues.

Objective: Electronic Patient Records have improved vastly the quality and efficiency of care delivered. However, the formation of single demographic database and the ease of electronic information sharing give rise to many concerns including issues of consent, by whom and how data are accessed and used. This paper examines the organizational and socio-technical issues related to privacy, confidentiality and security when employing electronic records within a maternity service hospital in England.

Predicting breast screening attendance using machine learning techniques.

Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm.

The deployment of information systems and information technology in field hospitals.

Information systems and related technologies continue to develop and have become an integral part of healthcare provision and hospital care in particular. Field hospitals typically operate in the most austere and difficult of conditions and have yet to fully exploit related technologies. This paper addresses those aspects of healthcare informatics, healthcare knowledge management and lean healthcare that can be applied to field hospitals, with a view to improving patient care.

Electronic health records approaches and challenges: a comparison between Malaysia and four East Asian countries.

An integrated Lifetime Health Record (LHR) is fundamental for achieving seamless and continuous access to patient medical information and for the continuum of care. However, the aim has not yet been fully realised. The efforts are actively progressing around the globe.

Application of knowledge management and the intelligence continuum for medical emergencies and disaster scenarios.

The world has recently witnessed several large scale natural disasters. These include the Asian tsunami which devastated many of the countries around the rim of the Indian Ocean in December 2004, extensive flooding in many parts of Europe in August 2005, hurricane katrina (September 2005), the outbreak of severe acute respiratory syndrome (SARS) in many regions of Asia and Canada in 2003 and the Pakistan earthquake (towards the end of 2005). Such emergency and disaster situations (E&DS) serve to underscore the utter chaos that ensues in the aftermath of such events, the many casualties and lives lost not to mention the devastation and destruction that is left behind.

Hereditary non-polyposis colorectal cancer risk assessment based on AI analysis of pedigree data.

Colorectal cancer (CRC) is one of the most common fatal cancers in developed countries and represents a significant public-health issue. About 3-5% of patients with CRC have hereditary non-polyposis colorectal cancer (HNPCC). Cancer morbidity and mortality can be reduced if early and intensive screening is pursued.

Hand motion segmentation against skin colour background in breast awareness applications.

Skin colour modelling and classification play significant roles in face and hand detection, recognition and tracking. A hand is an essential tool used in breast self-examination, which needs to be detected and analysed during the process of breast palpation. However, the background of a woman's moving hand is her breast that has the same or similar colour as the hand.

Transitional appearance-based motion tracking for real-time breast self-examination supervision.

Despite much research and clinical trials, breast cancer still presents a serious threat of premature death to women. Early detection of the lumps in the breast is a key contributing factor to the successful treatment of this potentially fatal disease. Performing breast self-examination (BSE) in an accurate manner can assist a woman in detecting any abnormalities in her breasts, which may mark the onset of potential disease.

Knowledge management and electronic care records: Incorporating social, legal and ethical issues.

Many challenges face developers of secure computer-based clinical systems but the technical problems are overshadowed by many obstacles, key amongst them being social and ethical issues. A sound Knowledge Management (KM) structure within clinical environments can recognise the responsibility of healthcare professionals to keep patient clinical data (for example, electronic care record (ECR) systems) secure. An arrangement is proposed that gives the most senior clinician in a healthcare facility the ultimate responsibility for security of clinical data held in the organisation.

Improving uptake of a breast screening programme: A knowledge management approach for opportunistic intervention.

Reducing mortality from breast cancer through screening has been accepted as a viable tool and breast screening has attracted a lot of attention from healthcare organisations worldwide. Government funded screening programmes in Europe, the Americas and Australia have made good progress in diagnosing and treating breast cancer through effective screening programmes. The UK's National Health Service (NHS) National Screening Programme manages one of the biggest publicly funded breast screening programmes.

Cancer risk analysis in families with hereditary nonpolyposis colorectal cancer.

Colorectal cancer (CRC) is one of the most common fatal cancers in developed countries and represents a significant public-health issue. About 3%-5% of patients with CRC have hereditary nonpolyposis colorectal cancer (HNPCC). Cancer morbidity and mortality can be reduced if early and intensive screening is pursued.

The efficacy of knowledge management for personalised healthcare.

This chapter examines some of the key issues surrounding the incorporation of the Knowledge Management (KM) paradigm for personalised healthcare. We discuss the complex nature of KM, some essential concepts necessary to make personalised healthcare a reality and introduce a schematic which illustrates the efficacy of KM for personalised health.

Neural network-based assessment of prognostic markers and outcome prediction in bilharziasis-associated bladder cancer.

In this paper the potential value of two prognostic factors, namely, bilharziasis status and tumor histological type, is investigated in relation to their abilities to predict disease progression and outcome of patients with bladder cancer, using radial basis function (RBF) neural networks. The bladder cancer data set is described by eight clinical and pathological markers. Two outcomes are of interest: either a patient is alive and free of disease or the patient is dead within five years of diagnosis.

A fuzzy logic based-method for prognostic decision making in breast and prostate cancers.

Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist.

Assessment of nodal involvement and survival analysis in breast cancer patients using image cytometric data: statistical, neural network and fuzzy approaches.

Accurate and reliable decision making in breast cancer prognosis can help in the planning of suitable surgery and therapy and, generally, optimise patient management through the different stages of the disease. In recent years, several prognostic factors have been used as indicators of disease progression in breast cancer. In this paper we investigate a fuzzy method, namely fuzzy k-nearest neighbour technique for breast cancer prognosis, and for determining the significance of prognostic markers and subsets of the markers, which include histology type, tumour grade, DNA ploidy, S-phase fraction, G0G1/G2M ratio, and minimum (start) and maximum (end) nuclear pleomorphism indices.

Fractal analysis in the detection of colonic cancer images.

The aim of this study was to investigate the value of fractal dimension in separating normal and cancerous images, and to examine the relationship between fractal dimension and traditional texture analysis features. Forty-four normal images and 58 cancer images from sections of the colon were analyzed. A "leave-one-out" analysis approach was used to classify the samples into each group.

Buried Plant Detection: A Volterra Series Modelling Approach Using Artificial Neural Networks.

This paper discusses the embedding of artificial neural networks (ANNs) into the framework of the Volterra series for modelling the problem of detecting buried pipes. This problem is formulated as a classification task whereby it is necessary to discriminate between the ground surface and an actual pipe reflection buried in noise in the return signal from ground probing radar. The objective is to filter out the unwanted surface reflection to enable improved mapping of the site being surveyed.