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. A VSM was developed where improvement possibilities were identified and attempts to eliminate non-value-added activities were made. A range of issues that were considered as a resource waste were highlighted, which led to a re-design process focusing on prioritizing blood tests and ultrasound procedures. On the administrative side, various measures were considered, including streamlining communication with medical departments, using QR codes for healthcare insurance payments, and efficient management of X-ray and CT scan online results. RESULTS: By implementing a lean approach, the following reductions in delay and waiting time were incurred: (1) pre-operative test results (for patients requiring medical procedures/operations) by 33.3% (from 134.4 to 89.4 min); (2) vascular interventions by 10.4% (from 54.6 to 48.9 min); and (3) admission to other hospital departments by 49.5% (from 118.3 to 59.8 min). Additionally, prior to the implementation of the lean strategy approach, only 22.9% of patients or their proxies (family members or friends), who responded to the survey, expressed satisfaction with the ED services. This percentage increased to 76.5% following the curtailment of non-value-added activities. Through statistical inferential test analyses, it can be confidently concluded that applying lean strategy and tools can improve patient flow in public/general hospital EDs and achieve better staff coordination within the various clinical and administrative hospital departments. To the authors' knowledge, such analysis in a Vietnamese hospital's ED context has not been previously undertaken.

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. The DNA molecule of the MCF-7 breast cancer cells has an electric dipole property that renders it sensitive and reactive to applied electromagnetic fields. Resonant frequencies derived from four genes mutated in MCF-7 breast cancer cells [rapamycin-insensitive companion of mammalian target of rapamycin (RICTOR), peroxisome proliferator-activated receptor (PPARG), Nijmegen breakage syndrome 1 (NBN) and checkpoint kinase 2 (CHEK2)] were applied in generating square pulsed electromagnetic waves. Effects were monitored through measurement of absorption of the samples with PrestoBlue™, and the significance of the treatment was determined using the t-test. There was a significant effect on MCF-7 cells after treatment with PEMF at the resonant frequencies of the following genes for specific durations of exposure: RICTOR for 10 min, PPARG for 10 min, NBN for 15 min, and CHEK2 for 5 min.

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. Receptor determinations use immunohistochemical (IHC) methods or radioactive ligand binding assays (LBA). In view of the heterogeneity of intratumoral receptor distribution, we examined the relative merits of the IHC and LBA methods. Our analyses reveal a more significant correlation of IHC-determined PR than ER with both nodal status and 5-year disease-free survival (DFS). In LBA, PR displayed higher correlation with survival and ER with nodal status. There was concordance of correlation of PR with DFS by both IHC and LBA. This study suggests a clear distinction between PR and ER, with PR displaying greater correlation than ER with disease progression and prognosis, and emphasizes the marked superiority of the IHC method over LBA. These findings may be valuable in the management of patients with breast cancer.

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. The palpated blocks are highlighted at the time of BSE performance. In a correct BSE performance, all blocks must be palpated, checked, and highlighted, respectively. If any abnormality, such as masses, is detected, then this must be reported to a doctor to confirm the presence of this abnormality and proceed to perform other confirmatory tests. The experimental results have shown that the BSE evaluation algorithm presented in this paper provides robust performance.

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. It caters to wireless body, personal, and near-me area networks. A pivotal part of the platform is the integrated inference engine/runtime environment that allows the mobile device to serve as a user-adaptable personal health assistant. The novelty of this system lays in a rapid visual development and remote deployment model. The complementary visual Inference Engine Editor that comes with the package enables artificial intelligence specialists, alongside with medical experts, to build data processing models by assembling different components and instantly deploying them (remotely) on patient mobile devices. In this paper, the new logic-centered software architecture for ubiquitous health monitoring applications is described, followed by a discussion as to how it helps to shift focus from software and hardware development, to medical and health process-centered design of new SWS applications.

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. METHODS: A preliminary questionnaire was administered (n = 52), in total, 24 responses were received. Sixteen responses were from personnel in the information technology department, 5 from health information department and 3 from midwifery managers. This was followed by a semi-structured interview with representatives from the clinical and technological side. RESULTS: A number of issues related to information governance (IG) have been identified, especially breaches on sharing personal information without consent from the patients have been identified as one immediate challenge that needs to be fixed. CONCLUSION: There is an immediate need for more robust, realistic, built-in accountability both locally and nationally on data sharing. A culture of ownership and strict adherence to IG principles is paramount. Focused training in the area of data, information and knowledge sharing will bring in a balance of legitimate usage against the individual's rights to confidentiality and privacy.

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.e. light, medium, and deep, at 97.8 % and 87.5 % accuracy respectively.

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. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction. The algorithm has been developed in an open source-based environment. The algorithm was tested on a 13-year dataset (1995-2008). This paper compares the algorithm and validates its accuracy and efficiency with different platforms. Nearly 80% accuracy and 88% positive predictive value and sensitivity were recorded for the algorithm. The results were encouraging; 40-50% of negative predictive value and specificity warrant further work. Preliminary results were promising and provided ample amount of reasons for testing the algorithm on a larger scale.

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. The aim is to provide a vision for the deployment of information systems and information technology in field hospitals, using the British Army's field hospital as a representative model.

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. Every stage of the development of the LHR initiatives had presented peculiar challenges. The best lessons in life are those of someone else's experiences. This paper presents an overview of the development approaches undertaken by four East Asian countries in implementing a national Electronic Health Record (EHR) in the public health system. The major challenges elicited from the review including integration efforts, process reengineering, funding, people, and law and regulation will be presented, compared, discussed and used as lessons learned for the further development of the Malaysian integrated LHR.

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. In the UK, only 75% of the intended population is screened and a diverse set of efforts has attempted to identify and initiate countermeasures to improve screening attendance. This paper identifies how innovative use of information and communication technologies (ICTs) can be the focus for strategising not only improved screening attendance but also better quality of care for women.

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. However, despite advances in screening, population-wide genetic screening for HNPCC is not currently considered feasible due to its complexity and expense. If the risk of a family having HNPCC can be identified/assessed, then only the high-risk fraction of the population would undergo intensive screening. This identification is currently performed by a genetic counselor/physician who makes the decision based on some pre-defined criteria. Here, we report on a system to identify the risk of a family having HNPCC based on its history. We compare artificial neural networks and statistical approaches for assessing the risk of a family having HNPCC and discuss the experimental results obtained by these two approaches.

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. One recurring theme that is apparent in all these situations is that, irrespective of the warnings of imminent threats, countries have not been prepared and ready to exhibit effective and efficient crisis management. This paper examines the application of the tools, techniques and processes of the knowledge economy to develop a prescriptive model that will support superior decision making in E&DS, thereby enabling effective and efficient crisis management.

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.

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. This is also an essential tool used to enhance breast awareness. Using the hand, in a specific configuration, and palpating the entire breast in a certain movement pattern can optimise her feeling of the breast, In this paper we describe an intelligent automated algorithm for tracking the finger pads of a moving hand with the movement videos captured by a common web camera. The algorithm employs the principle of HCRA (hand configuration recognition algorithm) and its refinement through a simple but novel transitional appearance-based model. A novel hand motion recognition algorithm (HMRA) is developed to recognise the motion pattern. Desirable tracking and recognition results have been achieved and the robustness of this algorithm is demonstrated in this paper.

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. Additionally, colour images recorded by a web camera are strongly affected by the lighting or brightness conditions. Hence, it is a challenging task to segment and track the hand against the breast without utilising any artificial markers, such as coloured nail polish. In this paper, a two-dimensional Gaussian skin colour model is employed in a particular way to identify a breast but not a hand. First, an input image is transformed to YCbCr colour space, which is less sensitive to the lighting conditions and more tolerant of skin tone. The breast, thus detected by the Gaussian skin model, is used as the baseline or framework for the hand motion. Secondly, motion cues are used to segment the hand motion against the detected baseline. Desired segmentation results have been achieved and the robustness of this algorithm is demonstrated in this paper.

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. Three hundred and twenty-one (321) patients are involved in this retrospective study, 83.5% of whom had been confirmed with bilharziasis history. Selected marker subsets are examined to improve the outcome predictive accuracy and to evaluate the effects of the assessed prognostic factors on such outcome. The highest predictive accuracy for patients with bladder adenocarcinoma, as obtained from the RBF network, is found to be 85% with one subset of markers. The predictive analysis shows that bilharziasis history and patients' histology type are both important prognostic factors in prediction and, for each histology type, different marker combinations with significant characteristics have been observed.

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. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than the statistical and artificial neural-network-based methods.

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. We also compare the method with (a) logistic regression as a statistical method, and (b) multilayer feed forward backpropagation neural networks as an artificial neural network tool, the latter two techniques having been widely used for cancer prognosis. Nodal involvement and survival analyses in breast cancer are carried out for 100 women who were clinically diagnosed with breast disease in the form of carcinoma and benign conditions, and seven prognostic markers collected for each patient. For nodal involvement analysis, node positive and negative patients are predicted whereas survival analysis is carried out for two categories: whether a patient is alive or dead within 5 years of diagnosis. The results obtained show that the fuzzy method yields the highest predictive accuracy of 88% for both nodal involvement and survival analyses obtained from the subsets of [tumour grade, S-phase fraction, minimum (start) nuclear pleomorphism index] and [tumour histology type, DNA ploidy, S-phase fraction, G0G1/G2M ratio], respectively. We believe that this technique has produced more reliable prognostic factor models than those obtained using either the statistical or artificial neural networks-based methods.

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. With fractal analysis there was a highly significant difference between groups (p < 0.0001). Correlation and entropy features showed greater differences between the groups (p < 0.0001). Nevertheless, the addition of fractal analysis to the feature analysis improved the sensitivity from 90% to 95% and specificity from 86% to 93%.