An analysis of the healthcare informatics and systems in Southeast Asia: a current perspective from seven countries.

This paper presents an overview of the healthcare systems in Southeast Asia, with a focus on the healthcare informatics development and deployment in seven countries, namely, Singapore, Cambodia, Malaysia, Thailand, Laos, the Philippines and Vietnam. Brief geographic and demographic information is provided for each country, followed by a historical review of the national strategies for healthcare informatics development. An analysis of the state-of-the-art healthcare infrastructure is also given, along with a critical appraisal of national healthcare provisions.

Critical success factors for achieving superior m-health success.

Recent healthcare trends clearly show significant investment by healthcare institutions into various types of wired and wireless technologies to facilitate and support superior healthcare delivery. This trend has been spurred by the shift in the concept and growing importance of the role of health information and the influence of fields such as bio-informatics, biomedical and genetic engineering. The demand is currently for integrated healthcare information systems; however for such initiatives to be successful it is necessary to adopt a macro model and appropriate methodology with respect to wireless initiatives. The key contribution of this paper is the presentation of one such integrative model for mobile health (m-health) known as the Wi-INET Business Model, along with a detailed Adaptive Mapping to Realisation (AMR) methodology. The AMR methodology details how the Wi-INET Business Model can be implemented. Further validation on the concepts detailed in the Wi-INET Business Model and the AMR methodology is offered via a short vignette on a toolkit based on a leading UK-based healthcare information technology solution.

How workflow management systems enable the achievement of value driven healthcare delivery.

Healthcare institutions globally are currently having major problems accessing and maintaining the large amounts of data that are continuously being generated. Examination of the clinical procedures relating to patient management reveals that many of these activities are repetitive. Workflow Management Systems (WFMS) can automate these repeated activities. Moreover, the introduction of WFMS would enable healthcare institutions to face this challenge of transforming large amounts of medical data into contextually relevant clinical information and knowledge. In order to emphasise the dynamic connection between healthcare, workflow and internet technologies, the intelligence continuum is introduced.

Implications for healthcare knowledge management systems: a case study.

The notion of incorporating knowledge management (KM) in the healthcare sector has recently witnessed a lot of interest, both from healthcare practitioners and scholars. Because KM for healthcare has just started to appear on the radar of healthcare stakeholders, there exists very limited research (particularly empirical data) to guide healthcare stakeholders, both from an academic and organizational perspective. This paper attempts to contribute to the adoption of KM in the clinical and healthcare sectors by collecting and analyzing data on technological, organizational and managerial perspectives on KM in these sectors. This paper provides an analysis of a case study which looks at current practices towards healthcare information management.

Towards a web-based knowledge management system for cardiovascular disease.

The objective of this paper is to examine the efficacy of the Knowledge Management (KM) paradigm for a web-based patient administration system (PAS) for cardiovascular disease (CVD). We discuss the role of contemporary information and communication technologies (ICTs) for the management of electrocardiographic information and how this can act as a foundation for a KM-based system.

A dynamic clinical dental relational database.

The traditional approach to relational database design is based on the logical organization of data into a number of related normalized tables. One assumption is that the nature and structure of the data is known at the design stage. In the case of designing a relational database to store historical dental epidemiological data from individual clinical surveys, the structure of the data is not known until the data is presented for inclusion into the database. This paper addresses the issues concerned with the theoretical design of a clinical dynamic database capable of adapting the internal table structure to accommodate clinical survey data, and presents a prototype database application capable of processing, displaying, and querying the dental data.

Towards total knowledge management for healthcare: clinical and organizational considerations.

Knowledge Management (KM) has made a significant impact on the global healthcare sector. However, it is important to address the link between knowledge, information and engineering. Knowledge Engineering (KE) is often only a small part of a KM-based project, yet some KM practitioners favour wholly KE-biased Knowledge Management projects, disregarding a more necessary holistic stance. This paper analyses some current achievements in the KM field and provides a benchmark from which academics and practitioners can attempt to attain "Total Knowledge Management for Healthcare" (TKMh).

The knowledge management landscape: implications for clinical knowledge management and practice.

The objective of this paper is to determine the future for Knowledge Management (KM) applications that focus on healthcare processes. This is achieved by tracing the evolution of KM by examining how different sectors have formulated industry-specific KM applications, then discussing the key constraints that these sectors have faced whilst formulating industry specific KM applications. It then details how these constraints can impede the coming of age of KM applications for healthcare. The results of several case studies on the future of healthcare KM applications are presented. This paper thus attempts to contribute to the adoption of KM in healthcare by looking at how practitioners can overcome stumbling blocks in KM healthcare applications.

Predicting clinical outcomes for newborns using two artificial intelligence approaches.

Two different approaches, based on artificial neural networks (ANN) and fuzzy logic, were used to predict a number of outcomes of newborns: How they would be delivered, their 5 minute Apgar score, and neonatal mortality. The goal was to assess whether the methods would be comparable or whether they would perform differently for different outcomes. The results were comparable for Correct Classification Rate (CCR) and Specificity (true negative cases). Sensitivity (true positive cases) was slightly higher for the back-propagation feed-forward ANN than using the Fuzzy-Logic Classifier (FLC). Since this is one single database and a very large one, it is possible that the FLC would perform better than the ANN for very small databases, as shown by some of the co-authors in the past. The next step will be to test a small database with both methods to assess strengths and weaknesses with the intent to use both if needed with some medical data in the future.

IRiS: an interactive reality system for breast self-examination training.

This paper presents a novel interactive reality video playback approach developed for biomedical training purposes, and tested on a prototype breast self-examination (BSE) multimedia training application. The system is developed in order to improve on existing video playback approaches as used in multimedia applications by providing control over not only time, as in conventional video playback, but also space. The benefits of interactive reality video playback are presented and the approach is compared with other similar approaches, such as QuickTime and iPIX. The design, development, final implementation, testing and evaluation plan of the IRiS system are presented. The paper also discusses future plans and the use of the system in other biomedical training scenarios.

Neural-network compensation methods for capacitive micromachined accelerometers for use in telecare medicine.

Transducers represent a key component of medical instrumentation systems. In this paper, sensors that perform the task of measuring the physical quantity of acceleration are discussed. These sensors are of special significance since, by integrating their output signal, accelerometers can additionally provide a measure of velocity and position. Applications for such measurements and, thus, of accelerometers, range from early diagnosis procedures for tremor-related diseases (e.g., Parkinson's) to monitoring daily patterns of patient activity using telemetry systems. The system-level requirements in such applications are considered and two novel neural-network transducer designs developed by the authors are presented, which aim to satisfy such requirements. Both designs are based on a micromachined sensing element with capacitive signal pickoff. The first is an open-loop design utilizing a direct-inverse-control strategy, while the second is a closed-loop design, where electrostatic actuation is used as a form of feedback. Both transducers are nonlinearly compensated, capable of self-test, and provide digital outputs.

Neural network analysis of clinicopathological and molecular markers in bladder cancer.

PURPOSE: To evaluate retrospectively the ability of an artificial neural network (ANN) to predict bladder cancer recurrence within 6 months of diagnosis and stage progression in patients with Ta/T1 bladder cancer, and 12-month cancer-specific survival in patients with T2-T4 bladder cancer. MATERIALS AND METHODS: Data were analyzed using a NeuralWorks Professional II/Plus software package. The input neural data consisted of clinicopathological and molecular characteristics. Distinct patient groups were used for the prediction of stage progression and tumor recurrence in Ta/T1 bladder cancers, and 12-month cancer-specific survival for patients with T2-T4 tumors. ANN predictions were compared with those of four consultant urologists. RESULTS: The accuracy of the neural network in predicting stage progression and recurrence within 6 months for Ta/T1 tumors and 12-month cancer-specific survival for T2-T4 cancers was 80%, 75% and 82% respectively; with corresponding figures for clinicians being 74%, 79% and 65%. On restricting the validation subset to patients with T1G3 tumors in relation to stage progression, the sensitivity of the ANN analysis increased to 100% with a specificity of 78% and an overall accuracy of 82%. The performance of the ANN in predicting stage progression in T1G3 tumors was significantly higher than that of clinicians (p = 0.25 for the ANN and p = 0.008 for clinicians, McNemar test). CONCLUSIONS: Data analysis using an ANN has been shown to be a useful adjunct in predicting outcomes in patients with bladder cancer and out-performs clinicians' predictions of stage progression in the high risk group of patients with T1G3 disease.

DNA ploidy and cell cycle distribution of breast cancer aspirate cells measured by image cytometry and analyzed by artificial neural networks for their prognostic significance.

Chromosomal abnormalities are commonly associated with cancer, and their importance in the pathogenesis of the disease has been well recognized. Also recognized in recent years is the possibility that, together with chromosomal abnormalities, DNA ploidy of breast cancer aspirate cells, measured by image cytometric techniques, may correlate with prognosis of the disease. Here, we have examined the use of an artificial neural network to predict: 1) subclinical metastatic disease in the regional lymph nodes and 2) histological assessment, through the analysis of data obtained by image cytometric techniques of fine needle aspirates of breast tumors. The cellular features considered were: 1) DNA ploidy measured in terms of nuclear DNA content as well as by cell cycle distribution; 2) size of the S-phase fraction; and 3) nuclear pleomorphism. A further objective of the study was to analyze individual markers in terms of impact significance on predicting outcome in both cases. DNA ploidy, indicated by cell cycle distribution, was found markedly to influence the prediction of nodal spread of breast cancer, and nuclear pleomorphism to a lesser degree. Furthermore, a comparison between histological assessment and artificial neural network prediction shows a closer correlation between the neural approach and the development of further metastases as indicated in subsequent follow-up, than does histological assessment. These data demonstrate that artificial neural networks are capable of providing powerful and reliable indicators of possible lymph node metastasis, using measurements of cellular features alone.

Automated feature extraction and identification of colon carcinoma.

OBJECTIVE: To assess an automated algorithm, developed for the classification of normal and cancerous colonic mucosa, using geometric analysis of features and texture analysis. STUDY DESIGN: Twenty-one images were analyzed, 10 from normal and 11 from cancerous mucosa. The classification was based on a regularity index dependent on shape, object orientation for establishing parallelism and five texture features derived using the co-occurrence image analysis method. RESULTS: Geometric analysis yielded an overall classification accuracy of 80%. The corresponding sensitivity and specificity were 94% and 64%, respectively. Using texture analysis, the overall classification accuracy was 90%, with a sensitivity and specificity of 82% and 100%, respectively. CONCLUSION: This initial study demonstrated that geometric and texture analysis techniques show promise for automated analysis of colon cancer.

Analysis of image cytometry data of fine needle aspirated cells of breast cancer patients: a comparison between logistic regression and artificial neural networks.

Image flow cytometry data of aspirated tumour cells from 102 patients with breast cancer were analysed and used as prognostic markers in an attempt to predict involvement of axillary lymph nodes and histological grade using logistic regression. Prediction was 70% for both nodal status and histological analyses. The outcome of this study is compared to an earlier study using the same cytological information to obtain prediction using a neural approach. Using artificial neural networks, prediction accuracy was 87% and 82% for nodal status and histological assessment, respectively. This study also attempts to identify the impact of individual prognostic factors. The statistical approach identified S-phase fraction and DNA-ploidy as the most important prediction markers for nodal status and histological assessment analyses. A comparison was made between these two quantitative techniques.

Neural network analysis of combined conventional and experimental prognostic markers in prostate cancer: a pilot study.

Prostate cancer is the second most common malignancy in men in the UK. The disease is unpredictable in its behaviour and, at present, no single investigative method allows clinicians to differentiate between tumours that will progress and those that will remain quiescent. There is an increasing need for novel means to predict prognosis and outcome of the disease. The aim of this study was to assess the value of artificial neural networks in predicting outcome in prostate cancer in comparison with statistical methods, using a combination of conventional and experimental biological markers. Forty-one patients with different stages and grades of prostate cancer undergoing a variety of treatments were analysed. Artificial neural networks were used as follows: eight input neurons consisting of six conventional factors (age, stage, bone scan findings, grade, serum PSA, treatment) and two experimental markers (immunostaining for bcl-2 and p53, which are both apoptosis-regulating genes). Twenty-one patients were used for training and 20 for testing. A total of 80% of the patients were correctly classified regarding outcome using the combination of factors. When both bcl-2 and p53 immunoreactivity were excluded from the analysis, correct prediction of the outcome was achieved in only 60% of the patients (P = 0.0032). This study was able to demonstrate the value of artificial neural networks in the analysis of prognostic markers in prostate cancer. In addition, the potential for using this technology to evaluate novel markers is highlighted. Further large-scale analyses are required to incorporate this methodology into routine clinical practice.

Expression of metastasis-associated genes h-mts1 (S100A4) and nm23 in carcinoma of breast is related to disease progression.

The murine 18A2/mts1 and its human homolog h-mts1 (S100A4), encoding a Ca2+-binding protein belonging to the S-100 family, are associated with high invasive and metastatic potentials of murine tumors, human tumor cell lines in vitro, and human tumors growing as xenografts. The nm23 is a putative metastasis-suppressor gene whose expression has been found to correlate inversely with the metastatic potential of some forms of human cancer. The products of both human genes alter cytoskeletal dynamics, with antagonistic effects. In view of the equivocal association of nm23 with the metastatic potential of human cancer, we suspected that the relative expression of h-mts1 and nm23 might reflect tumor progression more accurately than either of them alone. We describe here the expression of these genes in infiltrating ductal carcinomas of the breast and show that high h-mts1 expression is associated with metastatic spread to the regional lymph nodes. The expression of nm23 on its own did not show a statistically significant inverse correlation with nodal spread. However, the expression status of the two genes, taken together, correlated strongly with the occurrence of nodal metastases. Breast cancers with no detectable expression of h-mts1 were found to be estrogen and progesterone receptor positive. Expression of h-mts1 was not related to tumor differentiation. The clinical data, together with the state of expression of steroid receptors and the expression levels of h-mts1 and nm23 genes, were analyzed using artificial neural networks for accuracy in predicting nodal spread of the carcinomas. These analyses support the conclusion that, overall, h-mts1 expression appears to be associated with and indicative of more aggressive disease. Complemented with nm23, h-mts1 could provide a powerful marker of breast cancer prognosis.

Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa.

The development of an automated algorithm for the categorization of normal and cancerous colon mucosa is reported. Six features based on texture analysis were studied. They were derived using the co-occurrence matrix and were angular second moment, entropy, contrast, inverse difference moment, dissimilarity, and correlation. Optical density was also studied. Forty-four normal images and 58 cancerous images from sections of the colon were analyzed. These two groups were split equally into two subgroups: one set was used for supervised training and the other to test the classification algorithm. A stepwise selection procedure showed that correlation and entropy were the features that discriminated most strongly between normal and cancerous tissue (P < 0.0001). A parametric linear-discriminate function was used to determine the classification rule. For the training set, a sensitivity and specificity of 93.1% and 81.8%, respectively, were achieved, with an overall accuracy of 88.2%. These results were confirmed with the test set, with a sensitivity and specificity of 93.1% and 86.4%, respectively, and an overall accuracy of 90.2%.

Prediction of nodal metastasis and prognosis in breast cancer: a neural model.

BACKGROUND: An increasing number of women with breast cancer are detected with the disease at an early stage, when the lymph nodes are not involved. In order to obviate the necessity to carry out axillary dissection, accurate surrogates for lymph node involvement need to be identified. In this paper we have examined the use of a neural network to predict nodal involvement. The neural approach has also been extended to investigate its predictive applicability to the long-term prognosis of patients with breast cancer. A number of established and experimental prognostic markers have been studied in an attempt to accurately predict patient outcome 72 months after first examination. METHODS: 81, unselected patients, presenting clinically, who had all undergone mastectomy for invasive breast carcinoma were considered in this study. A total of 12 markers were analysed for the prediction of lymph node metastasis, while node status itself was used as an additional marker for the prognostic analysis. In this case the outcome related to whether a patient had relapsed within 72 months of diagnosis. In both cases, a number of marker combinations were analysed separately in an attempt to classify those most favourable marker interactions with respect to lymph node prediction and prognosis. Patients were randomly divided into a training set (n = 50) and a test set (n = 31). The simulation was developed using the NeuralWorks Professional II/Plus software (NeuralWare, Pittsburgh, Pa, USA). RESULTS: In the case of lymph node metastasis, the neural network was able to correctly predict axillary involvement, or otherwise, in 84% of the patients in the test set by considering 9 of the 12 available markers. This represents an improvement of 10% over the traditional approach which considers the tumour grade and size only. The sensitivity and specificity were also shown to be 73% and 90%, respectively. With regard to patient prognosis, again 84% classification accuracy was obtained using a subset of the markers, with a sensitivity of 50% and a specificity of 96%. CONCLUSIONS: Although this study considered a relatively small sample of patients, nevertheless it demonstrates that artificial neural networks are capable of providing strong indicators for predicting lymph node involvement. There is no longer a need for axillary dissection with all its implications in patient morbidity and demands on clinical resources. The management of breast cancer and the planning of strategies for adjuvant treatments is also facilitated by the use of neural networks for the long-term prognosis of patients.