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.