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Use of computer based decision tools to aid clinical decision making, has been a primary goal of research in biomedical informatics. Research in the last five decades has led to the development of Medical Decision Support (MDS) applications using a variety of modeling techniques, for a diverse range of medical decision problems. This paper surveys literature on modeling techniques for diagnostic decision support, with a focus on decision accuracy. Trends and shortcomings of research in this area are discussed and future directions are provided. The authors suggest that--(i) Improvement in the accuracy of MDS application may be possible by modeling of vague and temporal data, research on inference algorithms, integration of patient information from diverse sources and improvement in gene profiling algorithms; (ii) MDS research would be facilitated by public release of de-identified medical datasets, and development of opensource data-mining tool kits; (iii) Comparative evaluations of different modeling techniques are required to understand characteristics of the techniques, which can guide developers in choice of technique for a particular medical decision problem; and (iv) Evaluations of MDS applications in clinical setting are necessary to foster physicians' utilization of these decision aids.

Clinical decision making is a complex multi-stage process. Decision support can play an important role at each stage of this process. At present, the majority of clinical decision support systems have been focused on supporting only certain stages. In this paper we present the design and implementation of MET3--a prototype multi-agent system providing an integrative decision support that spans over the entire decision making process. The system helps physicians with data collection, diagnosis formulation, treatment planning and finding supporting evidence. MET3 integrates with external hospital information systems via HL7 messages and runs on various computing platforms available at the point of care (e.g., tablet computers, mobile phones). Building MET3 required sophisticated and reliable software technologies. In the past decade the open source software movement has produced mature, stable, industrial strength software systems with a large user base. Therefore, one of the decisions that should be considered before developing or acquiring a decision support system is whether or not one could use open source technologies instead of proprietary ones. We believe MET3 shows that the answer to this question is positive.

In this paper we have discussed the application of artificial intelligence in telemedicine using mobile device. The main goal of our research is to develop methods and systems to collect, analyze, distribute and use medical diagnostics information from multiple knowledge sources and areas of expertise. Physicians may collect and analyze information obtained from experts worldwide with the help of a medical decision support system. In this information retrieval system, modern communication tools such as computers and mobile phones can be used efficiently. In this work we propose a medical decision support system using the general packet radio service (GPRS). GPRS, a data extension of the mobile telephony standard Global system for mobile communications (GSM) is emerging as the first true packet-switched architecture to allow mobile subscribers to benefit from high-speed transmission rates and run JAVA based applications from their mobile terminals. An academic prototype of a medical decision support system using mobile device was implemented. The results reveal that the system could find acceptance from the medical community and it could be an effective means of providing quality health care in developing countries.

Healthcare in America continues to be of paramount importance, and one of the most highly debated public policy issues of our time. With annual expenditures already exceeding $2.4 trillion, and yielding less than optimal results, it stands to reason that we must turn to promising tools and solutions, such as information technology (IT), to improve service efficiency and quality of care. Presidential addresses in 2004 and 2008 laid out an agenda, framework, and timeline for national health information technology investment and development. A national initiative was long overdue. This report we show that advancements in both medical technologies and information systems can be capitalized upon, hence extending information systems usage beyond data collection to include administrative and decision support, care plan development, quality improvement, etc. In this paper we focus on healthcare services for palliative patients. We present the development and preliminary accounts of a successful initiative in the Medical Center of Central Georgia where footprints information technology was modified and integrated into the hospital's palliative care service and existing EMR systems. The project provides evidence that there are a plethora of areas in healthcare in which innovative application of information systems could significantly enhance the care delivered to loved ones, and improve operations at the same time..

Asthma is a chronic disease estimated to affect 6-7% of the total UK population. In addition, a number of studies have shown that asthma has become commoner since the 1970s, especially in children. The diagnosis of asthma can be difficult and its management requires the involvement of patients in a long-term treatment plan, something which general practitioners may be unable to achieve easily in the average 10-min consultation. As a consequence, asthma is underdiagnosed and undertreated. Deaths from the disease are often avoidable with timely and sufficient use of the available medication. In order to support this, the British Thoracic Society (BTS) has published guidelines for asthma management based upon a stepwise approach, in which a patient is categorized as being on one of five steps according to the severity of his or her asthma. The guidelines give "rules of thumb" for deciding when the patient should move up or down the steps. The most recent version of the guidelines also included special rules for children. Within a recent European Community project on Advanced Informatics in Medicine (AIM), we developed a prototype decision support system for asthma management targeted at the primary care setting and based on the British Thoracic Society guidelines. This paper reports this development, and describes the further work needed on the prototype. Plans for evaluation of the knowledge bases and for future full application production are also described.

This paper describes the conceptual framework and preliminary results of an outcome-oriented decision-support system prototype for the cardiovascular intensive care unit (CVICU). The major characteristics of this design include: (1) its problem-based approach to solving clinical problems; (2) an integrated structure with the hospital information system in terms of its data, model and knowledge bases; (3) proposed alternative modes of interaction that include monitoring and critiquing; (4) and research modules that design, manage, and analyze outcome-based clinical studies. At present, an initial prototype has been implemented on a PC as a set of modules accessible from a main menu. The structural framework of the overall system is fairly well defined but only limited quantitative, statistical and expert knowledge has been captured. The second phase of the project involves porting the prototype to a Unix workstation environment, refining and adding models to the model base, expanding its knowledge bases, reasoning capability, and testing the prototype with actual clinical cases in a real-time fashion.

This paper describes a microcomputer-based decision support system (DSS) for diabetes monitoring. The system's basic functions include a patient management subsystem, an electronic logbook, a nutrition and exercise module, a dictionary for diabetes-related terminology, a diabetic-physician data transfer module and an on-line help capability. DMSS (Diabetes Monitoring Support System) provides a data capturing capability, trend analysis, and nutrition/exercise decision support to improve the monitoring and maintenance of diabetes. It was designed to be used by both a physician and a patient and can also serve as a useful teaching aid for a new diabetic. Its basic advantages lie in its comprehensiveness and flexibility. It is also user-friendly, easy to operate, and does not require any previous computer experience.

This paper presents an overview of a problem requiring the design of a decision support system (DSS) to be used for infectious disease control at a Veterans Administration hospital. An overview of the DSS selected for this application is presented. The major design question addressed is that of determining the data base schema. An automated network data base design technique is presented along with a description of its use in actually designing a data base required for monitoring infectious disease control. This technique can generate data bases containing large numbers of data items and set relationships, while maintaining several useful properties within the data structure.

This paper describes the implementation of a computerized nosocomial infection control system for a 500-bed tertiary-care teaching hospital. It is implemented on a minicomputer that uses the relational data base management system INGRES, which is marketed by Relational Technology, Inc. This system, which replaces a manual one that depended on "needle sort" data cards, is designed to provide for entry of infection data that have been collected onto abstracting forms; decision support in the prospective analysis of suspicious infection rates or trends; generation of monthly, on-demand, and annual infection rate reports; retrospective interrogation and analysis of infection data for rates and trends that may explain or clearly indicate the sources of in-hospital (nosocomial) infections; updating of infection records as additional infection-related data become available and known to the hospital's infection control team; and ad hoc analysis and comparisons between data on control and infected patients, both prospectively and retrospectively.