Marius Fieschi

Enrico Coiera

Yu-Chan Jack Li

A fundamental challenge for medical informatics is to develop and apply better ways of understanding how information technologies and methods can help support the best care for every patient every day given available medical knowledge and resources. In order to provide the most effective healthcare possible, the activities of teams of health professionals have to be coordinated through well-designed processes centered on the needs of patients. For information systems to be accepted and used in such an environment, they must balance standardization based on shared medical knowledge with the flexibility required for customization to the individual patient. Developing innovative approaches to design and build evidence-based careflow management systems is essential for providing the knowledge management infrastructure of health care organizations that seeks to increase performance in delivering high quality care services by efficiently exploiting available resources. Parallel challenges arise in the organization of research at the biological and clinical levels, where the focus on systematically organizing and supporting processes of scientific inquiry by novel informatics methods and databases are in their very early stages. These Proceedings of Medinfo 2004 demonstrate the base of knowledge medical informatics professionals will collectively draw upon in the years ahead to meet these challenges and realize opportunities.

Medinfo

Health Data Processing: Systemic Approaches focuses on the design of health information systems and touches on the main themes of medical informatics and public health. This book is written for all health professionals in practice or in training and especially for decision-makers or future decision-makers in the field of health information systems. It aims to foster understanding of the determinants of health information systems and the main principles of information processing. It deals with the question of the reuse of data for purposes other than those which have presided over their collection. The data collected for the care must be available to enrich the knowledge, to make the health watch, to study the evolution of the health taking into account data of the environment, social factors, ... In addition to the questions of protection of the Individual liberties that these data and technologies make more acute, the irruption of the large masses of genetic data in particular, and more generally heterogeneous "big data," pose problems peculiar to this data but join the problems of interoperability and data integration. The book develops the methodological and conceptual aspects related to these issues. Proposes a methodology and an approach to the development of health information systems for a better use of digital technologies integrated in interoperable, coherent and evolving systems that make it possible to counter the counterproductive juxtaposition of multiple applications Illustrates a systemic, transversal conceptual vision that supports the complex reality of the healthcare world, where the interoperability of agents (professionals and software) is central It also aims to reuse the resources of data for knowledge improvement, health security and public health

Systemic Approaches

Health Data Processing

John Fox

Rolf Engelbrecht

The current scarcity of expert systems where the reasoning is based on Bayesian probability theory may be due to misconceptions about probabilities found in the literature. As argued by Cheeseman (1985), these misconceptions have led to the attitude: "The Bayesian approach doesn't work - so here is a new scheme". Several of these expert systems based on ad hoc "probability" concepts have been successful in a number of ways, demonstrating the necessity of being able to handle uncertainty in medical expert systems. They also demonstrate the need for a theoretically sound handling of uncertainty. In Andersen et al. (1986) it was postulated that knowledge organized in a causal network can be used for a unified approach to the main tasks of a medical expert system: diagnosis, planning of tests and explanations. The present paper explores this postulate in a causal probabilistic network. It also provides a practical demonstration that the problems supposedly associated with probabilistic networks are either non-existent or that practical solutions can be found. This paper reports on the methods implemented in MUNIN* -an expert system for electromyography (EMG) (Andreassen et al. 1987). EMG is the diagnosis of muscle and nerve diseases through analysis of bioelectrical signals from muscle and nerve tissue. In Andreassen et al.

Marius Fieschi

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