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PubMedDate not supplied

Three decades of research on computer applications in health care: medical informatics support at the Agency for Healthcare Research and Quality.

The Agency for Healthcare Research and Quality and its predecessor organizations-collectively referred to here as AHRQ-have a productive history of funding research and development in the field of medical informatics, with grant investments since 1968 totaling $107 million. Many computerized interventions that are commonplace today, such as drug interaction alerts, had their genesis in early AHRQ initiatives. This review provides a historical perspective on AHRQ investment in medical informatics research. It shows that grants provided by AHRQ resulted in achievements that include advancing automation in the clinical laboratory and radiology, assisting in technology development (computer languages, software, and hardware), evaluating the effectiveness of computer-based medical information systems, facilitating the evolution of computer-aided decision making, promoting computer-initiated quality assurance programs, backing the formation and application of comprehensive data banks, enhancing the management of specific conditions such as HIV infection, and supporting health data coding and standards initiatives. Other federal agencies and private organizations have also supported research in medical informatics, some earlier and to a greater degree than AHRQ. The results and relative roles of these related efforts are beyond the scope of this review.

Databases, Factual↗
PubMed1997

Health informatics education for pharmacy students in Romania.

The objectives and the curriculum of the health informatics course for pharmacy students at the University of Medicine and Pharmacy in Timisoara is presented. The relations with other disciplines and the integration into the overall educational program are discussed within the frame of the specific requirements of a pharmacy school. A description of our specific programs for pharmacy students devoted to laboratory works is also presented.

Computer Literacy↗
PubMed1989

Teaching medical informatics: teaching on the seams of disciplines, cultures, traditions.

This paper reviews different concepts of medical informatics and identifies two families of approaches to education in it: a "specialist" approach, whereby medical informatics is taught as a specialization track for established disciplines like medicine, computer science, nursing, engineering, etc., and a "generalistic" approach, whereby it is taught as an integrated discipline incorporating essential traits of the aforementioned disciplines. The pros and cons of these approaches are outlined. The need to accommodate specific requirements of education is emphasized and these are identified, together with an outline of particular challenges that we are facing.

Attitude to Computers↗
PubMed1998

Conceptual graph grammar--a simple formalism for sublanguage.

There are a wide variety of computer applications that deal with various aspects of medical language: concept representation, controlled vocabulary, natural language processing, and information retrieval. While technical and theoretical methods appear to differ, all approaches investigate different aspects of the same phenomenon: medical sublanguage. This paper surveys the properties of medical sublanguage from a formal perspective, based on detailed analyses cited in the literature. A review of several computer systems based on sublanguage approaches shows some of the difficulties in addressing the interaction between the syntactic and semantic aspects of sublanguage. A formalism called Conceptual Graph Grammar is presented that attempts to combine both syntax and semantics into a single notation by extending standard Conceptual Graph notation. Examples from the domain of pathology diagnoses are provided to illustrate the use of this formalism in medical language analysis. The strengths and weaknesses of the approach are then considered. Conceptual Graph Grammar is an attempt to synthesize the common properties of different approaches to sublanguage into a single formalism, and to begin to define a common foundation for language-related research in medical informatics.

Computer Graphics↗
PubMed2002

Theory, abstraction and design in medical informatics.

OBJECTIVE: To analyze the scientific and engineering components of Medical Informatics. A clear characterization of these components should be undertaken to categorize different areas of Medical Informatics and create a research agenda for the future. METHODS: We have adapted a classical ACM and IEEE report on computing to analyze Medical Informatics from three different viewpoints: Theory, Abstraction, and Design. RESULTS: We suggest that Medical Informatics can be considered from these three perspectives: (1) Theory, from which medical informaticians formally characterize the properties of the objects of study, creating new theories or using and adapting existing theories (e.g., from mathematics), (2) Abstraction, from which medical informaticians deal with all aspects of medical information and create new abstractions, methods, and technology-independent models, which can be experimentally verified, and (3) Design, from which medical informaticians develop systems or act as information brokers or advisors between medical and technology professionals, to improve the quality of computer applications in medicine. CONCLUSION: Based on this framework, we suggest that Medical Informatics has an independent scientific character, different from other applied informatics areas. Finally, we analyze these three perspectives using data mining in medicine.

Medical Informatics↗
PubMed1983

Training in medical informatics.

A course is described for the training of medical students in medical informatics (other terms: computational medicine, medical computing). The philosophy behind the course is that there are several modes or levels of human interaction in working with computers, running from registrative functions to assistance at diagnosis and therapy. The course, which consists of five full days, contains lessons in the areas of medical data bases, hospital information systems, medical records, biological signal analysis, computer-assisted diagnosis making, and patient simulation by CAI. Each group of two students has a terminal available, connected to a mini- and/or a microcomputer. The microprocessor takes care of signal analysis and the minicomputer is available for data base construction and interactive operations. The article describes the purposes and contents of all different lessons of this course.

Computer-Assisted Instruction↗
PubMed2001

Information visualisation in clinical Odontology: multidimensional analysis and interactive data exploration.

In 1995, the MedView project, based on a co-operation between computing science and clinical medicine was initiated. The overall goal of the project was to develop models, methods and tools to support clinicians in their daily diagnostic work. As part of MedView, two information visualisation tools were developed and tested as solutions to the problem of visualising clinical experience derived from large amounts of clinical data. The first tool (The Cube) was based on the idea of dynamic three-dimensional (3D) parallel diagrams, an idea similar to the notion of 3D parallel co-ordinates. The Cube was developed to enhance the clinician's ability to intelligibly analyse existing patient material and to allow for pattern recognition and statistical analysis. The second tool (SimVis) was based on a similarity assessment-based interaction model for exploring data, and was designed to help clinicians to classify and cluster clinical examination data. User interaction was supported by 3D visualisation of clusters and similarity measures. Both tools were tested on a knowledge base containing about 1500 examinations obtained from different clinics. Clinical practice indicated that the basic ideas are conceptually appealing to the involved clinicians as the tools can be used for generating and testing of hypotheses.

Humans↗
PubMed2000

Computers and the pediatric surgeon: a primer.

Computers have become an integral part of surgical practice. To use and maintain computers effectively, the surgeon must have a basic knowledge of the inner workings of the computer. It also is helpful to understand how the systems have evolved. Medical computing started in the financial department of large hospitals. From there it expanded to clinical data systems. Coincident with the development of clinical data systems was the introduction of the IBM personal computer in 1981 and the development of the Internet. All these events led to the use of the personal computer as a communication tool. This will shape much of how we use computers in the coming millennium. The computer is made up of several component parts. The brain of the computer is the central processing unit (CPU), which performs all of the calculations in the computer. The CPU works in concert with the random access memory (RAM) and hardware peripherals to perform tasks as directed by a program. To use this increasingly complex tool effectively, the pediatric surgeon must have a basic knowledge of information systems. It is through this knowledge that information systems may be used to enhance the efficiency of pediatric surgical practice.

Child↗
PubMed1991

Latent Semantic Indexing of medical diagnoses using UMLS semantic structures.

The relational files within the UMLS Metathesaurus contain rich semantic associations to main concepts. We invoked the technique of Latent Semantic Indexing to generate information matrices based on these relationships and created "semantic vectors" using singular value decomposition. Evaluations were made on the complete set and subsets of Metathesaurus main concepts with the semantic type "Disease or Syndrome." Real number matrices were created with main concepts, lexical variants, synonyms, and associated expressions. Ancestors, children, siblings, and related terms were added to alternative matrices, preserving the hierarchical direction of the relation as the imaginary component of a complex number. Preliminary evaluation suggests that this technique is robust. A major advantage is the exploitation of semantic features which derive from a statistical decomposition of UMLS structures, possibly reducing dependence on the tedious construction of semantic frames by humans.

Abstracting and Indexing↗