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PubMed1995

[Medical informatics in research, teaching and patient management].

The field of medical informatics in its current understanding is defined and criteria distinguishing this field from similar areas are provided. Special consideration is given to its position at a School of Medicine - in particular to the University of Vienna Medical School with the Vienna General Hospital as its teaching hospital. Demands for medical informatics and electronic data processing (EDP) in this extended field of activity come from four different sources: (1) research in medical informatics, (2) teaching of medical informatics as well as EDP training, (3) EDP service for research and teaching, and (4) EDP hospital operations to assist patient care. (Purely administrative EDP demands are not considered here.) It is shown that the different demands can be fulfilled by the usually available institutions involved in medical informatics and EDP at a School of Medicine. At many places these institutions are as follows: (1) a department or division of medical informatics with a possibly attached computer center dedicated to provide assistance in the area of research and teaching, (2) the computer center of the respective university the School of Medicine belongs to, (3) the computer center of the hospital-owned institution responsible for all EDP activities connected to patient care, and (4) external software companies and EDP training centers. To succeed in the development of an exhaustive, school-wide system of medical informatics and EDP that considers the different demands in research, teaching, and EDP hospital operations equally, close and well-suited coordination between the institutions involved is necessary.

Artificial Intelligence↗
PubMedDate not supplied

Value of a long-term clinical database.

To study the value of historical clinical information, which we defined as data more than one year old, we measured the frequency and patterns of use of historical laboratory data among 87 providers in a multispecialty group practice delivering ambulatory care. During a one-month observation period, 38% of the providers requested historical data for use in clinical decision making. The requests were made for 19% of the patients, and represent 4% of the provider-initiated transactions on the computing system (328 per month). A survey also indicated that historical data are beneficial in clinical decision making.

Attitude of Health Personnel↗
PubMed1993

Discrimination and reproducibility of an information maximizing multivariable model.

Predictor variables for multivariate rules are frequently selected by methods that maximize likelihood rather than information. We compared the discrimination and reproducibility of a prediction rule for pneumonia derived using extended dependency analysis (EDA), an information maximizing variable selection program, with that of a validated rule derived using logistic regression. Discrimination was measured by receiver-operating characteristic (ROC) analysis, and reproducibility by rederivation of the rule on 200 replicate samples of size 250 and 500, generated from a training cohort of 905 patients using Monte Carlo techniques. Four of the five predictor variables selected by EDA were identical to those selected by logistic regression. With each variable weighted by its conditional contribution to total information transmission, EDA discriminated pneumonia and nonpneumonia in the training cohort with an ROC area of 0.800 (vs 0.816 for logistic regression, p = 0.60), and in the validation cohort with an area of 0.822 (vs 0.821 for logistic regression, p = 0.98). EDA demonstrated reproducibility comparable to that of logistic regression according to most criteria for replicability. Replicate EDA models showed good discrimination in the training and testing cohorts, and met statistical criteria for validation (no significant difference in ROC areas at a one-tailed alpha level of 0.05) in 80.8% to 94.2% of cases. We conclude that extended dependency analysis selected the most important variables for predicting pneumonia, based on a validated logistic regression model. The information-theoretic model showed good discriminatory power, and demonstrated reproducibility according to clinically reasonable criteria. Information-theoretic variable selection by extended dependency analysis appears to be a reasonable basis for developing clinical prediction rules.

Cohort Studies↗
PubMed1993

Work-sampling: a statistical approach to evaluation of the effect of computers on work patterns in healthcare.

An increasing number of health-care institutions are in the process of implementing clinical computing systems. The need for an accurate assessment of the clinical, administrative, social, and financial effects of such systems has been recognized. Techniques have been developed to evaluate these effects on the work patterns of health-care workers including: time-motion analysis, subjective evaluations, review of departmental statistics, personal activity records, and work-sampling. This study reviews these techniques, discusses both positive and negative aspects, and presents a step-by-step description of work-sampling.

Humans↗
PubMedDate not supplied

[Coding, nomenclatures, PMSI, medical records, cards with microprocessors: what coherence?].

Computerization of information on health care delivery is being implemented in France. Within hospitals, the PMSI program aimed at coding and storing medical information on patients and their treatment has started. More recently was launched a program for computerizing outpatient care (coding and storing information on medical acts, pathologies and prescriptions). Patient individual cards with microprocessor have been implemented. The challenge ahead is how to coordinate all these processes to make them compatible, and coherent and to avoid wasteful disorders.

Delivery of Health Care↗
PubMed1997

Harnessing and controlling the information deluge.

A number of information management tools are available to assist the general practitioner to cope more readily with the deluge of information presented to them. These tools can also enable the practitioner to seek out relevant pieces of information in a timely fashion to assist with patient care.

Humans↗
PubMed1998

Linking a medical vocabulary to a clinical data model using Abstract Syntax Notation 1.

We have created a clinical data model using Abstract Syntax Notation 1 (ASN. 1). The clinical model is constructed from a small number of simple data types that are built into data structures of progressively greater complexity. Important intermediate types include Attributes, Observations, and Events. The highest level elements in the model are messages that are used for inter-process communication within a clinical information system. Vocabulary is incorporated into the model using BaseCoded, a primitive data type that allows vocabulary concepts and semantic relationships to be referenced using standard ASN. 1 notation. ASN. 1 subtyping language was useful in preventing unbounded proliferation of object classes in the model, and in general, ASN.1 was found to be a flexible and robust notation for representing a model of clinical information.

Artificial Intelligence↗
PubMed1998

How groups co-ordinate their concepts and terminology: implications for medical informatics.

Conceptual and terminological systems are established and maintained by the communities who use them. This paper reports experiments which investigate the role of communication and interaction in the process. The experiments show that isolated pairs of communicators and virtual communities of interacting pairs naturally converge on their own conceptual and terminological systems when confronted with a common task. The results also indicate that the system converged on is optimal for that particular group engaged in that particular task. These findings are discussed in relation to the increasing use of tightly coordinated medical teams and its implications for getting them to adopt standardized medical terminologies.

Concept Formation↗
PubMed1998

Cooperative knowledge evolution: a construction-integration approach to knowledge discovery in medicine.

In this paper, we perform a cognitive analysis of knowledge discovery processes. As a result of this analysis, the construction-integration theory is proposed as a general framework for developing cooperative knowledge evolution systems. We thus suggest that for the acquisition of new domain knowledge in medicine, one should first construct pluralistic views on a given topic which may contain inconsistencies as well as redundancies. Only thereafter does this knowledge become consolidated into a situation-specific circumscription and the early inconsistencies become eliminated. As a proof for the viability of such knowledge acquisition processes in medicine, we present the IDEAS system, which can be used for the intelligent documentation of adverse events in clinical studies. This system provides a better documentation of the side-effects of medical drugs. Thereby, knowledge evolution occurs by achieving consistent explanations in increasingly larger contexts (i.e., more cases and more pharmaceutical substrates). Finally, it is shown how prototypes, model-based approaches and cooperative knowledge evolution systems can be distinguished as different classes of knowledge-based systems.

Artificial Intelligence↗
PubMed1998

Standards to support development of terminological systems for healthcare telematics.

The Technical Committee on "Medical Informatics" of the European Committee for Standardization (CEN/TC251) is supporting developers of terminological systems in healthcare by a series of standards. The dream of "universal" coding system was abandoned in favor of a coherent family of terminologies, diversified according to tasks; two ideas were introduced: (1) the "categorical structure", i.e. a model of semantic categories and their relations within a subject field and (2) the "cross-thesaurus", i.e. a system of descriptors to build a systematic representation (called here "dissection") for each terminological phrase, coherent across diverse terminologies on a given subject field. The goal is to assure coexistence and interoperability (and reciprocal support for development and maintenance) to three generations of systems: (1) traditional paper-based systems (first generation); (2) compositional systems built according to a categorical structure and a cross-thesaurus (second generation) and (3) formal models (third generation). Various scenarios are presented, on the exploitation of computer-based terminological systems. The idea of "operational meaning" of terminological phrases within administrative and organizational contexts and the idea of "task-oriented details" are also introduced, to justify and exploit design constraints on terminological systems.

Europe↗
PubMed1994

Applications of medical informatics in antibiotic therapy.

The Infectious Disease Society of America is concerned about the excessive and inappropriate use of antibiotics in U.S. hospitals. Applications of Medical Informatics can help improve the use of antibiotics and help improve patient care by monitoring and managing enormous amounts of patient information. Monitoring the duration of every antibiotic ordered in the hospital or keeping tract of the antibiotic susceptibilities for five years are examples of tasks better performed by computers. The impact of computers in medicine is seen by some as disappointing. The computer revolution has not had the impact in medicine experienced by other areas. The acceptance and use of computers by medicine will be evolutionary rather than revolutionary. In 1979, the MYCIN project demonstrated that the computer could aid physicians in the selection of antibiotics. However, MYCIN was never clinically used because physicians were require to enter all patient information into the computer. The development of computerized medical records is an essential step to further the development and implementation of computer-aided decision support. The science of Medical Informatics is still relatively new but is emerging as a distinct academic field. A few hospitals are now installing information systems and have determined that these systems will play an essential role in their ability to survive into the next century. The telephone and the automobile have been recognized as two of the most important tools for improving medical care during the past 100 years. People could more readily get medical care and the time to transmit medical information was greatly reduced through physician use of the telephone and automobile. The computer is a tool that can be used to help physicians manage the great amount of medical information being generated every day. The computer can also alert the physician of patient conditions that need attention. However, it is the physician who must use and apply the computer provided information. Thus, the computer will assist but not replace physicians in providing medical care.

Anti-Bacterial Agents↗
PubMed2004

Medical informatics standards applicable to emergency department information systems: making sense of the jumble.

The adoption of medical informatics standards by emergency department information systems (EDISs) is not universal, despite obvious benefits. Clinicians and administrators looking to obtain an EDIS need to know exactly what the various standards can do for them and how the systems they depend on can be integrated and extended. In addition to the standard methods for systems to communicate (chiefly Health Level 7 [HL7]) and those required for submission of claims (Current Procedural Terminology [CPT]-4, International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM], and X12N), there are several other available standards that are clinically useful and can greatly improve the ability to access and exchange patient information. Major advances in the Unified Medical Language System of the National Library of Medicine have made the patient medical record information standards (Systematized Nomenclature of Medicine [SNOMED], Logical Observation Identifiers, Names, and Codes [LOINC], RxNorm) easily accessible. Detailed knowledge of the arcana associated with the technical aspects of the standards is not needed (or desired) by clinicians to use standards-based systems. However, some knowledge about the commonly used standards is helpful in choosing an EDIS, interfacing the EDIS with the other hospital information systems, extending or upgrading systems, and adopting decision support technologies.

Humans↗
PubMed1996

A Web-based architecture for the intelligent management of chronic patients.

We describe a distributed architecture for medical informatics applications, based on the World-Wide Web (WWW) environment. After discussing previous experiences in the application of the WWW for medical purposes, we outline the features of a Common Lisp HTTP server designed to provide access to medical informatics applications using a standard Web browser. As an example of application, we describe a system for therapy planning and revision in the field of insulin-dependent diabetes. The system performs automatic data analysis and interpretation and provides advice on possible adjustments to the therapeutic protocol that the patients are following, taking advantage of the network and multimedia capabilities offered by the WWW for user interaction.

Chronic Disease↗