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PubMedDate not supplied

Where does informatics fit in health care organizations?

Why is medical informatics important to health care leaders? As an emerging science, informatics focuses on applying computing and communication technology to decision making for clinicians and managers. It enhances the understanding of how information and communication systems can impact the work health care managers must accomplish. As the cost of technology for digital information management continues to decline, organizations and individuals will look for ways to offset the human costs of managing and conveying information. The way of the paper medical record is being replaced by the less expensive and more efficient digital information systems. Leaders of health care organizations need to look for every opportunity to deploy networks and computers to reduce the labor costs of data collection, storage, retrieval, and analysis.

Computer Communication Networks↗
PubMed1986

Computers in dentistry.

Traditionally, each professional school has developed its own course or department of health care informatics. We may find it more efficient and productive to establish a department of Medical Informatics to serve all health science colleges. Therefore the initial costs would be reduced and the common knowledge bank would be greatly increased. In a position paper written for the Association of Academic Health Centers lies the detailed plans for the establishment of an integrated health sciences center computer resource. The inclusion of computer courses in the curriculum can be summarized in the following action step. The chief administrative officer of the academic health center should facilitate the work of the deans of the schools of the health professions in convening faculty task forces that will address the introduction of computer literacy and computer applications in health care to the curricula. Although dental informatics is still in its infancy, we must lay a solid foundation for the controlled growth and development of this field of dental science. The problems encountered in developing a course in dental informatics are not unique to this one area. All specialties of health care are grappling with the questions posed by medical informaticians. Although each specialty has its own needs and requirements, the basic underlying principles of medical informatics remains the same. The knowledge we can gain by the exchange of experiences between all fields can greatly increase the speed and accuracy of development. The primary goal of computer literacy in dental education should be to prepare our students for the changing practice environment of the future.

Computer Literacy↗
PubMed1986

Computerized history taking for training medical students.

A lesson on computerized patient history-taking is described of a block-course in medical informatics, offered to medical students during their clinical training. The lesson stresses the importance of careful observation during the patient interview and the storage of original data without human interpretation. The lesson has been evaluated during 1 academic year and errors in different categories of medical data were determined: data exactly given by the patient, data hidden in the interview, and data not given by the patient. Mean errors in these types of data appeared to be 21, 47, and 58%, respectively.

Computer-Assisted Instruction↗
PubMed2006

Identifying important concepts from medical documents.

Automated medical concept recognition is important for medical informatics such as medical document retrieval and text mining research. In this paper, we present a software tool called keyphrase identification program (KIP) for identifying topical concepts from medical documents. KIP combines two functions: noun phrase extraction and keyphrase identification. The former automatically extracts noun phrases from medical literature as keyphrase candidates. The latter assigns weights to extracted noun phrases for a medical document based on how important they are to that document and how domain specific they are in the medical domain. The experimental results show that our noun phrase extractor is effective in identifying noun phrases from medical documents, so is the keyphrase extractor in identifying important medical conceptual terms. They both performed better than the systems they were compared to.

Algorithms↗
PubMed2005

Annual report to the nation on the status of cancer, 1975-2002, featuring population-based trends in cancer treatment.

BACKGROUND: The American Cancer Society (ACS), the Centers for Disease Control and Prevention (CDC), the National Cancer Institute (NCI), and the North American Association of Central Cancer Registries (NAACCR) collaborate annually to provide information on cancer rates and trends in the United States. This year's report updates statistics on the 15 most common cancers in the five major racial/ethnic populations in the United States for 1992-2002 and features population-based trends in cancer treatment. METHODS: The NCI, the CDC, and the NAACCR provided information on cancer cases, and the CDC provided information on cancer deaths. Reported incidence and death rates were age-adjusted to the 2000 U.S. standard population, annual percent change in rates for fixed intervals was estimated by linear regression, and annual percent change in trends was estimated with joinpoint regression analysis. Population-based treatment data were derived from the Surveillance, Epidemiology, and End Results (SEER) Program registries, SEER-Medicare linked databases, and NCI Patterns of Care/Quality of Care studies. RESULTS: Among men, the incidence rates for all cancer sites combined were stable from 1995 through 2002. Among women, the incidence rates increased by 0.3% annually from 1987 through 2002. Death rates in men and women combined decreased by 1.1% annually from 1993 through 2002 for all cancer sites combined and also for many of the 15 most common cancers. Among women, lung cancer death rates increased from 1995 through 2002, but lung cancer incidence rates stabilized from 1998 through 2002. Although results of cancer treatment studies suggest that much of contemporary cancer treatment for selected cancers is consistent with evidence-based guidelines, they also point to geographic, racial, economic, and age-related disparities in cancer treatment. CONCLUSIONS: Cancer death rates for all cancer sites combined and for many common cancers have declined at the same time as the dissemination of guideline-based treatment into the community has increased, although this progress is not shared equally across all racial and ethnic populations. Data from population-based cancer registries, supplemented by linkage with administrative databases, are an important resource for monitoring the quality of cancer treatment. Use of this cancer surveillance system, along with new developments in medical informatics and electronic medical records, may facilitate monitoring of the translation of basic science and clinical advances to cancer prevention, detection, and uniformly high quality of care in all areas and populations of the United States.

Age Distribution↗
PubMed1998

An Internet station for telemedicine in the Azerbaijan Republic.

We have established the first medical Internet and telemedicine station in the Azerbaijan Republic. The station began work on 16 June 1997. In the next three months a list of local and international medical electronic resources was drawn up and contacts made with medical informatics organizations and medical libraries. Five telemedicine consultations were carried out with Moscow clinics in specialties such as cardiology, ophthalmology, endocrinology and surgery. The quickest and most cost-effective way of improving medical care in Azerbaijan is to provide medical information support using modern telecommunications.

Azerbaijan↗
PubMed2003

[Quality of and quality assurance in health care preventive health care].

Quality in healthcare and healthcare protection represents one from the most complex characteristics of the state and functioning of healthcare system. In fact, the quality represents the responsibility of all the components/participants of the system: inhabitants, actual patients, the potentional patients, decisions makers, purchasors and the performers of services. The quality of the healthcare protection is that level of the insured healthcare services to the individuals or the inhabitants who satisfies the probability of the desired outcome for health in relation quality of the healthcare protection is that level of the insured healthcare services to the individual or the inhabitants who satisfies the probability of the desired outcome for health in relation to the standards of the healthcare protection. The estimations of the actual degree of quality of the corrective action in aim that the noticed defects get removed. The quality estimation is the first step in the quality insurance and includes the analysis of the structural, process and outcome variables. In the majority of countries of the world estimation I insurance of quality mostly are represented at hospitals, and one from the reasons is that the hospital protection is very expensive I required the strict control of the resources of cost. In the other hand in the primary healthcare protection was performed significantly lesser number of the research of the estimation I insurance in this segments of the health care protection significantly are more complex, in our country are being performed the pilot studies in this domain. And that at the chair for the medical informatics of the Medical faculty of Sarajevo, and in future will be intensified the activities thanks to the insurance of the Agency for Healthcare Quality and Accreditation FB&H in the performing of the strategic aims of the reforms of the healthcare by the competent ministaries of health.

Bosnia and Herzegovina↗
PubMed1995

The architecture of a distributed medical dictionary.

Exploiting high-speed computer networks to provide a national medical information infrastructure is a goal for medical informatics. The Distributed Medical Dictionary under development at Baylor College of Medicine is a model for an architecture that supports collaborative development of a distributed online medical terminology knowledge-base. A prototype is described that illustrates the concept. Issues that must be addressed by such a system include high availability, acceptable response time, support for local idiom, and control of vocabulary.

Artificial Intelligence↗
PubMed1996

A poor precedent.

The single greatest impediment to the successful leveraging of computer-based patient records (CPRs) is the difficulty of creating and maintaining comparable patient descriptions. Specifically, it will be hard to justify the investment required to deploy CPRs widely if the patient descriptions they store are not comparable across successive releases of controlled health-care vocabularies. Thus, it is necessary to solve the controlled health-care vocabulary update problem for CPRs before the comparability of patient descriptions can be sustained. What may seem to be a narrow technical problem of interest only to maintainers of health-care enterprise systems is, instead, a central problem of medical informatics. Cimino's "Formal descriptions and adaptive mechanisms for changes in controlled medical vocabularies" describes a classification of the changes appearing in the 1994 Edition of the International Classification of Diseases (ICD-9-CM). His paper describes the conversion of differences detected between the 1993 and 1994 releases of ICD-9-CM and a conversion of the elements of the classification into the required formal changes to the Medical Entities Dictionary (MED), part of the CPR in use at Columbia Presbyterian Medical Center. Because the process of detecting differences begins with an empirical analysis of the ASCII representations of the 1993 and 1994 releases of ICD-9-CM, it is impossible for a computer program to infer the intent of the changes that caused the differences; instead, a content expert must infer the intent and then update the MED accordingly. A typical task is to infer whether a change in naming also reflects a change in the meaning named. While Cimino's methods and their execution are exemplary in every respect, they nevertheless constitute a kind of "reverse engineering"-an ad hoc attempt to infer intent from details. Reverse engineering of changes to controlled medical vocabularies is a poor precedent. Such methods should be viewed as necessary short-term expedients only, and all parties concerned should work toward an incremental plan by which the intent of changes to controlled health-care vocabularies can be made both explicit and machine processible. Only then can the comparability of patient descriptions be sustained.

Disease↗
PubMed2002

Evaluation in health informatics: computer simulation.

The evaluation of complex medical informatics applications involves not only the information system, but also its impact on the organizational environment in which it is implemented. In instances where these applications cannot be evaluated with traditional experimental methods, computer simulation provides a flexible approach to evaluation. The construction of a computer simulation model involves the development of a model that represents important aspects of the system under evaluation. Once validated, the model can be used to study the effects of variation in system inputs, differences in initial conditions and changes in the structure of the system. Three examples are discussed, namely, a wide-area health care network, physician order entry into a hospital information system, and the use of an information system designed to prevent medical errors that lead to adverse drug events in hospitals.

Computer Simulation↗
PubMed2003

Criteria for classification of medical information.

Medical information, which is the central notion in medical informatics, covers a large scale of structures and forms. Several classifications are possible and two criteria have been used in this paper: structural level and informational level. According to structural level we can distinguish three major areas: bioinformatics and neuroinformatics for molecular/cellular level, medical informatics for individual level and health informatics for community level and healthcare units. According to informational level the terms of data and knowledge are used and the representative information for each structural level is analysed also from this point of view: Finally, information transfer from living systems to computers is also seen through the structural point of view.

Biological Science Disciplines↗
PubMed2006

A MEDLINE categorization algorithm.

BACKGROUND: Categorization is designed to enhance resource description by organizing content description so as to enable the reader to grasp quickly and easily what are the main topics discussed in it. The objective of this work is to propose a categorization algorithm to classify a set of scientific articles indexed with the MeSH thesaurus, and in particular those of the MEDLINE bibliographic database. In a large bibliographic database such as MEDLINE, finding materials of particular interest to a specialty group, or relevant to a particular audience, can be difficult. The categorization refines the retrieval of indexed material. In the CISMeF terminology, metaterms can be considered as super-concepts. They were primarily conceived to improve recall in the CISMeF quality-controlled health gateway. METHODS: The MEDLINE categorization algorithm (MCA) is based on semantic links existing between MeSH terms and metaterms on the one hand and between MeSH subheadings and metaterms on the other hand. These links are used to automatically infer a list of metaterms from any MeSH term/subheading indexing. Medical librarians manually select the semantic links. RESULTS: The MEDLINE categorization algorithm lists the medical specialties relevant to a MEDLINE file by decreasing order of their importance. The MEDLINE categorization algorithm is available on a Web site. It can run on any MEDLINE file in a batch mode. As an example, the top 3 medical specialties for the set of 60 articles published in BioMed Central Medical Informatics & Decision Making, which are currently indexed in MEDLINE are: information science, organization and administration and medical informatics. CONCLUSION: We have presented a MEDLINE categorization algorithm in order to classify the medical specialties addressed in any MEDLINE file in the form of a ranked list of relevant specialties. The categorization method introduced in this paper is based on the manual indexing of resources with MeSH (terms/subheadings) pairs by NLM indexers. This algorithm may be used as a new bibliometric tool.

Abstracting and Indexing↗
PubMed2003

The operating room of the future: white paper summation.

On November 8 and 9, 2001, leading experts in patient safety, medical informatics, advanced surgical devices, telesurgery, and surgical facilities met to formulate strategic directions for the "OR of the Future" in both military and civilian healthcare. The meeting was co-hosted by the Telemedicine and Advanced Technology Research Center (TATRC) part of the U.S. Army Medical Research and Materiel Command at Fort Detrick, and the University of Maryland Medical Center. Researchers, surgeons, and experts in the field of operating room (OR) technology addressed the current state of research and technological developments. Experts in (1) patient safety, (2) medical informatics, (3) advanced surgical devices, (4) telesurgery, and (5) surgical facilities met in focused work groups to develop a proposed research agenda for each content area. Afterwards, each focused group agreed to develop a 'White Paper' on each specific area, addressing the current and future prospectus. In addition, they attempted to provide a recommended research roadmap for the 'OR of the Future.'

Facility Design and Construction↗
PubMedDate not supplied

Analysis of citations in four MIE proceedings.

Scientific work in medical informatics has been going on for more than 25 years; the results of this work are published in one way or another. How are these publications used to avoid the invention of the wheel for the second and third time? To get an overview of this, four proceedings of the Medical Informatics Europe conferences out of the last eight years have been analysed, to show the behaviour of authors in different countries and in different years in respect of citations.

Authorship↗
PubMed2003

Evidence-based medicine and psychiatric practice.

Evidence-based medicine (EBM) has been defined as "the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients." EBM requires the ability to apply a knowledge of medical informatics (e.g., efficiently searching the medical literature) and clinical epidemiology (e.g., being able to critically appraise the literature) to the treatment of individual patients. This article provides an introduction to the history, philosophy, and methods of EBM as applied to psychiatric practice. The article summarizes the five-step EBM model: (1) formulate the question; (2) search for answers; (3) appraise the evidence; (4) apply the results; and (5) assess the outcome. Resources (including Web sites) for further learning are provided.

Clinical Competence↗