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PubMed1998

Evolution of web site design: implications for medical education on the Internet.

Since its inception, the world wide web (WWW) has possessed the potential for becoming a 'watershed' medium for conveying complex, structured information across vast temporal and geographical barriers. In 1995, the MedWorld project (http:(/)/medworld.stanford.edu) was created at the Stanford University School of Medicine in an effort to innovate and explore the design process of creating WWW applications specifically for medical education. Until recently, the evolution of WWW applications has been mainly driven by technological advances in client-server technology, enabling or translating traditional modes of collaborative medical education (e.g. voice, presence, print, motion) into WWW devices and applications. Many of these applications, while technologically advanced, lack focused development of interface and interactivity design, which may enhance learning experiences. WWW applications which incorporate design innovation in parity with advances in client-server technology have been termed, 'third generation' web sites and have the potential to improve the quality of WWW applications designed for medical education. This work describes how the MedWorld project has created a 'third generation' WWW application by utilizing innovation in information, interface and interactivity design to create innovative WWW technology for the medical education arena.

Communications Media↗
PubMed1999

German adaptations of ICD-10.

The introduction of the ICD-10, published by WHO 1992-94 in English and by DIMDI 1994/95 in German, is a very slow process. Some states introduced ICD-10 for the preparation of statistics of mortality, but only few use it for morbidity. ICD-9 is in Germany only in hospitals still in use. Much effort was put into the improvement of the official ICD-10 and the development of additional aids for more simple and better encoding of diagnoses. Thus a revision especially for ambulatory health care (ICD-10-SGBV with the incorporated ICD-10-Basisschlüssel) and a collection of German terms and expressions of diagnoses that are not at all part of the official ICD-10 (ICD-10-Diagnosenthesaurus) were published. Three years ago a conversion table ICD-9/-10 was developed which can now be harmonised with WHO's Translator. The experiences with all these instruments are satisfying. The development of methods for automatic encoding of free-text phrases of diagnoses has now been started.

Disease↗
PubMedDate not supplied

The medical folder as an active tool in defining the clinical decision-making process.

Whenever the user of a consultation system needs to know a large amount of information items that are possibly inter-related, a system that is able to cooperate with the user can simplify the interaction and increase its speed. In fact, these systems can recognize the goals of the user, individuate the information related to his goals, and finally focus their attention on this information [1]. This paper describes research work on the design and creation of a medical folder management system capable of establishing cooperative dialogue with users who have access to the information contained therein. Particular attention has thus been paid to the problems of man-computer dialogue and user models. The research work has addressed the problem of integrating into the system knowledge about the medical domain and users (physicians are only used for the time being), which are both necessary to activate a cooperative dialogue. After analysing the literature on the problems of user models, this paper presents and formalizes a study performed at the IVth Surgical Clinic of the Rome University Umberto I General Hospital to identify and represent how, when, and with what goals the physician consults the medical folder, as well as the dialogue procedures he normally adopts. The paper also illustrates important characteristics of the CADMIO prototype, which has been developed since the study was made. CADMIO stores information about users for use in recognizing and interpreting their behaviour, providing help, and in acquiring and returning further information. Whilst always bearing in mind the physician's pre-established goals, it structures information to facilitate his consultation activity, offering him options for the retrieval of further information semantically linked to that already obtained.

Decision Making, Computer-Assisted↗
PubMed2001

Impact of an electronic information system on physician workflow and data collection in the intensive care unit.

OBJECTIVE: To test the hypotheses that: (1) integrating information processing tasks using an electronic clinical information system (ECIS) decreases time to complete these tasks by hand; and (2) structured data entry encourages generation of more detailed records and capture of specific data elements even when entry is voluntary. DESIGN: Prospective observational time analysis during medical documentation tasks. Retrospective analysis of clinical documentation completed by hand or electronically. SETTING: Eleven bed pediatric intensive care unit within an academic medical center. PARTICIPANTS: Five pediatric intensive care medicine attending physicians. MEASUREMENTS: Compared handwritten and electronic documentation to determine: (1) time spent entering data or composing notes; (2) number of descriptors documenting patients' physical exams; (3) users' preferences for structured or unstructured data entry; (4) frequency of documenting specific data elements related to nutritional support. RESULTS: Documentation time varied by user but not charting method: it took 13 % less time to document using the ECIS but this was not significant. Electronic documents were more detailed than handwritten containing 50 % more descriptors (17.8 +/- 1.4 vs 11.6 +/- 1.4) overall and some data elements that were not handwritten: information related to nutritional supplementation was recorded in 13 % of electronic documents but in none of 89 handwritten documents. CONCLUSIONS: Electronic and handwritten documentation consumed equal amounts of time. Structured entry, compared to handwriting, may encourage recording of specific or otherwise unincorporated data elements resulting in a more detailed record. This suggests that user interfaces and decision support components may influence both the types and complexity of clinical data recorded by caregivers.

Analysis of Variance↗
PubMed2001

A comparison of classification algorithms to automatically identify chest X-ray reports that support pneumonia.

We compared the performance of expert-crafted rules, a Bayesian network, and a decision tree at automatically identifying chest X-ray reports that support acute bacterial pneumonia. We randomly selected 292 chest X-ray reports, 75 (25%) of which were from patients with a hospital discharge diagnosis of bacterial pneumonia. The reports were encoded by our natural language processor and then manually corrected for mistakes. The encoded observations were analyzed by three expert systems to determine whether the reports supported pneumonia. The reference standard for radiologic support of pneumonia was the majority vote of three physicians. We compared (a) the performance of the expert systems against each other and (b) the performance of the expert systems against that of four physicians who were not part of the gold standard. Output from the expert systems and the physicians was transformed so that comparisons could be made with both binary and probabilistic output. Metrics of comparison for binary output were sensitivity (sens), precision (prec), and specificity (spec). The metric of comparison for probabilistic output was the area under the receiver operator characteristic (ROC) curve. We used McNemar's test to determine statistical significance for binary output and univariate z-tests for probabilistic output. Measures of performance of the expert systems for binary (probabilistic) output were as follows: Rules--sens, 0.92; prec, 0.80; spec, 0.86 (Az, 0.960); Bayesian network--sens, 0.90; prec, 0.72; spec, 0.78 (Az, 0.945); decision tree--sens, 0.86; prec, 0.85; spec, 0.91 (Az, 0.940). Comparisons of the expert systems against each other using binary output showed a significant difference between the rules and the Bayesian network and between the decision tree and the Bayesian network. Comparisons of expert systems using probabilistic output showed no significant differences. Comparisons of binary output against physicians showed differences between the Bayesian network and two physicians. Comparisons of probabilistic output against physicians showed a difference between the decision tree and one physician. The expert systems performed similarly for the probabilistic output but differed in measures of sensitivity, precision, and specificity produced by the binary output. All three expert systems performed similarly to physicians.

Acute Disease↗
PubMed1995

The frequencies of disease names with the natural language used in the hospital information system.

The statistical behavior of disease names referred by physicians with the natural language in a large hospital information system is little known despite the theoretical and practical interest. To address this issue, we reviewed and investigated the usage-frequencies of 18,274 disease names, 10,288 for outpatient care and 7986 for inpatient care, referred from October 1983 to June 1992 with the notation of the natural language in Japanese by the use of the registration-retrieval system of disease names at Fukui Medical School, Japan. Consequently, we found that the investigated distributions did not conform to the Poisson distribution, but conformed well to the Polya-Eggenberger distribution in both cases of outpatient and inpatient care. It implies that the disease names with the natural language are possibly referred by physicians with some interrelations.

Disease↗
PubMed2000

The implementation of speech recognition in an electronic radiology practice.

For both efficiency and economic reasons, our practice (200,000 examinations) has converted all remote dictation to speech recognition transcription (PowerScribe, L & H, Burlington, MA). The design criteria included complete automation to the existing radiology information system (RIS), with full RIS capabilities immediately available following dictation. All dictations for computed tomography, magnetic resonance imaging, ultrasound, and nuclear medicine were converted from remote transcription to speech recognition over a 2-week period (following a 4-week installation phase and 8 days of training). The average turnaround time for these reports decreased from approximately 2 hours to less than 1 minute. Reports are then sent to the institutional Electronic Medical Record and are available throughout all facilities in a nominal 2 minutes. Speech recognition rates were surprisingly high, although certain phrases caused consistent difficulties and certain staff required retraining. This presents our analysis of both successful and problematic areas during our design and implementation, as well as statistical performance analyses.

Efficiency↗
PubMed2000

[Experiences with a current speech recognition system in creating cardiology reports].

Development of speech recognition software is at a stage where you can use it effectively for creating cardiological reports or at least parts of it. We were very successful in using the Dragon Naturally Speaking system for our reports and we don't need a secretary for our writings any longer. Very important for effective work is a fast and good PC hardware, especially a good sound system and a sufficient amount of internal memory, also important is patience of the user, because a longer training phase is required. I recommend for own experiences to use the standard version which is cheap and includes all necessary features. Following some fundamental rules anyone can be successful with speech recognition. Development of speech recognition is increasing rapidly so that everyone of us will get in contact with it sooner or later, it would be better to be prepared for it now.

Cardiology↗
PubMed2001

Managing predefined templates and macros for a departmental speech recognition system using common software.

The authors have developed a networked database system to create, store, and manage predefined radiology report definitions. This was prompted by complete departmental conversion to a computer speech recognition system (SRS) for clinical reporting. The software complements and extends the capabilities of the SRS, and 2 systems are integrated by means of a simple text file format and import/export functions within each program. This report describes the functional requirements, design considerations, and implementation details of the structured report management software. The database and its interface are designed to allow all radiologists and division managers to define and update template structures relevant to their practice areas. Two key conceptual extensions supported by the template management system are the addition of a template type construct and allowing individual radiologists to dynamically share common organ system or modality-specific templates. In addition, the template manager software enables specifying predefined report structures that can be triggered at the time of dictation from printed lists of barcodes. Initial experience using the program in a regional, multisite, academic radiology practice has been positive.

Computer Peripherals↗
PubMedDate not supplied

Computer-assisted dynamic integration of multiple medical thesauruses.

We have previously described a user-interactive rule-based computer program (Dyna-SaurI) designed for dynamic thesaurus integration, and demonstrated its efficacy on integrating dermatological subsets of the MeSH and SNOMED thesauruses. In the present study, we have refined our rules for merging and mapping multiple thesauruses and tested these rules. We then applied them with a set of optimized parameters to the integration of a third thesaurus, a subset of the International Coding Index for Dermatology, with the Integrated MeSH-SNOMED thesaurus. The parameter changes resulted in improved ranking of more specific and conceptually closer terms.

Abstracting and Indexing↗
PubMed1995

Free text analysis.

In the context of hospital information systems (HIS) medical free text analysis is reviewed with respect to current automated approaches to literature retrieval, case retrieval and fact retrieval from textual data in the patient record. The Unified Medical Language System (UMLS) project has enormously stimulated current research. It is expected that UMLS knowledge sources and SNOMED III (which need a translation into other languages as soon as possible) as well as the conceptual graphs formalism, could become standards to utilize free text information contained in HIS databases.

Abstracting and Indexing↗
PubMed1994

The GALEN project.

The GALEN project is developing language independent concept representation systems as the foundations for the next generation of multilingual coding systems. It aims to support the flexibility required to cope with the diversity amongst medical applications, while ensuring the coherence necessary for integration and re-use of terminologies. GALEN is developing a fully compositional and generative formal system for modelling concepts: the GALEN Representation and Integration Language (GRAIL) Kernel. Its goal is to overcome many of the problems with traditional coding and classification systems, in particular the combinatorial explosion of terms in enumerative systems and the generation of nonsensical terms in partially compositional systems. It will also provide a clean separation between the concept model and linguistic mechanisms which interpret that model (i.e., the words in a specific language, syntax, alternative phrasings, etc.) in order to allow the development of multilingual systems. GRAIL aims to be formally sound and produce models that are verifiable and contain no contradictions or ambiguities, with realistic human effort. A Coding Reference (CORE) Model of medical terminology covering is being developed which aims to represent the core concepts in for example pathology, anatomy and therapeutics, that have widespread applicability in medical applications. It should also provide the basis for specialist extensions according to the formal principles of GRAIL. The main results of GALEN will be delivered as a Terminology Server (TeS) which encapsulates and coordinates the functionality of the concept module, multilingual module, and code conversion module, and also provides a uniform applications programming interface and network services for use by external applications.

Databases, Bibliographic↗
PubMed1994

MENELAS: an access system for medical records using natural language.

The overall goal of MENELAS is to provide better access to the information contained in natural language patient discharge summaries, through the design and implementation of a pilot system able to access medical reports through natural languages. A first, experimental version of the MENELAS indexing prototype for French has been assembled. Its function is to encode free text PDSs into both an internal representation and ICD-9-CM nomenclature codes. A preliminary evaluation shows the potential for reasonable coverage and precision. The MENELAS prototype will be enhanced and extended into a pilot system which will be tested in two hospital sites.

Abstracting and Indexing↗
PubMed1995

Computerized measurement of the content analysis of natural language for use in biomedical and neuropsychiatric research.

Over several decades, the senior author, with various colleagues, has developed an objective method of measuring the magnitude of commonly useful and pertinent neuropsychiatric and neuropsychological dimensions from the content and form analysis of verbal behavior and natural language. Extensive reliability and validation studies using this method have been published involving English, German, Spanish and many other languages, and which confirm that these Content Analysis Scales can be reliably scored cross-culturally and have construct validity. The validated measures include the Anxiety Scale (and six subscales), the Hostility Outward Scale (and two subscales), the Hostility In Scale, the Ambivalent Hostility Scale, the Social Alienation-Personal Disorganization Scale, the Cognitive Impairment Scale, the Depression Scale (and seven subscales), and the Hope Scale. Here, the authors report the development of artificial intelligence (LISP based) software that can reliably score these Content Analysis Scales, whose achievement facilitates the application of these measures to biomedical and neuropsychiatric research.

Diagnosis, Computer-Assisted↗
PubMed1995

An intelligent interactive system for delivering individualized information to patients.

This paper is a report on the first phase of a long-term, interdisciplinary project whose goal is to increase the overall effectiveness of physicians' time, and thus the quality of health care, by improving the information exchange between physicians and patients in clinical settings. We are focusing on patients with long-term and chronic conditions, initially on migraine patients, who require periodic interaction with their physicians for effective management of their condition. We are using medical informatics to focus on the information needs of patients, as well as of physicians, and to address problems of information exchange. This requires understanding patients' concerns to design an appropriate system, and using state-of-the-art artificial intelligence techniques to build an interactive explanation system. In contrast to many other knowledge-based systems, our system's design is based on empirical data on actual information needs. We used ethnographic techniques to observe explanations actually given in clinic settings, and to conduct interviews with migraine sufferers and physicians. Our system has an extensive knowledge base that contains both general medical terminology and specific knowledge about migraine, such as common trigger factors and symptoms of migraine, the common therapies, and the most common effects and side effects of those therapies. The system consists of two main components: (a) an interactive history-taking module that collects information from patients prior to each visit, builds a patient model, and summarizes the patients' status for their physicians; and (b) an intelligent explanation module that produces an interactive information sheet containing explanations in everyday language that are tailored to individual patients, and responds intelligently to follow-up questions about topics covered in the information sheet.

Anthropology, Cultural↗