bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influenza Immunizations in the Elderly: A Continuous Quality Improvement Project
Authors: Evans M. F. , Bloom J. , Juma A. 
Published: March 2018

AbstractAs part of the continuous quality improvement program at The Toronto Hospital’s Department of Family & Community Medicine (TTH-DFCM), it was considered necessary to examine the structures, processes and outcomes of influenza immunization for the elderly. Objective: The study sought to (a) document the current influenza immunization process; (b) quantify influenza immunization rates for elderly patients during two consecutive immunization seasons (1996 and 1997), and compare these rates across physician teams, attending staff vs. residents, patient gender, and patient age groups; (c) compare influenza immunization rates with other centers; and (d) identify barriers and propose solutions to improve influenza immunization rates in the elderly. Design: Evaluation Formative Research. Setting: A computerized roster of 15,000 patients at The Toronto Hospital, Department of Family and Community Medicine, a University of Toronto academic teaching center. Participants: Active patients age 65 years and over. Dependent variable: Influenza immunization. Independent variables: Physician Teams, Physician status, Patient gender, and Patient age group. Results: Immunization rates of attendees increased from 75.4% to 78.7%; over 3% increase from 1996 to 1997. Major subgroups which benefited from increased immunization rates were patients in the Blue team, patients age 70-74 years, and female patients. Conclusion: This study presents a rigorous examination of the components of the influenza immunization program, and demonstrates improved immunization rates over a two-year period. Suggestions for future action have been identified. The study design can also serve as a model for future clinical quality improvement projects.
Assessing and Improving the Quality of Information for Health Evaluation and Promotion
Authors: Gillies A. 
Published: March 2018

AbstractStudies by the author based upon a sample of over 1.1 million patients in the North West of England have shown that the introduction of computers cannot be demonstrated to produce significant improvements in health promotion activity. Other studies have revealed error rates in health-care information systems in excess of 50%. This paper considers the reasons for these findings and demonstrate that there is a level of maturity of information management required before information systems can make a significant contribution to health promotion and evaluation. With the advent of locality commissioning and a re-affirmation of the emphasis upon health promotion activity set out in Health of the Nation and Our Healthier Nation [8], the role of information management has become even more significant. The paper presents a maturity model developed by the author, known as General Practice Information Maturity Model (GPIMM) that provides a strategic framework to improve the use of information by practices and to improve practice to enable viable sharing of accurate information. The paper will show the model can be used as a basis for the development of training and information strategies. The model identifies computerised practices as being in one of five stages of maturity: (1) Computerised, (2) Computerised PHC Team, (3) Coded, (4) Bespoke, (5) Paperless. The model specifically leads practices to a point in their development where they are able to carry out health evaluation, promotion and audit activities as a routine activity. Once these activities form part of practice activity they are then extended to encompass a group of practices such as a Primary Care Group. Finally, a case study is presented to show how the model was used to enable two very different but geographically neighbouring practices to work together towards the establishment of a common dataset. This dataset will be used to enable health promotion and evaluation activity to be provided for a total population of over 20,000 patients.
From Phenotype to Genotype: Issues in Navigating the Available Information Resources
Authors: McCray A. T. , Bodenreider O. , Mitchell J. A. 
Published: March 2018

Summary

Objectives:
As part of an investigation of connecting health professionals and the lay public to both disease and genomic information, we assessed the availability and nature of the data from the Human Genome Project relating to human genetic diseases.

Methods:
We focused on a set of single gene diseases selected from main topics in MEDLINEplus, the NLM’s principal resource focused on consumers. We used publicly available websites to investigate specific questions about the genes and gene products associated with the diseases. We also investigated questions of knowledge and data representation for the information resources and navigational issues.

Results:
Many online resources are available but they are complex and technical. The major challenges encountered when navigating from phenotype to genotype were (1) complexity of the data, (2) dynamic nature of the data, (3) diversity of foci and number of information resources, and (4) lack of use of standard data and knowledge representation methods.

Conclusions:
Three major informatics issues arise from the navigational challenges. First, the official gene names are insufficient for navigation of these web resources. Second, navigational inconsistencies arise from difficulties in determining the number and function of alternate forms of the gene or gene product and maintaining currency with this information. Third, synonymy and polysemy cause much confusion. These are severe obstacles to computational navigation from phenotype to genotype, especially for individuals who are novices in the underlying science. Tools and standards to facilitate this navigation are sorely needed.
Scalable Software Architectures for Decision Support
Authors: Musen M. A. 
Published: March 2018

AbstractInterest in decision-support programs for clinical medicine soared in the 1970s. Since that time, workers in medical informatics have been particularly attracted to rule-based systems as a means of providing clinical decision support. Although developers have built many successful applications using production rules, they also have discovered that creation and maintenance of large rule bases is quite problematic. In the 1980s, several groups of investigators began to explore alternative programming abstractions that can be used to build decision-support systems. As a result, the notions of “generic tasks” and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) problem-solving methods – domain-independent algorithms for automating stereotypical tasks – and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper highlights how developers can construct large, maintainable decision-support systems using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.
A Realistic Model of Human Structure from the Visible Human Data
Authors: Pflesser B. , Pommert A. , Riemer M. , Schubert R. , Schiemann T. , Tiede U. , Schumacher U. , Höhne K. H. 
Published: March 2018

AbstractThe computer-based 3D models of the human body reported to date suffer from poor spatial resolution. The Visible Human project has delivered high resolution cross-sectional images that are suited for generation of high-quality models. Yet none of the 3D models described to date reflect the quality of the original images. We present a method of segmentation and visualization which provides a new quality of realism and detail. Using the example of a 3D model of the inner organs, we demonstrate that such models, especially when combined with a knowledge base, open new possibilities for scientific, educational, and clinical work.
Analysis of Data Associated with Seemingly Temporal Clustering of a Rare Disease
Authors: Goldbourt U. , Chen R. 
Published: March 2018

Abstract:Three statistical tests aimed at detecting temporal clustering within a given short series of diagnoses are presented. These tests are based on a standardized time interval between consecutive diagnoses. Two of the tests (the Cuscore and the Sets tests) are derived from sequential monitoring techniques which are sensitive to temporal clustering within the data set. The third test (R test) is not sequential and its sensitivity is focused on the average increase in the overall rate of the disease rather than on clustering within the series. Power curves are presented for conditions related to the intensity level of the subtle epidemic, the cluster size and the number of diagnoses. None of the techniques showed highest efficiency over all the specified conditions. The R test is the most efficient when the relative risk is 2 or less, and the Cuscore test is the most efficient method when the relative risk is ≥2.5.
Knowledge-Acquisition Tools for Medical Knowledge-Based Systems
Authors: Lanzola G. , Quaglini S. , Stefanelli M. 
Published: March 2018

Abstract:Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.
Representing Clinical Narratives Using Conceptual Graphs
Authors: Baud R. H. , Rassinoux A. M. , Wagner J. C. , Lovis C. , Juge C. , Alpay L. L. , Michel P. A. , Degoulet P. , Scherrer J. R. 
Published: March 2018

Abstract:The analysis of medical narratives and the generation of natural language expressions are strongly dependent on the existence of an adequate representation language. Such a language has to be expressive enough in order to handle the complexity of human reasoning in the domain. Sowa’s Conceptual Graphs (CG) are an answer, and this paper presents a multilingual implementation, using French, English and German. Current developments demonstrate the feasibility of an approach to natural Language Understanding where semantic aspects are dominant, in contrast, to syntax driven methods. The basic idea is to aggregate blocks of words according to semantic compatibility rules, following a method called Proximity Processing. The CG representation is gradually built, starting from single words in a semantic lexicon, to finally give a complete representation of the sentence under the form of a single CG. The process is dependent on specific rules of the medical domain, and for this reason is largely controlled by the declarative knowledge of the medical Linguistic Knowlege Base.
The Learning Process in the Epistemology of Medical Information
Authors: Clancey W. J. 
Published: March 2018

Abstract:Progress in the past few decades in representing medical knowledge, along with the availability of low-cost, powerful workstation computers, has increased interest in encoding all medical records in electronic form. But despite the advantages of legibility, access, and automated performance reviews, computerization may also restrict what can be recorded and rigidify health care interactions with patients. Balancing the beneficial and negative effects requires an understanding of medical practice, especially the difference between human knowledge and today’s computer programs. Human perceptual and conceptual capabilities bring an aspect of improvisation and reinterpretation to every human action, which in general computers today cannot replicate. Designers of medical record systems must consider the creative process by which descriptions are generated by people, as well as the collaborative process by which descriptions of past work are reinterpreted for guiding future decisions. This paper provides a framework for understanding these issues, illustrated by design opportunities.
An Ontology-based Multi-agent Architecture for Distributed Health-care Information Systems
Authors: Lanzola G. , Stefanelli M. , Falasconi S. 
Published: March 2018

Abstract:Managing patients in a shared-care context is a knowledge-intensive activity. To support cooperative work in medical care, computer technology should both augment the capabilities of individual specialists and enhance their ability of interacting with each other and with computational resources. Thus, a major shift is needed from centralized first generation health-care information systems to distributed environments composed of several interconnected agents, cooperating in maintaining a full track of the patient clinical history and supporting health-care providers in all the phases of the patient-management process. This paper outlines a general methodology to make architectural choices while designing or integrating new software components into a distributed health-care information system. A particular stress is laid on the specification of shared conceptual models, or ontologies, providing agents committing to them with the common semantic foundation required for effective interoperation.
Evaluation of Techniques for the Presentation of Laboratory Data. II: Accuracy of Interpretation
Authors: Hoeke J. O. O. , Bonke B. , van Strik R. , Gelsema E. S. , Verheij R. 
Published: March 2018

Abstract:Four tabular and two graphical techniques for the presentation of laboratory test results were evaluated in a reaction time experiment with 25 volunteers. Artificial variables and values were used to represent sets of 12 laboratory tests to eliminate the possible effects of clinical experience. Analyses focused on four types of errors in interpretation. Color-coded tables and one of the color-coded graphs greatly (2.8 times or better) reduced the number of incorrectly classified test results, as compared to the reference presentation technique. This was mainly due to a reduction of the number of abnormal test results that were not noticed by the subjects when using these presentation techniques.
Controlled Trial on Medical Birth Notification Design
Authors: Mäkelä M. , Teperi J. , Hemminki E. 
Published: March 2018

AbstractIn a controlled trial, two different forms were compared with respect to thoroughness of completing medical data on procedures during pregnancy, labor, and in newborns. The contents of 547 forms filled in by hospital personnel were compared to data abstracted from hospital records by trained research assistants. Forms with open-ended questions lacked information on 29% of the most common procedures and forms with a check-box format 9%. The results support the use of the check-box format in routine collection of data on common medical procedures.
Is a Picture Worth a Thousand Medical Words? A Randomized Trial of Reporting Formats for Medical Research Data
Authors: Elting L. S. , Bodey G. P. 
Published: March 2018

AbstractMonitoring data that vary over time is an essential component of medical practice. This is doubly true in clinical trials in which the overall safety and efficacy of investigational treatments in populations must be monitored in addition to the status of the individual patients who receive them. We report the results of a randomized trial of four reporting methods for time-dependent information derived from clinical trials; narrative text, table, pie chart and icon. Multivariate analysis of variance with a repeated measures design was used to analyze the efficiency of subjects’ (physicians, research nurses and laboratory personnel) assimilation of information. Icons were found to be superior to the other reporting formats tested in both speed (p <0.0001) and accuracy (p = 0.02). The differences were most pronounced in subjects’ first exposure to the data, suggesting that icons reduce the time needed for training. We conclude that icons are a valuable method for presentation of time-dependent information in medical settings.
Multivariate Procedures to Describe Clinical Staging of Melanoma
Authors: Manton K. G. , Woodbury M. A. , Wrigley J. M. , Cohen H. J. 
Published: March 2018

AbstractAnalyzing multivariate clinical data to identify subclasses of patients being treated for a specific disease may improve patient management and increase understanding of the behavior of disease under clinical conditions. In some cases, patients have been classified on prognostic characteristics using standard risk assessment procedures (e.g.. Cox’ regression). This requires long term follow-up, differentiates patients only on attributes relevant to survival, and assumes that patients are sampled from a common population. Other approaches involve the use of clustering algorithms to classify patients into categories based on multiple clinical attributes. We illustrate the use of a multivariate statistical procedure to directly characterize patients on multiple clinical characteristics. The procedure is designed to analyze discrete response data with parameters representing individual differences within groups. Its use is illustrated for patients with Stage I melanoma in determining how age is related to treatment response in different patient groups.
Balancing Acute and Long-term Care: The Mathematics of Throughput in Departments of Geriatric Medicine
Authors: Harrison G. W. , Millard P. H. 
Published: March 2018

AbstractThe empirical distribution of length of stay of patients in departments of geriatric medicine is fit extremely well by a sum of two exponentials. Most of the patients in a geriatric department are rehabilitated and discharged or they die within a few weeks of admission, but the few who become long-stay patients remain for months or even years. A model is presented for the flow of patients through a geriatric department, which has analogies to models of drug flow in pharmacokinetics. The theoretical model explains why the empirical pattern of length of stay in the occupied beds fits a sum of two exponentials; conversely, the empirical distribution, obtained from the midnight bed state report, can be used to study the effect of various policy decisions on both immediate and future admission rates for the department, and shows the benefits of policies which reduce long-stay patient numbers by improving long-stay rehabilitation.
Comparison of Rheumatological Diagnoses by a Bayesian Program and by Physicians
Authors: Moens H. J. , van der Korst J. K. 
Published: March 2018

AbstractA Bayesian decision support system was developed for the diagnosis of rheumatic disorders. Knowledge in this system is represented as evidential weights of findings. Simple weights were calculated as the logarithm of likelihood ratios on the basis of 1,000 consecutive patients from a rheumatological clinic. The effect of various methods to improve performance of the system by modification of the weights was studied. Three methods had a mathematical basis; a fourth consisted of weights adapted by a human expert, which allowed inclusion of diagnostic rules such as defined in widely accepted criteria sets. The system’s performance was measured in a test population of 570 different cases from the same clinic and compared with predictions of diagnostic outcome made by rheumatologists. The weights from a human expert gave optimal results (sensitivity 65% and specificity 96%), that were close to the physicians’ predictions (sensitivity 64% and specificity 98%). The methods to measure the performance of the various models used in this study emphasize sensitivity, specificity and the use of receiver operating characteristics.
Medical Reasoning and Patient Requests in Decision-Making for Female Genitourinary Infections
Authors: Timpka T. , Buur T. 
Published: March 2018

AbstractThe aim of this study was to investigate General Practitioners’ (GPs’) collection and analysis of data on women presenting with lower genitourinary (GU) complaints, also taking into account patient preferences. For 135 patients seen after nurse triage, eleven GPs recorded the clinical data they had explored, an evaluation of the patient’s desire (or not) for medical interventions, a preliminary diagnosis with certainty estimate, and management actions.Whether a clinical data item was explored varied between 97% of the patients for fever to 15% for pelvic examination. Decisions regarding diagnosis followed to a large extent results from screening laboratory tests. For prescription of antibiotics the patient’s desire (or not) for medical interventions was a significant discriminant. The GPs reported the lowest diagnostic certainty for diagnosis of urethritis, the highest for cystitis. Factors contributing to physicians’ uncertainty in their decisionmaking were assumed patient reluctance for medical interventions and negative screening tests.In conclusion, patient requests and preferences significantly influenced the GPs, even though results from screening laboratory tests were mainly used as the basis for decisions. The high reliance on screening tests may often cause other clinical information to be neglected. Thus, the uncertainty regarding patients with negative laboratory test results may reflect that important information is not included in the clinical analysis.
Poisson Approximation to a Negative Binomial Process in the Surveillance of Rare Health Events
Authors: Gallus G. , Marchi M. , Radaelli G. 
Published: March 2018

AbstractThe Poisson approximation to a negative binomial process is evaluated regarding the surveillance of rare health events in the framework of the “Sets” scheme. This scheme defines an alarm in terms of “distance” between consecutive events of interest. The system’s parameters are determined by minimizing the expected delay for an alarm when a given increase in the event rate has occurred, subject to a restriction on the rate of false alarms. It is shown that the main consequence of the Poisson approximation lies in an increase of the false alarm probability with respect to the assigned one, whilst influence on the expected delay for a true alarm is lower. It is, however, found that over a large range of practical instances, the Poisson assumption provides a reasonable description of the negative binomial process.