Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Adopt, Adapt, Enact or Use? – A Framework and Methodology for Extracting and Integrating Conceptual Mechanisms of IT Adoption and Use

Part 1: IS/IT Implementation and AppropriationInternational audienceInformation Systems (IS) are omnipresent in today’s organizations. While much research has been performed on adoption, implementation, and use of IS, still many practitioners are faced with IS change endeavors in organizations that equal “death march” projects and fail before or directly after go-live. Research with a positivist stance has thoroughly studied factors that describe individuals’ intentions to adopt or use technology, while largely ignoring social and organizational contexts. Researchers with a constructivist view, on the other hand, have studied how social processes and structures change or emerge in the light of the new IS. We suggest that there is a need to combine what we know from these two streams in an attempt to clarify terminological bafflement that seems to be caused by the different philosophical stances. Our paper contributes by suggesting a framework and methodology for collecting and re-assembling scattered conceptual pieces of organizational and individual IT adoption and integrating them into a coherent understanding

Evaluation of allergy and eosinophilia level in peripheral blood of patients with cardiovascular diseases

Background: Cardiovascular diseases are the most common cause of deaths in Iran and other developing countries. The risk factors for cardiovascular diseases are divided into two categories; the variable risk factors and the non-variable risk factors. Many recent studies evaluated the relationship between higher eosinophilia and allergy levels with the incidence, progress and severity of cardiovascular diseases, but the exact correlation between these two still remains  unknown. The current study was designed to assess the relationship between allergic responses and eosinophilia amongst patients with cardiovascular diseases in Ilam province, in comparison with healthy individuals.Materials and Methods: In this case-control study, we enrolled 59 cardiovascular patients and 55 healthy individuals without any history of allergy and parasitic infections. A questionnaire including questions about demographic data, family history of heart disease, history of diabetes, hyperlipidemia, physical activity, smoking, stress, dietary fat consumption, salt intake, allergies to certain substances, history of parasitic disease and history of hypertension was completed. The blood was taken from each participant and CBC and IgE titer were measured.Results: There was a significant relationship for the variables such as the family history of cardiovascular disease (P<0.001), diabetes (P<0.003), hyperlipidemia (P<0.0001), high blood pressure (P<0.0001) and physical activity (P<0.0001) between the case and the control groups. The mean IgE titer in case group was 95.3±71 and 62.44±49 in control group. The mean eosinophilia level in peripheral blood was 3.95±1.057 in case and 1.53±0.57 in control group. The difference between the IgE and eosinophilia levels in the case and the control groups was statistically significant (P<0.0001).Conclusion: Based on our results, it can be concluded the increase in levels of IgE and eosinophilia can be considered by cardiologists as a reliable diagnostic tool for predicting cardiovascular diseases

PENGELOMPOKKAN DAN PEMETAAN KARAKTERISTIK KEMISKINAN DI KABUPATEN MALUKU BARAT DAYA PROVINSI MALUKU DENGAN MENGGUNAKAN SELF-ORGANIZING MAP DAN ANALISIS BIPLOT

Berdasarkan hasil survei sosial ekonomi nasional (SUSENAS) BPS Provinsi Maluku menunjukkan bahwa kabupaten Maluku Barat Daya (MBD) merupakan kabupaten yang memiliki persentase tingkat kemiskinan tertinggi di provinsi Maluku dengan persentase tingkat kemiskinan 30,18 persen. Penelitian ini bertujuan untuk melakukan pengelompokkan desa/kecamatan di Kabupaten MDB guna melihat karakteristik kemiskinan pada setiap cluster. Selain itu, juga dilakukan pemetaan karakteristik kemiskinan untuk setiap desa/kecamatan di Kabupaten MBD sebagai upaya untuk mengetahui keragaman karakteristik kemiskinan. Metode pengelompokkan yang digunakan adalah algoritma jaringan syaraf tiruan Self Organizing Map (SOM) dan Biplot. Hasil penelitian memperlihatkan bahwa hasil pengelompokkan terbaik adalah dengan metode Biplot karena memiliki rasio yang lebih kecil. Adapun hasil pengelompokkan 17 kecamatan di kabupaten MBD terbagi dalam 4 cluster yakni cluster 1 terdiri kecamatan Pulau-Pulau Terselatan, Letti dan Moa; cluster 2 terdiri dari kecamatan Pulau-Pulau Babar dan Babar Timur; cluster 3 terdiri dari kecamatan Pulau Masela, Mdona Hyera, Kepulauan Romang, Damer, Wetar, dan Wetar Barat; sedangkan cluster 4 terdiri dari kecamatan Wetar Utara, Wetar Timur, Kisar Utara, Lakor, Dawelor Dawera dan Pulau Wetang. Ada 11 faktor yang mempengaruhi sehingga cluster 4 tergolong dalam cluster dengan karakteristik kemiskinan tertinggi. Sedangkan ada 4 faktor yang mempengaruhi sehingga cluster 3 tergolong dalam cluster dengan karakteristik kemiskinan cukup tinggi

Broadening horizons: the case for capturing function and the role of health informatics in its use

Background Human activity and the interaction between health conditions and activity is a critical part of understanding the overall function of individuals. The World Health Organization’s International Classification of Functioning, Disability and Health (ICF) models function as all aspects of an individual’s interaction with the world, including organismal concepts such as individual body structures, functions, and pathologies, as well as the outcomes of the individual’s interaction with their environment, referred to as activity and participation. Function, particularly activity and participation outcomes, is an important indicator of health at both the level of an individual and the population level, as it is highly correlated with quality of life and a critical component of identifying resource needs. Since it reflects the cumulative impact of health conditions on individuals and is not disease specific, its use as a health indicator helps to address major barriers to holistic, patient-centered care that result from multiple, and often competing, disease specific interventions. While the need for better information on function has been widely endorsed, this has not translated into its routine incorporation into modern health systems. Purpose We present the importance of capturing information on activity as a core component of modern health systems and identify specific steps and analytic methods that can be used to make it more available to utilize in improving patient care. We identify challenges in the use of activity and participation information, such as a lack of consistent documentation and diversity of data specificity and representation across providers, health systems, and national surveys. We describe how activity and participation information can be more effectively captured, and how health informatics methodologies, including natural language processing (NLP), can enable automatically locating, extracting, and organizing this information on a large scale, supporting standardization and utilization with minimal additional provider burden. We examine the analytic requirements and potential challenges of capturing this information with informatics, and describe how data-driven techniques can combine with common standards and documentation practices to make activity and participation information standardized and accessible for improving patient care. Recommendations We recommend four specific actions to improve the capture and analysis of activity and participation information throughout the continuum of care: (1) make activity and participation annotation standards and datasets available to the broader research community; (2) define common research problems in automatically processing activity and participation information; (3) develop robust, machine-readable ontologies for function that describe the components of activity and participation information and their relationships; and (4) establish standards for how and when to document activity and participation status during clinical encounters. We further provide specific short-term goals to make significant progress in each of these areas within a reasonable time frame

How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspective

Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of "small data" and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data

Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri