Search results for: knowledge discovery in healthcare databases

Authors: Pavol Tanuska, Pavel Vazan, Michal Kebisek, Dominika Jurovata

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The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system, thus, the proposed solution has been verified. The paper documents how it is possible to apply new discovery knowledge to be used in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.

Keywords: hierarchical process control, knowledge discovery from databases, neural network, process control

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Authors: Mohit Kumar, Mayur Betharia

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Healthcare domain, with the emergence of Electronic Medical Record (EMR), collects a lot of data which have been attracting Data Mining expert’s interest. In the past, doctors have relied on their intuition while making critical clinical decisions. This paper presents the means to analyze the Medical workflows to get business insights out of huge dumped medical databases. Market Basket Analysis (MBA) which is a special data mining technique, has been widely used in marketing and e-commerce field to discover the association between products bought together by customers. It helps businesses in increasing their sales by analyzing the purchasing behavior of customers and pitching the right customer with the right product. This paper is an attempt to demonstrate Market Basket Analysis applications in healthcare. In particular, it discusses the Market Basket Analysis Algorithm ‘Apriori’ applications within healthcare in major areas such as analyzing the workflow of diagnostic procedures, Up-selling and Cross-selling of Healthcare Systems, designing healthcare systems more user-friendly. In the paper, we have demonstrated the MBA applications using Angiography Systems, but can be extrapolated to other modalities as well.

Keywords: data mining, market basket analysis, healthcare applications, knowledge discovery in healthcare databases, customer relationship management, healthcare systems

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Authors: Maram Saleh

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Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains.

Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Yousuf Nasser Al Khamisi

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Purpose: This paper presents a Knowledge Transfer (KT) Framework in healthcare sectors by applying a systematic literature review process to the healthcare organizations domain to identify enablers and barriers of KT in Healthcare. Methods: The paper conducted a systematic literature search of peer-reviewed papers that described key elements of KT using four databases (Medline, Cinahl, Scopus, and Proquest) for a 10-year period (1/1/2008–16/10/2017). The results of the literature review were used to build a conceptual framework of KT in healthcare organizations. The author used a systematic review of the literature, as described by Barbara Kitchenham in Procedures for Performing Systematic Reviews. Findings: The paper highlighted the impacts of using Knowledge Management (KM) concept at a healthcare organization in controlling infectious diseases in hospitals, improving family medicine performance and enhancing quality improvement practices. Moreover, it found that good-coding performance is analytically linked with a knowledge sharing network structure rich in brokerage and hierarchy rather than in density. The unavailability or ignored of the latest evidence on more cost-effective or more efficient delivery approaches leads to increase the healthcare costs and may lead to unintended results. Originality: Search procedure produced 12,093 results, of which 3523 were general articles about KM and KT. The titles and abstracts of these articles had been screened to segregate what is related and what is not. 94 articles identified by the researchers for full-text assessment. The total number of eligible articles after removing un-related articles was 22 articles.

Keywords: healthcare organisation, knowledge management, knowledge transfer, KT framework

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: Vahid Bairami Rad

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Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented.

Keywords: spatial data base, knowledge discovery database, data mining, spatial relationship, predictive data mining

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

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Authors: Shogo Kato, Daisuke Okamoto, Satoko Tsuru, Yoshinori Iizuka, Ryoko Shimono

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Healthcare safety has been perceived important. It is essential to prevent troubles in healthcare processes for healthcare safety. Trouble prevention is based on trouble prediction using accumulated knowledge on processes, troubles, and countermeasures. However, information on troubles has not been accumulated in hospitals in the appropriate structure, and it has not been utilized effectively to prevent troubles. In the previous study, though a detailed knowledge acquisition process for trouble prediction was proposed, the knowledgebase for countermeasures was not involved. In this paper, we aim to propose the structure of the knowledgebase for countermeasures in the knowledge acquisition process for trouble prediction in healthcare process. We first design the structure of countermeasures and propose the knowledge representation form on countermeasures. Then, we evaluate the validity of the proposal, by applying it into an actual hospital.

Keywords: trouble prevention, knowledge structure, structured knowledge, reusable knowledge

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Authors: Diego De Castro Rodrigues, Marcelo Lisboa Rocha, Daniela M. De Q. Trevisan, Marcos Dias Da Conceicao, Gabriel Rosa, Rommel M. Barbosa

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The use of association rules algorithms within data mining is recognized as being of great value in the knowledge discovery in databases. Very often, the number of rules generated is high, sometimes even in databases with small volume, so the success in the analysis of results can be hampered by this quantity. The purpose of this research is to present a method for reducing the quantity of rules generated with association algorithms. Therefore, a computational algorithm was developed with the use of a Weka Application Programming Interface, which allows the execution of the method on different types of databases. After the development, tests were carried out on three types of databases: synthetic, model, and real. Efficient results were obtained in reducing the number of rules, where the worst case presented a gain of more than 50%, considering the concepts of support, confidence, and lift as measures. This study concluded that the proposed model is feasible and quite interesting, contributing to the analysis of the results of association rules generated from the use of algorithms.

Keywords: data mining, association rules, rules reduction, artificial intelligence

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Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

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In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: decision support system, data mining, knowledge discovery, data discovery, fuzzy logic

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Authors: Asima Mehaboob Khan, Rizwan Taj

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Purpose: This study aims to estimate the knowledge, attitude and practices of medical ethics among young healthcare professionals. Method: A qualitative descriptive study was conducted among young healthcare professionals from both public and private sector medical institutions. Using the convenience sampling technique, 272 healthcare professionals participated in this study. A pre-structured modified questionnaire was used to collect the data. Descriptive analyses were executed for each variable. Result: About 76.47% of healthcare professional considers the importance of adequate knowledge of medical ethics, and 82.24% declared lecture, seminars and clinical discussion as the source of their medical knowledge of biomedical ethics. About 42.44% of healthcare professionals exhibited a negative attitude toward medical ethics, 57.72% showed a mildly positive attitude, whereas 1.10% and 0.74% indicated a moderately positive attitude and a highly positive attitude towards medical ethics. Similarly, the level of practice according to medical ethics is also very poor among young healthcare professionals. 34.56% of healthcare professionals deviated from medical ethics during their clinical practices, whereas 0.74% showed a good level of medical practice according to medical ethics. Conclusion: It is concluded in this research study that young healthcare professionals have adequate theoretical knowledge of medical ethics but are not properly trained to perform their clinical practices according to the guidelines of medical ethics. Furthermore, their professional attitude is poorly developed to maintain medical ethics during their clinical practices.

Keywords: knowledge, attitude, practices, medical ethics

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: Zeba Mahmood

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The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper.

Keywords: knowledge, knowledge management, knowledge discovery in databases, business, operational, information, data mining

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Authors: Fahad Hanash Alzahrani

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This paper describes some recent advances in a quickly developing area of data storing and processing based on Data Warehouses and Data Mining techniques, which are associated with software, hardware, data mining algorithms and visualisation techniques having common features for any specific problems and tasks of their implementation.

Keywords: data warehouse, data mining, knowledge discovery in databases, on-line analytical processing

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Authors: Kgabo H. Badimo, Sheryl Buckley

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Knowledge is increasingly recognised in this, the knowledge era, as a strategic resource, by public sector organisations, in view of the public sector reform initiatives. People and knowledge play a vital role in attaining improved organisational performance and high service quality. Many government departments in the public sector have started to realise the importance of knowledge management in streamlining their operations and processes. This study focused on knowledge management in the public healthcare service organisations, where the concept of service provider competitiveness pales to insignificance, considering the huge challenges emanating from the healthcare and public sector reforms. Many government departments are faced with challenges of improving organisational performance and service delivery, improving accountability, making informed decisions, capturing the knowledge of the aging workforce, and enhancing partnerships with stakeholders. The purpose of this paper is to examine the knowledge management practices of the Gauteng Department of Health in South Africa, in order to understand how knowledge management practices influence improvement in organisational performance and healthcare service delivery. This issue is explored through a review of literature on dominant views on knowledge management and healthcare service delivery, as well as results of interviews with, and questionnaire responses from, the general staff of the Gauteng Department of Health. Web-based questionnaires, face-to-face interviews and organisational documents were used to collect data. The data were analysed using both the quantitative and qualitative methods. The central question investigated was: To what extent can the conditions required for successful knowledge management be observed, in order to improve organisational performance and healthcare service delivery in the Gauteng Department of Health. The findings showed that the elements of knowledge management capabilities investigated in this study, namely knowledge creation, knowledge sharing and knowledge application, have a positive, significant relationship with all measures of organisational performance and healthcare service delivery. These findings thus indicate that by employing knowledge management principles, the Gauteng Department of Health could improve its ability to achieve its operational goals and objectives, and solve organisational and healthcare challenges, thereby improving organisational.

Keywords: knowledge management, Healthcare Service Delivery, public healthcare, public sector

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Authors: Yumeng Ma, Fang Wang, Jinxia Huang

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Researchers put forward higher requirements for efficient acquisition and utilization of domain knowledge in the big data era. As literature is an effective way for researchers to quickly and accurately understand the research situation in their field, the knowledge discovery based on literature has become a new research method. As a tool to organize and manage knowledge in a specific domain, the subject knowledge base can be used to mine and present the knowledge behind the literature to meet the users' personalized needs. This study designs the construction route of the subject knowledge base for specific research problems. Information extraction method based on knowledge engineering is adopted. Firstly, the subject knowledge model is built through the abstraction of the research elements. Then under the guidance of the knowledge model, extraction rules of knowledge points are compiled to analyze, extract and correlate entities, relations, and attributes in literature. Finally, a database platform based on this structured knowledge is developed that can provide a variety of services such as knowledge retrieval, knowledge browsing, knowledge q&a, and visualization correlation. Taking the construction practices in the field of activating blood circulation and removing stasis as an example, this study analyzes how to construct subject knowledge base based on literature knowledge extraction. As the system functional test shows, this subject knowledge base can realize the expected service scenarios such as a quick query of knowledge, related discovery of knowledge and literature, knowledge organization. As this study enables subject knowledge base to help researchers locate and acquire deep domain knowledge quickly and accurately, it provides a transformation mode of knowledge resource construction and personalized precision knowledge services in the data-intensive research environment.

Keywords: knowledge model, literature knowledge extraction, precision knowledge services, subject knowledge base

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Authors: Yousuf N. Al Khamisi, Eduardo M. Hernandez, Khurshid M. Khan

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Purpose: This paper presents a model of a Knowledge-Based (KB) using Lean Six Sigma (L6σ) principles to enhance the performance of healthcare leadership. Design/methodology/approach: Using L6σ principles to enhance healthcare leaders’ performance needs a pre-assessment of the healthcare organisation’s capabilities. The model will be developed using a rule-based approach of KB system. Thus, KB system embeds Gauging Absence of Pre-requisite (GAP) for benchmarking and Analytical Hierarchy Process (AHP) for prioritization. A comprehensive literature review will be covered for the main contents of the model with a typical output of GAP analysis and AHP. Findings: The proposed KB system benchmarks the current position of healthcare leadership with the ideal benchmark one (resulting from extensive evaluation by the KB/GAP/AHP system of international leadership concepts in healthcare environments). Research limitations/implications: Future work includes validating the implementation model in healthcare environments around the world. Originality/value: This paper presents a novel application of a hybrid KB combines of GAP and AHP methodology. It implements L6σ principles to enhance healthcare performance. This approach assists healthcare leaders’ decision making to reach performance improvement against a best practice benchmark.

Keywords: Lean Six Sigma (L6σ), Knowledge-Based System (KBS), healthcare leadership, Gauge Absence Prerequisites (GAP), Analytical Hierarchy Process (AHP)

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Galih Imaduddin

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Knowledge has been viewed as one of the most important resources in organizations, including those that operate in the healthcare sector. On that basis, Knowledge Management (KM) is crucial for healthcare organizations to improve their productivity and ensure effective utilization of their resources. Despite the growing interests to understand how KM might work for healthcare organizations, there is only a modest amount of empirical inquiries which have specifically focused on the tools and initiatives to share knowledge. Hence, the main purpose of this paper is to investigate the way healthcare organizations, particularly public sector ones, utilize knowledge sharing tools and initiatives for the benefit of patient-care. Employing a qualitative method, 13 (thirteen) Community Health Centers (CHCs) from a high-performing district health setting in Indonesia were observed. Data collection and analysis involved a repetition of document retrievals and interviews (n=41) with multidisciplinary health professionals who work in these CHCs. A single case study was cultivated reflecting on the means that were used to share knowledge, along with the factors that inhibited the exchange of knowledge among those health professionals. The study discovers that all of the thirteen CHCs exhibited and applied knowledge sharing means which included knowledge documents, virtual communication channels (i.e. emails and chatting applications), and social learning forums such as staff meetings, morning briefings, and communities of practices. However, the intensity of utilization was different among these CHCs, in which organizational culture, leadership, professional boundaries, and employees’ technological aptitude were presumed to be the factors that inhibit knowledge sharing processes. Making a distance with the KM literature of other sectors, this study denounces the primacy of technology-based tools, suggesting that socially-based initiatives could be more reliable for sharing knowledge. This suggestion is largely due to the nature of healthcare work which is still predominantly based on the tacit form of knowledge.

Keywords: knowledge management, knowledge sharing, knowledge sharing tools and initiatives, knowledge sharing inhibitors, primary health care organizations

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Authors: Saleh Alshehri

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Human skin detection is a vital step in many applications. Some of the applications are critical especially those related to security. This leverages the importance of a high-performance detection algorithm. To validate the accuracy of the algorithm, image databases are usually used. However, the suitability of these image databases is still questionable. It is suggested that the suitability can be measured mainly by the span the database covers of the color space. This research investigates the validity of three famous image databases.

Keywords: image databases, image processing, pattern recognition, neural networks

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Authors: Maryam Bakhtyar, Pegah Akrami

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Intuitional insight or mystical cognition is a different insight from common, concrete and intellectual insights. This kind of insight is not achieved by visionary contemplation but by the recitation of God, self-purification, and mystical life. In this insight, there is no distance or medium between the subject of cognition and its object, and they have a sort of unification, unison, and incorporation. As a result, knowledgeable consider this insight as direct, immediate, and personal. The goal of this insight is God, cosmos’ creatures, and the general inner and hidden aspect of the world that is nothing except God’s manifestations in the view of mystics. AS our common cognitions have diversity and stages, intuitional insight also has diversity and levels. As our senses are divided into concrete and rational, mystical discovery is divided into superficial discovery and spiritual one. Based on Islamic mystics, the preferable way to know God and believe in him is intuitional insight. There are two important criteria for evaluating mystical intuition, especially for beginner mystics of intellect and revelation. Indeed, the conclusion and a brief evaluation of Islamic mystics’ viewpoint is the main subject of this paper.

Keywords: intuition, discovery, mystical insight, personal knowledge, superficial discovery, spiritual discovery

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Authors: Wesam Darawsheh

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Background: there are scarcity of studies done to investigate the level of knowledge and the level of awareness and perception of Jordanians about occupational therapy (OT). Aim: to investigate the level of awareness of lay people, clients receiving services and healthcare professionals of OT, identify the common misconceptions about OT, and to explore ways whereby the knowledge and awareness about OT can be increased. Methodology: a cross sectional design was employed in this study where a survey was distributed in the Northern, Southern, Western, Eastern provinces and the Middle (capital city: Amman) province of Jordan. The survey consisted of eight section and 61 questions that aims to investigate the demographics of participants, self evaluation concerning knowledge and awareness about OT, sources of knowledge about OT, the perception of the aims, fields of practice, OT settings, misconceptions about OT, and suggestion to improve knowledge and awareness about OT. Results: A total of 829 participants were enrolled in this study: 459 lay people, 155 clients who are currently receiving OT services, 215 healthcare professionals. About 57% of the participants did not hear about OT, and 48% of those who reported to hear about OT did not have sufficient knowledge about it. There are several misconceptions associated with OT. The statistical analysis was executed using IBM SPSS software, Version 22.0 (SPSS, Chicago, USA). Conclusion: it is the responsibility of OTRs to increase the knowledge and awareness about OT in Jordan. This is required for the profession to proliferate and to be given its status.

Keywords: knowledge, occupational therapy misconceptions, healthcare professionals, lay people, Jordan

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Authors: Partha Saha, Uttam Kumar Banerjee

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Maternal and Child Healthcare (MCH) has always been regarded as one of the important issues globally. Reduction of maternal and child mortality rates and increase of healthcare service coverage were declared as one of the targets in Millennium Development Goals till 2015 and thereafter as an important component of the Sustainable Development Goals. Over the last decade, worldwide MCH indicators have improved but could not match the expected levels. Progress of both maternal and child mortality rates have been monitored by several researchers. Each of the studies has stated that only less than 26% of low-income and middle income countries (LMICs) were on track to achieve targets as prescribed by MDG4. Average worldwide annual rate of reduction of under-five mortality rate and maternal mortality rate were 2.2% and 1.9% as on 2011 respectively whereas rates should be minimum 4.4% and 5.5% annually to achieve targets. In spite of having proven healthcare interventions for both mothers and children, those could not be scaled up to the required volume due to fragmented health systems, especially in the developing and under-developed countries. In this research, a knowledge discovery based interactive Decision Support System (DSS) has been developed on web platform which would assist healthcare policy makers to develop evidence-based policies. To achieve desirable results in MCH, efficient resource planning is very much required. In maximum LMICs, resources are big constraint. Knowledge, generated through this system, would help healthcare managers to develop strategic resource planning for combatting with issues like huge inequity and less coverage in MCH. This system would help healthcare managers to accomplish following four tasks. Those are a) comprehending region wise conditions of variables related with MCH, b) identifying relationships within variables, c) segmenting regions based on variables status, and d) finding out segment wise key influential variables which have major impact on healthcare indicators. Whole system development process has been divided into three phases. Those were i) identifying contemporary issues related with MCH services and policy making; ii) development of the system; and iii) verification and validation of the system. More than 90 variables under three categories, such as a) educational, social, and economic parameters; b) MCH interventions; and c) health system building blocks have been included into this web-based DSS and five separate modules have been developed under the system. First module has been designed for analysing current healthcare scenario. Second module would help healthcare managers to understand correlations among variables. Third module would reveal frequently-occurring incidents along with different MCH interventions. Fourth module would segment regions based on previously mentioned three categories and in fifth module, segment-wise key influential interventions will be identified. India has been considered as case study area in this research. Data of 601 districts of India has been used for inspecting effectiveness of those developed modules. This system has been developed by importing different statistical and data mining techniques on Web platform. Policy makers would be able to generate different scenarios from the system before drawing any inference, aided by its interactive capability.

Keywords: maternal and child heathcare, decision support systems, data mining techniques, low and middle income countries

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Authors: Inas S. Khayal, Weiping Zhou, Jonathan Skinner

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Healthcare delivery systems around the world are in crisis. The need to improve health outcomes while decreasing healthcare costs have led to an imminent call to action to transform the healthcare delivery system. While Bioinformatics and Biomedical Engineering have primarily focused on biological level data and biomedical technology, there is clear evidence of the importance of the delivery of care on patient outcomes. Classic singular decomposition approaches from reductionist science are not capable of explaining complex systems. Approaches and methods from systems science and systems engineering are utilized to structure healthcare delivery system data. Specifically, systems architecture is used to develop a multi-scale and multi-dimensional characterization of the healthcare delivery system, defined here as the Healthcare Delivery System Knowledge Base. This paper is the first to contribute a new method of structuring and visualizing a multi-dimensional and multi-scale healthcare delivery system using systems architecture in order to better understand healthcare delivery.

Keywords: health informatics, systems thinking, systems architecture, healthcare delivery system, data analytics

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Authors: Sinead Impey, Damon Berry, Selma Furtado, Miriam Galvin, Loretto Grogan, Orla Hardiman, Lucy Hederman, Mark Heverin, Vincent Wade, Linda Douris, Declan O'Sullivan, Gaye Stephens

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Healthcare is a knowledge-rich environment. This knowledge, while valuable, is not always accessible outside the borders of individual clinics. This research aims to address part of this problem (at a study site) by constructing a maximal data set (knowledge artefact) for motor neurone disease (MND). This data set is proposed as an initial knowledge base for a concurrent project to develop an MND patient data platform. It represents the domain knowledge at the study site for the duration of the research (12 months). A knowledge elicitation method was also developed from the lessons learned during this process - the WICKED method. WICKED is an anagram of the words: eliciting and confirming data, information, knowledge, wisdom. But it is also a reference to the concept of wicked problems, which are complex and challenging, as is eliciting expert knowledge. The method was evaluated at a second site, and benefits and limitations were noted. Benefits include that the method provided a systematic way to manage data, information, knowledge and wisdom (DIKW) from various sources, including healthcare specialists and existing data sets. Limitations surrounded the time required and how the data set produced only represents DIKW known during the research period. Future work is underway to address these limitations.

Keywords: healthcare, knowledge acquisition, maximal data sets, action design science

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Authors: Heather Clark, Jon Kelly

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Burnout among healthcare professionals has been elevated to a high level of concern. The descriptions of the healthcare workplace often include language such as, stressful, long hours, rotating shifts, weekends and holidays, and exhausting. New graduate healthcare professionals are being sent into the workplace with little to no coping skills, knowledge of signs and symptoms of burnout, or resources that are available. The authors of this study created a university course entitled 'coping in your profession' that enrolled registered nurses, licensed practical nurses, EMTs, nurse assistants, and medical assistants. The course addresses burnout, self-analysis, incivility, coping mechanisms, and organizational responsibilities for employee well-being. The students were surveyed using QualtricsXM that included a pre-course and post-course analysis. Pre-course results showed high levels of individual experiences with burnout and limited knowledge of resources to combat burnout. Post-course results included personal growth and that students’ perception of burnout can be prevented at both the individual and the organization levels. Students also indicated that few to no resources to combat burnout existed at their place of employment. Addressing burnout at the educational level helps prepare graduates with the knowledge and tools to combat burnout at the individual and organization level.

Keywords: burnout, coping, healthcare workers, incivility, resilience

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Authors: Taiwo Hassanat Bawa-Muhammad, Opeoluwa Hope Adegoke

Abstract:

Introduction: This study aims to examine the knowledge and perceptions of preconception care among healthcare workers in Nigeria, recognizing its crucial role in ensuring safe pregnancies. Despite its significance, awareness of preconception care remains low in the country. The study seeks to assess the understanding of preconception services and identify the barriers that hinder their efficacy. Methods: Through semi-structured interviews, 129 healthcare workers across six states in Nigeria were interviewed between January and March 2023. The interviews explored the healthcare workers' knowledge of preconception care practices, the socio-cultural influences shaping decision-making, and the challenges that limit accessibility and utilization of preconception care services. Results: The findings reveal a limited knowledge of preconception care among healthcare workers, primarily due to inadequate information dissemination within the healthcare system. Additionally, cultural beliefs significantly influence perceptions surrounding preconception care. Furthermore, financial constraints, distance to healthcare facilities, and poor health infrastructure disproportionately restrict access to preconception services, particularly for vulnerable populations. The study also highlights insufficient skills and outdated training among healthcare workers regarding preconception guidance, primarily attributed to limited opportunities for professional development. Discussion: To improve preconception care in Nigeria, comprehensive education programs must be implemented, taking into account the societal influences that shape perceptions and behaviors. These programs should aim to dispel myths and promote evidence-based practices. Additionally, training healthcare workers and integrating preconception care services into primary care settings, with support from religious and community leaders, can help overcome barriers to access. Strategies should prioritize affordability while emphasizing the broader benefits of preconception care beyond fertility concerns alone. Lastly, widespread literacy campaigns utilizing trusted channels are crucial for effectively disseminating information and promoting the adoption of preconception practices in Nigeria.

Keywords: preconception care, knowledge, healthcare workers, Nigeria, barriers, education, training

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Authors: Saman Naqvi

Abstract:

Introduction: Telemedicine is the practise of providing remote healthcare to patients via the utilisation of communication technologies. Its application has become increasingly important since the Coronavirus Disease 2019 (COVID-19) pandemic. It is essential to determine the knowledge and attitudes of healthcare professionals concerning its use in order to maximise its application. Purpose: We aim to examine and evaluate the current understanding and perceptions of medical staff toward the use of telemedicine. Methods: In this cross-sectional study, we surveyed 1091 healthcare professionals worldwide. Following an extensive review of the literature, data were gathered using a questionnaire. To depict the participant profile, frequency, percentages, and cumulative percentages were determined. Results: The majority of respondents had either heard of (90.9%), seen (65.3%), or were familiar with (74.6%) how telemedicine is implemented in practice. 72.2% of people were familiar with the tools that could be applied to this technology. Those with a medical degree and experience of under five years were found to be more familiar with telemedicine. Additionally, opinions on providing healthcare remotely were largely favorable, with 80% of respondents stating that it reduced staff burden and 80.6% thinking that it eliminated unnecessary transportation costs. Furthermore, 83% expressed that it saves clinicians' time. However, 20% of participants believed telemedicine adds to staff workload and 40% of healthcare professionals felt it compromises patient privacy and information confidentiality. Conclusion: Despite being a new and developing practice in many countries, telemedicine appears to have a bright future. This is crucial during a pandemic as it provides effective healthcare while maintaining social isolation measures. Moreover, the majority of the participants in this study demonstrated a good understanding and a favorable attitude toward telemedicine.

Keywords: healthcare system, global survey, knowledge, attitude, covid 19, telemedicine

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Authors: Nadjat Zerf Boudjettou, Fahima Nader, Rachid Chalal

Abstract:

Knowledge management is to acquire and represent knowledge relevant to a domain, a task or a specific organization in order to facilitate access, reuse and evolution. This usually means building, maintaining and evolving an explicit representation of knowledge. The next step is to provide access to that knowledge, that is to say, the spread in order to enable effective use. Knowledge management in the medical field aims to improve the performance of the medical organization by allowing individuals in the care facility (doctors, nurses, paramedics, etc.) to capture, share and apply collective knowledge in order to make optimal decisions in real time. In this paper, we propose a knowledge management approach based on integration technique of heterogeneous data in the medical field by creating a data warehouse, a technique of extracting knowledge from medical data by choosing a technique of data mining, and finally an exploitation technique of that knowledge in a case-based reasoning system.

Keywords: data warehouse, data mining, knowledge discovery in database, KDD, medical knowledge management, Bayesian networks

Procedia PDF Downloads 371