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The increase of treatment quality offered by the healthcare organizations is one of the main challenges of the modern health informatics. The personalization of treatment presupposes the real-time adaptation of treatment schemes since the clinical status of the patient and circumstances inside a healthcare organization constantly change. In this pa...
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... Whereas the data-driven CP is designed and integrated with the existing electronic health records [11, 12, 13, and 14]. Ontology, probabilistic, and neural network techniques are adopted to enable efficient and adaptive service [15,16]. However, dealing with ontology-based CP summarization (or presentation) are challenging because of the complex nature of CP variation analysis, accepted standards, and hospital (or healthcare) characteristics. ...
Though a clinical pathway is one of the tools used to guide evidence-based healthcare, promoting the practice of evidence-based decisions on healthcare services is incredibly challenging in low resource settings (LRS). This paper proposed a novel approach for designing an automated and dynamic generation of clinical pathways (CPs) in LRS through a hybrid (knowledge-based and data-driven based) algorithm that works with limited clinical input and can be updated whenever new information is available. Our proposed approach dynamically maps and validate the knowledge-based clinical pathways with the local context and historical evidence to deliver a multi-criteria decision analysis (concordance table) for adjusting or readjusting the order of knowledge-based CPs decision priority. Our finding shows that the developed approach successfully delivered probabilistic-based CPs and found a promising result with Jimma Health Center “pregnancy, childbearing, and family planning” dataset.
... This relatively low level can be related to the nature of the discussed topics with public health aims in oppose to the medical papers that demands some kind of decision-support and rules defining, such as in diagnosing systems Mohammadhassanzadeh et al., 2017) or deciding on the most suitable treatment (Rung Ching Chen et al., 2012). There were topics in health management that were also related to supporting decisions such as deciding on the best care plan for a patient (Alexandrou, Xenikoudakis and Mentzas, 2008) or the best way to communicate in hospital workflows (Nelson and Sen, 2014). Pharmaceutical topics, like finding adverse events between drugs (Natsiavas et al., 2018), or discovering new drugs (McCusker et al., 2014) also relied on finding new knowledge from a series of already known information. ...
... An example of using defined SW rules in decision-based questions is the work of Alexandrou, Xenikoudakis and Mentzas (2008). The authors mentioned an example of one of the defined rules in their decision-based system for identifying the best treatment for each patient. ...
The semantic web (SW) offers tools for supporting data integration and sharing across disparate resources in the web. Meanwhile, health research needs an efficient approach for handling heterogenous data integration for the massive amounts of available health-related data to help discovering new scientific breakthroughs. In this thesis, the current and potential relationships between the semantic web and health research are aimed to be understood and identified through systematically reviewing the literature and examining the SW features in a proof-ofconcept health-related demonstrator. Firstly, a systematic literature review of 447 articles addressing health questions and using the SW standards was conducted to map the literature and identify any gaps or opportunities. The results of the review were analysed in a mixed approach of quantitative and qualitative methods producing two taxonomies: 1) the health aims and 2) the SW features taxonomies. The review revealed the most and least addressed health questions as well as the used SW features in the literature. Secondly, a semantic web-based demonstrator was developed to represent the NHS dispensed prescriptions topic and examine some of the identified SW features. The prescriptions demonstrator consists of three interlinked OWL ontologies: the BNF, NHS and prescriptions ontologies along with their converted RDF instances. Moreover, two health questions, inspired from the traditional health literature and suggested by health experts in a focus group, were translated into SPARQL queries and ran across the ontologies to test more of the SW features. It has been learned that the SW has a potential in supporting health research and accelarting research findings in the areas of: data representaion, data integration and knowledge discovery. However, there are some challenges need resolving for a better result such as: data accessibility, security, quality, heterogeneity and lack of user-friendly tools.
... The rule base can create new knowledge and update ontology accordingly. Reference [26], [27] established the integration of knowledge reasoning infrastructure stored in the software architecture through the SWRL rule base and the JESS expert system. The continuous reasoning generates new knowledge ontology, provides feedback to the system, and then updates the ontology. ...
Clinical pathway is a multi-disciplinary treatment plan and work mode, which is favorable for improving healthcare service quality and reducing medical costs. Most of references demonstrate that variance analysis and handling is the key to clinical pathway management. Thus, the clinical pathway variance has become the focus of scholars. This paper uses the text mining technique to present a literature review of 496 academic articles in the field of clinical pathway variance analysis and handling, which published between 1994 and 2018. Moreover, this paper conducts a bibliometric analysis to visualize the clinical pathway variance research. In variance analysis and handling, there are a lot of imprecise knowledge and fuzzy relations to be reasoned with knowledge of different domains. In this study, methods of clinical pathway variance analysis and handling are illustrated. In addition, this paper points out the limitations of each method. Based on the results, the future prospects of clinical pathway variance analysis and handling research is proposed.
... Components of CDSS are decision making model that analyzes. Organizes data and knowledge for data calculation, information model used in deduction, knowledge basis function, result of decision making model, host application and application setting that transmits, collects data having interaction with application [15][16][17][18][19][20]. Major function of CDSS performs decision making support function, cost control function, management function over clinical complexity and detailed level, management and administration function. ...
... Smoking, body weight index as examination factors that can be utilized according to diseases of chronic diseases to create management model for high risk of chronic diseases. This utilized decision making model based on rules of chronic diseases by CDSS [15][16][17][18][19][20]23,28,34] in PHR platform of prior studies. When decision standard of high blood pressure, diabetes, hyperlipidemia, chronic diseases, is analyzed in decision making model based on rules of CDSS, there is a high possibility that another chronic diseases can occur at the same time when one has only one disease among chronic diseases such as high blood pressure, diabetes, hyperlipidemia and we can see that diagnosis standard for each chronic disease is not very different [22]. ...
As IT convergence technique develops, medical technology and apparatus are being modernized opening the era that we can obtain variable information easily anywhere, anytime thanks to wireless communication developed, further. These social changes enabled us to obtain information related to health more efficiently. Modern society is rapidly aging and more people experience chronic diseases because of their wrong eating habit, obesity and insufficient exercise. Thus a demand for health improvement and management at a certain term is increasing rather than complete therapy. Previously, major medical institutions managed personal medical history regarding patients mainly in health management but it is not changing its method to self-utilization and management by individual patient as of now along with medical institutions as fusion technology develops, and individual health record information can easily be checked anywhere, anytime through personal health record (PHR) platform. Unlike developing speed of related technology, however, there is a limitation in expansion, development of individual health record service, personal information security currently. In this paper, we propose mobile service regarding life style improvement targeting high risk chronic diseases based on PHR platform. PHR platform determines high blood pressure, diabetes, hyperlipidemia diseases which are three main chronic diseases using users’ data and can monitor chronic diseases in portable mobile device. Also, the service provides by organically, mutually connected form through feedback towards input from health states of users in mobile device. By proposing contents about service based on efficient individual health record through mobile device that maximized transportability based on PHR platform, proposed method will contribute to industry development and activation of application service development of individual health record. Increase in consistency and reliability through standardization of afterwards health management service is expected to contribute to reduction in social cost and improvement of national health being the basis to realize communication activation of health record between medical institutions, efficient management and education of patients, reduction in dual examinations.
Decision support clinical pathways are used to improve the performance of the health-care management system. An effective clinical pathway (CP) helps to know the optimal treatment route that patients will follow. The extent of the CP goes from the first contact in the health-center (or hospital) to the completion of the treatment until the patient is dismissed. Up to now, far too little attention has been paid to a systematic review, the research and the development of CPs in a low resource setting (LRS). The main focus has been primarily on data-intensive environments where there is no shortage of resources. A systematic search in PubMed and Web of Science was conducted for bundling and categorizing the relevant approaches for LRS. Of 45 full reviewed articles, 25/45(55.6%) and 20/45(44.4%) of the studies were conducted using knowledge-based and data-driven approaches respectively. Among the knowledge-based studies, 9/25(36%) were reporting a stand-alone applications, 10/25(40%) attempting to deliver a paper-based CP, and the remaining focus was on web-based applications. In the data-driven approaches, 15/20(75%) tried to integrate with the electronic health record. The paper identifies the approaches for executing CPs and highlights key considerations for building LRS-compatible CPs. Data-driven CPs do not only resolve the challenges of improving the quality of existing knowledge-based CPs, but also enable evidence-based practice, improve outcomes, and reduce cost and delay.KeywordsLimited resource settingClinical decision support systemClinical workflowClinical pathwayPatient flowPoint of care service
In the medicine practice, due to the privacy and safety of electronic medical record (EMR), the sharing, research and application of EMR have been hindered to a certain extent. Thus, it becomes increasingly important to study semantic electronic medical data integration, so as to meet the needs of doctors and researchers and help them quickly access high-quality information. This paper focuses on the realization of semantic EMRs. It shows how to uses APDG (Advanced Patient Data Generator) to create a set of virtual patient data for depression. Furthermore, it explains how to develop clinical and semantic description rules to construct semantic EMRs for depression and discusses how those generated virtual patient data can be used in the system of Smart Ward for the test and demonstration, without violating the legal issues (e.g., privacy and security) of patient data.
To improve the diagnosis and prescription for military personnel, it is required to adopt Clinical Decision Support System (CDSS) in armed forces hospitals. The objective of this paper is to suggest a CDSS for armed forces hospitals using semantic web technologies. To this end, we designed military medical ontologies and military medical rules which consist of the various concepts and rules for supporting medical activities. We developed a semantic web-based CDSS to demonstrate the use of the ontologies and rules for treating military patients. We also showed the process of semantic search for the medical records which are created from the semantic web-based CDSS.
Efficient healthcare management has increasingly drawn much attention in healthcare sector along with recent advances in IT convergence technology. Population aging and a shift from an acute to a chronic disease with a long duration of illness have urgently necessitated healthcare service for efficient, systematic health management. Clinical decision support system (CDSS) is an integrated healthcare system that effectively guides health management and promotion, recommendation for regular health check-up, tailor-made diet therapy, health behavior change for self-care, alert service for drug interaction in patients with chronic diseases with a high prevalence. Although CDSS rule-based algorithm aids guidelines and decision making according to a single chronic disease, it is unable to inform unique characteristics of each chronic disease and suggest preventive strategies and guidelines of complex diseases. Therefore, this study proposes evolutionary rule decision making using similarity based associative chronic disease patients to normalize clinical conditions by utilizing information of each patient and recommend guidelines corresponding detailed conditions in CDSS rule-based inference. Decision making guidelines of chronic disease patients could be systematically established according to various environmental conditions using database of patients with different chronic diseases.
In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.
Patients with chronic diseases need continuous healthcare management along with a treatment-based healthcare service and an extended decision-making support model that provides self-management guidelines and a checkup schedule based on the patients' behavior. Thus, in order to facilitate improvement in patients' health, the present study proposes a Healthcare Decision Support System (HDSS)-based PEI model in which personalized, rule-evolution, and intelligent concepts are mounted. The proposed PEI model provides personalized monitoring and guidelines through the Dynamic Healthgraphics service using Infographics, which is a data visualization technique to view the examination results and health status of patients at a glance.
