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Key summary points: Aim: The aim of our study was to assess barriers and facilitators to CDSS use reported by European physicians treating older fallers and explore differences in their perceptions. Findings: Our main findings were that a barrier to CDSS use is that physicians feel that complex geriatric patients need a physician's clinical judgement and not the advice of a CDSS. Regional differences in barrier and facilitator perceptions occurred across Europe. Message: Our main message is that when designing a CDSS for Geriatric falls patients, the patient's medical complexity must be addressed whilst maintaining the doctor's decision-making autonomy, and to increase successful CDSS implementation in Europe, regional differences in barrier perception should be overcome. Purpose: Fall-Risk Increasing Drugs (FRIDs) are an important and modifiable fall-risk factor. A Clinical Decision Support System (CDSS) could support doctors in optimal FRIDs deprescribing. Understanding barriers and facilitators is important for a successful implementation of any CDSS. We conducted a European survey to assess barriers and facilitators to CDSS use and explored differences in their perceptions. Methods: We examined and compared the relative importance and the occurrence of regional differences of a literature-based list of barriers and facilitators for CDSS usage among physicians treating older fallers from 11 European countries. Results: We surveyed 581 physicians (mean age 44.9 years, 64.5% female, 71.3% geriatricians). The main barriers were technical issues (66%) and indicating a reason before overriding an alert (58%). The main facilitators were a CDSS that is beneficial for patient care (68%) and easy-to-use (64%). We identified regional differences, e.g., expense and legal issues were barriers for significantly more Eastern-European physicians compared to other regions, while training was selected less often as a facilitator by West-European physicians. Some physicians believed that due to the medical complexity of their patients, their own clinical judgement is better than advice from the CDSS. Conclusion: When designing a CDSS for Geriatric Medicine, the patient's medical complexity must be addressed whilst maintaining the doctor's decision-making autonomy. For a successful CDSS implementation in Europe, regional differences in barrier perception should be overcome. Equipping a CDSS with prediction models has the potential to provide individualized recommendations for deprescribing FRIDs in older falls patients. [ABSTRACT FROM AUTHOR]

Experiential learning is critical in preparing health informatics students for the workplace. Although health informatics has evolved to developing information systems that optimize patient care and patient engagement, an understanding of the EHR system is essential for students. A hands-on component in performing tasks using the CDSS in the EHR system was designed to assess the experiential learning gained by students. Pre- and post-test questions to assess students' understanding of the CDSS were administered. Additionally, at post-test, students responded to questions on student engagement, student learning, and student interest in extended learning. Students' understanding of the CDSS significantly increased at post-test (p < .01). Open-ended responses confirm their improved understanding of CDSS concepts and functionality. Students found the hands-on CDSS component to be engaging. Also, students believed the hands-on component contributed to learning as well as an interest in learning more about the CDSS. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study is to discuss and analyze the use of automated SNOMED CT clinical coding in clinical decision support systems (CDSSs) for preventive care. The central question that this study seeks to answer is whether the utilization of SNOMED CT in CDSSs can improve preventive care. Methods: PubMed, Google Scholar, and Cochrane Library were searched for articles published in English between 2001 and 2012 on SNOMED CT, CDSS, and preventive care. Outcome Measures: Outcome measures were the sensitivity or specificity of SNOMED CT coded data and the positive predictive value or negative predictive value of SNOMED CT coded data. Additionally, we documented the publication year, research question, study design, results, and conclusions of these studies. Results: The reviewed studies suggested that SNOMED CT successfully represents clinical terms and negated clinical terms. Conclusions: The use of SNOMED CT in CDSS can be considered to provide an answer to the problem of medical errors as well as for preventive care in general. Enhancement of the modifiers and synonyms found in SNOMED CT will be necessary to improve the expected outcome of the integration of SNOMED CT with CDSS. Moreover, the application of the tree-augmented naïve (TAN) Bayesian network method can be considered the best technique to search SNOMED CT data and, consequently, to help improve preventive health services. [ABSTRACT FROM AUTHOR]

Knowledge-Intensive Case Based Reasoning (KI-CBR) systems mainly depend on ontology. Using ontology as domain knowledge supports the implementation of semantically-intelligent case retrieval algorithms. The case-based knowledge must be encoded with the same concepts of the domain ontology. Standard medical ontologies, such as SNOMED CT (SCT), can play the role of domain ontology to enhance case representation and retrieval. This study has three stages. First, we propose an encoding methodology using SCT. Second, this methodology is used to encode the case-based knowledge. Third, all the used SCT concepts are collected in a reference set, and an OWL2 ontology of 550 pre-coordinated concepts is proposed. A diabetes diagnosis is chosen as a case study of our proposed framework. SCT is used to provide a pre-coordination concept coverage of ∼75% for diabetes diagnosis terms. Whereas, the uncovered concepts in SCT are proposed. The resulting OWL2 ontology will be used as domain knowledge representation in diabetes diagnosis CBR systems. The proposed framework is tested by using 60 real cases. [ABSTRACT FROM AUTHOR]

Supervised classification algorithms have become very popular because of their potential application in developing intelligent data analytic software. These algorithms are known to be sensitive to the characteristic and structure of input datasets, therefore, researchers use different algorithm selection methods to select the most suitable classification algorithm for specific dataset. These methods do not consider the uncertainty about input dataset, and relative importance of different performance measurements (such as speed, accuracy, and memory usage) in the target application domain. Therefore, these methods are not appropriate for software development. This is especially true in medical field where various high dimensional noisy data might be used with the software. Hence, software developers need to select one supervised classification algorithm that has the highest potential to provide good performance in wide variety of datasets. In this regard, an Analytic Hierarchy Process (AHP) based meta-learning algorithm is proposed to identify the most suitable supervised classification algorithm for developing clinical decision support system (CDSS). The results from ten publicly available medical datasets indicate that Support Vector Machine (SVM) has the highest potential to perform well on variety of medical datasets. [ABSTRACT FROM AUTHOR]

Purpose: The influence of dietary composition on blood pressure is an important subject in healthcare. Interactions between antihypertensive drugs and diet (IBADD) is the most important factor in the management of hypertension. It is therefore essential to support healthcare providers’ decision making role in active and continuous interaction control in hypertension management. The aim of this study was to implement an ontology-based clinical decision support system (CDSS) for IBADD management (IBADDM). We considered the concepts of antihypertensive drugs and foods, and focused on the interchangeability between the database and the CDSS when providing tailored information. Methods: An ontology-based CDSS for IBADDM was implemented in eight phases: (1) determining the domain and scope of ontology, (2) reviewing existing ontology, (3) extracting and defining the concepts, (4) assigning relationships between concepts, (5) creating a conceptual map with CmapTools, (6) selecting upper ontology, (7) formally representing the ontology with Protégé (ver.4.3), (8) implementing an ontology-based CDSS as a JAVA prototype application. Results: We extracted 5,926 concepts, 15 properties, and formally represented them using Protégé. An ontology-based CDSS for IBADDM was implemented and the evaluation score was 4.60 out of 5. Conclusion: We endeavored to map functions of a CDSS and implement an ontology-based CDSS for IBADDM. [ABSTRACT FROM AUTHOR]

Abstract: The primary aim of this work was the development of a uniform, contextualized and sustainable knowledge-based framework to support adverse drug event (ADE) prevention via Clinical Decision Support Systems (CDSSs). In this regard, the employed methodology involved first the systematic analysis and formalization of the knowledge sources elaborated in the scope of this work, through which an application-specific knowledge model has been defined. The entire framework architecture has been then specified and implemented by adopting Computer Interpretable Guidelines (CIGs) as the knowledge engineering formalism for its construction. The framework integrates diverse and dynamic knowledge sources in the form of rule-based ADE signals, all under a uniform Knowledge Base (KB) structure, according to the defined knowledge model. Equally important, it employs the means to contextualize the encapsulated knowledge, in order to provide appropriate support considering the specific local environment (hospital, medical department, language, etc.), as well as the mechanisms for knowledge querying, inference, sharing, and management. In this paper, we present thoroughly the establishment of the proposed knowledge framework by presenting the employed methodology and the results obtained as regards implementation, performance and validation aspects that highlight its applicability and virtue in medication safety. [Copyright &y& Elsevier]

Abstract: As people have interests in their health recently, development of medical domain application has been one of the most active research areas. One example of the medical domain application is the detection system for heart disease based on computer-aided diagnosis methods, where the data are obtained from some other sources and are evaluated based on computer-based applications. Earlier, the use of computer was to build a knowledge based clinical decision support system which uses knowledge from medical experts and transfers this knowledge into computer algorithms manually. This process is time consuming and really depends on medical experts’ opinions which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining knowledge from the patient’s clinical data. The proposed clinical decision support system for the risk prediction of heart patients consists of two phases: (1) automated approach for the generation of weighted fuzzy rules and (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity. [Copyright &y& Elsevier]