• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.279-280
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• 2014

정보통신기술의 발달로 말미암아 병의원에서도 다양한 정보시스템의 도입이 활발하고, 초기에는 데이터의 전자적 관리 및 공유를 위한 시스템이 주를 이루었으나 점차 병의원 운영관리에 대한 직접적인 의사결정지원 기능이 강조되고 있다. 그러나 기존의 시스템들은 대부분 의료 전문가들의 지식에 기반하여 진료행위가 정해진 절차를 벗어나지 않도록 하는 데에만 초점을 맞추었고, 환자나 경영자 입장을 충분히 고려하지 못하였다. 이에 본 논문에서는 전문적 의료 지식 베이스가 아닌 병의원에서 수집된 데이터를 기반으로 다양한 참여자들에게 유용한 기능을 제공하기 위한 능동형 임상의사결정지원시스템의 개념과 구조에 대하여 논의하고자 한다.

• Journal of Information Technology Applications and Management
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• v.21 no.2
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• pp.81-98
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• 2014

Recently, due to external environment like the changes in health policy and various healthcare accreditations, along with hospital's internal efforts to improve the quality of medical services, demands for the development of medical information systems are increasing. Some examples are clinical information like DUR (Drug Utilization Review), CVR (Critical Value Report), and automatic benefit processing by treatment purposes, or hospital DSS (Decision Support System) on overall medical practice. Such systems act as a guide in making clinic judgments during practice or in other medical practice, and their effects on the medical treatment improvements are being proven by previous studies. In the reality of increasing attention in the effects of medical treatment improvement, studies related to hospital DDS were mostly focused on clinical, technical, and engineering points of view, and studies focusing on the user viewpoint are very limited. In order to verify the effects of DSS on practice improvements and hospital's management performance, this study used a research model constructed to verify how SERVQUAL of hospital DSS affects hospital management performance in BSC (Balanced Score Card) point of view. To empirically verify the research model, a questionnaire was conducted on the basis of "K-University Hospital's DSS" on clinicians and hospital employees related to system development, and the relationships between the factors were analyzed through path analysis. As a result of path analysis, excluding reactivity, tangibility, confidence, reliability, empathy among service qualities, had partially significant effects on management performance factors (learning and growth, internal process, financial affairs). This study is to prepare the theoretical ground on the management performance analysis of hospital DSS, and suggest the service quality of the system that should be considered in the planning and development stages for improved system.

• Clinical Endoscopy
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• v.56 no.1
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• pp.14-22
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• 2023

Barrett's esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett's esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett's esophagus and elaborate on potential artificial intelligence in the future.

• Journal of Physiology & Pathology in Korean Medicine
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• v.28 no.1
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• pp.113-121
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• 2014

With the development of information technology, knowledge information-oriented and information systems are being rapidly paced. In addition, doctor's needs of the system that assist decision making is gradually increasing. Because the complex process of decision-making should be a lot. We propose a web based system for supporting medical treatment based on Korean medicine ontology. There are three kinds of processes. First, a pattern is decided for patient' symptoms, a formula for the pattern is selected and medicinal materials constituting the formula is added or removed. Second, a formula is decided for patient' symptoms, medicinal materials constituting the formula is added or removed. Third, a Treat method is decided for patient' symptoms, medicinal materials constituting the formula is added or removed. We have designed and implemented the clinical decision support system that supports flexible processes and necessary information and functions. The system shows the appropriate form of ontology knowledge as interrelated and provide analysis and processing, does not show simply search. The system is one of the systems utilizing ontology and a web based system that can be used in anywhere. Therefore, This system Will be useful as for doctors to make decision.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.450-451
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• 2012

The need of Clinical Decision Support System (CDSS) in healthcare setup is increasing day by day. EHR Meaningful Use advocates CDSS as an important component of EHR/EMR systems. CDSS can be ranged from a simple to a very sophisticated system. The more complex CDSS systems need more attention to develop because of many reasons including its Knowledge Base (KB) structure/maintenance/evolution, inference capabilities and usability. Above all the KB maintenance and evolution is very crucial and important from the perspective of useful decision capabilities. Also the richness of the KB is important to cover the decision gaps handling a particular situation in the course of patient care. It cannot be expected from the clinicians to remember everything in regard to patient diagnosis and treatment. Similarly, it is also crucial for clinicians to keep themselves updated with the new research in the area. That is the reason they frequently require accessing to the online knowledge resources. Literature proved that online knowledge resources are capable providing answers to questions that might not be answered rely only on clinician wisdom and experience. This paper provides the theme of meaningful utilization of online knowledge resources in the context of diagnosis and treatment process for cancer patients more specifically Head and Neck cancer.

• Journal of Information Processing Systems
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• v.8 no.1
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• pp.67-84
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• 2012

An important amount of clinical data concerning the medical history of a patient is in the form of clinical reports that are written by doctors. They describe patients, their pathologies, their personal and medical histories, findings made during interviews or during procedures, and so forth. They represent a source of precious information that can be used in several applications such as research information to diagnose new patients, epidemiological studies, decision support, statistical analysis, and data mining. But this information is difficult to access, as it is often in unstructured text form. To make access to patient data easy, our research aims to develop a system for extracting information from unstructured text. In a previous work, a rule-based approach is applied to a clinical reports corpus of infectious diseases to extract structured data in the form of named entities and properties. In this paper, we propose the use of a Boolean inference engine, which is based on a cellular automaton, to do extraction. Our motivation to adopt this Boolean modeling approach is twofold: first optimize storage, and second reduce the response time of the entities extraction.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.722-729
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• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• Information Systems Review
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• v.15 no.2
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• pp.59-73
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• 2013

Healthcare costs are continuously increasing due to longer life expectancy and providing global healthcare services through medical tourism is new service growth engine for Korea. Several countries have well established programs and infrastructure dedicated to medical tourism. South Korea is attempting to become a major player in this domain by undertaking broad initiatives. The success of medical tourism is greatly impacted by easy access to two types of information, namely, medical and travel information. The National Health Insurance System in Korea collects huge amount of clinical and financial information from all hospitals. However, this information does not get used effectively in health and travel information systems to support medical tourism. This paper provide clear process map of medical tourism to understand how the patient and information process both medical and tourism fields also describe the need of customer and service provider. In this paper, we develop a medical tourism service system that will promote information exchange and service delivery.

• Perspectives in Nursing Science
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• v.10 no.2
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• pp.158-166
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• 2013

Purpose: This study was conducted to investigate patient safety regulations and guidelines in order to understand their current status, and to examine support measures to improve patient safety in Korean hospitals. Methods: The participants were the safety officers from hospitals with 200 or more beds and 112 hospitals responded to the online survey. The questions covered patient safety regulations, the performance level of patient safety activities, patient safety incident reporting systems, the dedicated professional, training, support mechanisms, and expectations of reporting systems. Results: Among preventative measures, fall prevention and hand hygiene were reported to be most widely practiced (92% and 91%, respectively). Time-out for invasive procedures showed a relatively low practice rate at 70%. Among patient care activities, transfusion, surgery and sedation, medication, and infection management were performed by 84, 74, 93 and 93% of the hospitals, respectively. Patient safety activities included patient safety committee, patient safety cooperation between decision-making bodies, patient safety workshops, seminars, lectures, and training for employees. Conclusion: Patient safety regulations and guidelines have not yet been sufficiently prepared, and a public institution such as a certification authority is of crucial importance to enforce these guidelines.

• BMB Reports
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• v.43 no.10
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• pp.643-648
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• 2010

Sir William Osler (1849-1919) recognized that "variability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions we know as disease". Accordingly, the traditional methods of medicine are not always best for all patients. Over the last decade, the study of genomes and their derivatives (RNA, protein and metabolite) has rapidly advanced to the point that genomic research now serves as the basis for many medical decisions and public health initiatives. Genomic tools such as sequence variation, transcription and, more recently, personal genome sequencing enable the precise prediction and treatment of disease. At present, DNA-based risk assessment for common complex diseases, application of molecular signatures for cancer diagnosis and prognosis, genome-guided therapy, and dose selection of therapeutic drugs are the important issues in personalized medicine. In order to make personalized medicine effective, these genomic techniques must be standardized and integrated into health systems and clinical workflow. In addition, full application of personalized or genomic medicine requires dramatic changes in regulatory and reimbursement policies as well as legislative protection related to privacy. This review aims to provide a general overview of these topics in the field of personalized medicine.