OBJECTIVE: For the development of interoperable and sharable knowledge-based clinical decision support systems, it is important to evaluate the appropriateness of knowledge in each phase. In this study, an evaluation of early phase's knowledge model for hypertension management was conducted to develop a more precise and useful knowledge model. METHODS: The knowledge model for hypertension management based on JNC7 was modeled using a knowledge representation tool based on SAGE. Two physicians were involved in evaluating the process of the knowledge model. They reviewed 36 scenarios and made recommendations based on the knowledge model. These recommendations were compared with those derived from the model. RESULTS: Eight algorithms and 223 evidence statements were included in the knowledge model. The concordance rate of the recommendations between the physicians and the model for the goal BP were 61% and 93% by the respective physicians. Six scenarios showed low proficiency and efficiency for drug recommendation. Two refinements of the knowledge model were made based on the results. CONCLUSION: The evaluation process of the knowledge model in the early phase provides more precise and useful knowledge model in the next.
Decision Support Systems, Clinical ; Hypertension

This paper proposes a clinical alarm management system based on the theory of the closed loop control. The alarm management mechanism can be divided into the expected standard, improving execution rule, rule execution, medical devices with alarm functions, results analysis strategy and the output link. And, we make relevant application and discussion. Results showed that the mechanism can be operable and effective.
Clinical Alarms ; Decision Support Systems, Clinical

The purpose of the article is to describe the various radiology consultation models in the Era of Precision Medicine. Since the inception of our specialty, radiologists have served as consultants to physicians of various disciplines. A variety of radiology consultation services have been described in the literature, including clinical decision support, patient-centric, subspecialty interpretation, and/or some combination of these. In oncology care in particular, case complexity often merits open dialogue with clinical providers. To explore the utility and impact of radiology consultation services in the academic setting, this article will further describe existing consultation models and the circumstances that precipitated their development. The hybrid model successful at our tertiary cancer center is discussed. In addition, the contributions of a consultant radiologist in breast cancer care are reviewed as the archetype of radiology consultation services provided to oncology practitioners.
Breast Neoplasms ; Consultants ; Decision Support Systems, Clinical ; Humans ; Precision Medicine

Decision Support Systems, Clinical ; Humans ; Medicine, Chinese Traditional

Although guidelines and formulas have been developed through clinical practice to define infusion rate and volume, over- and under-resuscitation are still common, followed by increasing morbidity and mortality. In order to establish an effective management for early fluid resuscitation, the clinical decision support system (CDSS) has been established. The CDSS, by utilizing information systems coupled with decision support technology, could provide recommendations for the amount of fluid to be infused based on measured biological response. The results showed that patients treated with CDSS had a significantly lower mortality, increased ventilator-free days, and ICU-free days as compared with those treated with traditional fluid management. This article reviews the concepts as well as the result of recent clinical studies of CDSS for burn patients.
Burns ; therapy ; Decision Support Systems, Clinical ; Fluid Therapy ; Humans

As an methodology for promoting the quality and efficiency of health care, clinical decision support systems (CDSSs) have gained much improvement. The knowledge base (KB) plays an important role in DSS. For CDSSs, the construction of KB means modeling the medical knowledge based on a suitable model. This study analyzes the SAGE model, then implements it on knowledge of diagnosis and treatment of Metabolic Syndrome (MS), and improves the SAGE to enhance its expression ability. The model is constructed as the KB in CDSS, and be applied in hospital. The evaluation result of CDSS reveals that the SAGE model should be useful in clinical application. Finally, this study propounds some points yet to be improved in the SAGE.
Decision Support Systems, Clinical ; Knowledge Bases ; Models, Theoretical ; Research Design

Humans ; Decision Support Systems, Clinical ; Rare Diseases/therapy*

Clinical decision support systems are interactive computer systems for situational decision making and can improve decision efficiency and safety of care. We investigated the role of these systems in enhancing prehospital care. This narrative review included full-text articles published since 2000 that were available in databases/e-journals including Web of Science, PubMed, Science Direct, and Google Scholar. Search keywords included “clinical decision support system,” “decision support system,” “decision support tools,” “prehospital care,” and “emergency medical services.” Non-journal articles were excluded. We revealed 14 relevant studies that used such a support system in prehospital emergency medical service. Owing to the dynamic nature of emergency situations, decision timing is critical. Four key factors demonstrated the ability of clinical decision support systems to improve decision-making, reduce errors, and improve the safety of prehospital emergency activity: computer-based, offer support as a natural part of the workflow, provide decision support in the time and place of decision making, and offer practical advice. The use of clinical decision support systems in prehospital care resulted in accurate diagnoses, improved patient triage and patient outcomes, and reduction of prehospital time. By improving emergency management and rescue operations, the quality of prehospital care will be enhanced.
Computer Systems ; Decision Making ; Decision Support Systems, Clinical ; Diagnosis ; Emergencies ; Emergency Medical Services ; Humans ; Triage

Artificial Intelligence (AI) to support the medical decision-making process has long been both an interest and concern of physicians and the public. However, the introduction of open source software, supercomputers, and a variety of industry innovations has accelerated the progress of the development of AI in clinical decision support systems. This article summarizes the current trends and challenges in the medical field, and presents how AI can improve healthcare systems by increasing efficiency and decreasing costs. At the same time, it emphasizes the centrality of the role of physicians in utilizing AI as a tool to supplement their decisions as they provide patient-oriented care.
Artificial Intelligence* ; Clinical Decision-Making ; Decision Support Systems, Clinical ; Delivery of Health Care

BACKGROUNDExisting hospital information systems with simple statistical functions cannot meet current management needs. It is well known that hospital resources are distributed with private property rights among hospitals, such as in the case of the regional coordination of medical services. In this study, to integrate and make full use of medical data effectively, we propose a data warehouse modeling method for the hospital information system. The method can also be employed for a distributed-hospital medical service system.

METHODSTo ensure that hospital information supports the diverse needs of health care, the framework of the hospital information system has three layers: datacenter layer, system-function layer, and user-interface layer. This paper discusses the role of a data warehouse management system in handling hospital information from the establishment of the data theme to the design of a data model to the establishment of a data warehouse. Online analytical processing tools assist user-friendly multidimensional analysis from a number of different angles to extract the required data and information.

RESULTSUse of the data warehouse improves online analytical processing and mitigates deficiencies in the decision support system. The hospital information system based on a data warehouse effectively employs statistical analysis and data mining technology to handle massive quantities of historical data, and summarizes from clinical and hospital information for decision making.

CONCLUSIONSThis paper proposes the use of a data warehouse for a hospital information system, specifically a data warehouse for the theme of hospital information to determine latitude, modeling and so on. The processing of patient information is given as an example that demonstrates the usefulness of this method in the case of hospital information management. Data warehouse technology is an evolving technology, and more and more decision support information extracted by data mining and with decision-making technology is required for further research.