We construct networks from a prescription database (PD) in a long-term care facility and detect communities in these networks (PD networks) . The nodes of the networks consist of the field’s data of the PD such as a patient ID, a drug name and a doctor’s ID. A community is a sub networks in which nodes are densely connected internally. Because a community in the PDN involves patient ID nodes, the community fixes the group of patients. We can detect communities in a PD network using two fields: “patient” and “drug” as nodes, but we cannot detect them when using all the fields. By evaluating the drugs in the communities, we can clinically characterize the communities. Next, we define a Network Similarity Index (NSI) to measure similarity between communities. Using this index, we find that there exist many communities in the network which have not changed significantly for a long time. Moreover, we find that the patient groups obtained from community detection are different from those from a cluster analysis. These findings will propose another method to analyze the clinical data.

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　At Community pharmacies, many of the medical errors are mistakes at the time of picking medicines. Project Collect Medical Near-miss/Adverse Event Information also reports that 25.3 percent of all the medical errors are the stakes at the time of picking medicines. Based on input data of medical prescriptions, our pharmacy developed and introduced a system for checking information on Personal Digital Assistant (below regarded as PDA) in 2004, and achieved an effect to reduce mistakes. In the view of costs, however, the system has not yet been introduced to all of pharmacy. Therefore, so that even relatively small pharmacies can introduce the system easily, the system was transplanted into smart devices, and evaluated. For reading of barcodes, an attached camera in the smart devices was used. The result said that the percentage of mistakes at the time of picking medicines for the transplanted system is not erent from mistakes for the existed PDA system, and the transplanted system can also be operated without problems in the view of safe. It is thought that this idea could reduce costs of introducing picking systems, and lower barrier of introduction that many pharmacies have.

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　In recent years, as the social environment surrounding healthcare has been more demanding, it is more important than ever for multiple healthcare facilities to cooperate effectively using ICT. One outstanding solution includes outsourcing of imaging diagnosis such as CT or MRI, which may be proliferated because of its convenience and scalability. However, when it comes to cardiac imaging, it is extremely important to prepare for the unexpected cardiac risk of subjects. It may be of great importance not only for the efficiency and reliability of transfer but also for improvement of clinical outcome to share the imaging results instantly between facilities. To solve this potential problem, we newly developed an image sharing system for acute medicine (ISSAM) utilizing mobile devices. It consists of Linux-based server application for file sharing with authentication and delivery by HTTP over SSL and imaging server of various formats such as PDF, jpeg, and DICOM, together with virtual private network (VPN). Our system enables medical staffs to share medical imaging in PC browser as well as in mobile devices. Its central functions were tested and feasibility was confirmed. We are launching clinical trials to compare this system with conventional way in the cardiac imaging clinic in terms of stratification for acute cardiovascular risk.

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　Minds website has been continuously providing medical information on the Internet with a focus on clinical practice guidelines since 2002. This study was conducted with the aim of revealing future challenges of Minds website by analyzing results of a questionnaire survey regarding user satisfaction. Survey method was online self-administered questionnaire. Study population was users registered at the Minds website. The number of responders to the questionnaire was 2,940. Response rate was 8.2%. With regards to the question “if the purpose of usage was achieved”, the rates of positive answers were high in medical students for “study or knowledge acquisition purpose (23 persons, 53.5%)” and in health-care providers for “medical examination purpose (82 persons, 50.0%)”. Those whose purpose of usage was satisfied had used many kinds of contents and the answer rate of “contents were helpful” was high. We would like to handle the challenges for Minds website that were revealed by this study by addressing the issues and making improvements in the system and its operation.

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