OBJECTIVES: Health Avatar Beans was for the management of chronic kidney disease and end-stage renal disease (ESRD). This article is about the DialysisNet system in Health Avatar Beans for the seamless management of ESRD based on the personal health record. METHODS: For hemodialysis data modeling, we identified common data elements for hemodialysis information (CDEHI). We used ASTM continuity of care record (CCR) and ISO/IEC 11179 for the compliance method with a standard model for the CDEHI. According to the contents of the ASTM CCR, we mapped the CDHEI to the contents and created the metadata from that. It was transformed and parsed into the database and verified according to the ASTM CCR/XML schema definition (XSD). DialysisNet was created as an iPad application. The contents of the CDEHI were categorized for effective management. For the evaluation of information transfer, we used CarePlatform, which was developed for data access. The metadata of CDEHI in DialysisNet was exchanged by the CarePlatform with semantic interoperability. RESULTS: The CDEHI was separated into a content list for individual patient data, a contents list for hemodialysis center data, consultation and transfer form, and clinical decision support data. After matching to the CCR, the CDEHI was transformed to metadata, and it was transformed to XML and proven according to the ASTM CCR/XSD. DialysisNet has specific consideration of visualization, graphics, images, statistics, and database. CONCLUSIONS: We created the DialysisNet application, which can integrate and manage data sources for hemodialysis information based on CCR standards.
Chronic Disease ; Compliance ; Continuity of Patient Care* ; Fabaceae ; Health Information Management ; Health Records, Personal ; Humans ; Information Storage and Retrieval* ; Kidney Failure, Chronic ; Renal Dialysis* ; Renal Insufficiency, Chronic ; Semantics

The recognition of emotional facial expressions is critical for our social interactions. While some prior studies have shown that a high anxiety level is associated with more sensitive recognition of emotion, there are also reports supporting that anxiety did not affect or reduce the sensitivity to the recognition of facial emotions. To reconcile these results, here we investigated whether the effect of individual anxiety on the recognition of facial emotions is dependent on the emotion category and the race of the target faces. We found that, first, there was a significant positive correlation between the individual anxiety level and the recognition sensitivity for angry faces but not for sad or happy faces. Second, while the correlation was significant for both low- and high-intensity angry faces during the recognition of the observer's own-race faces, there was significant correlation only for low-intensity angry faces during the recognition of other-race faces. Collectively, our results suggest that the influence of anxiety on the recognition of facial emotions is flexible depending on the characteristics of the target face stimuli including emotion category and race.
Anxiety ; Continental Population Groups ; Facial Expression ; Humans ; Interpersonal Relations

Around the world electronic health records data are being shared and exchanged between two different systems for direct patient care, as well as for research, reimbursement, quality assurance, epidemiology, public health, and policy development. It is important to communicate the semantic meaning of the clinical data when exchanging electronic health records data. In order to achieve semantic interoperability of clinical data, it is important not only to specify clinical entries and documents and the structure of data in electronic health records, but also to use clinical terminology to describe clinical data. There are three types of clinical terminology: interface terminology to support a user-friendly structured data entry; reference terminology to store, retrieve, and analyze clinical data; and classification to aggregate clinical data for secondary use. In order to use electronic health records data in an efficient way, healthcare providers first need to record clinical content using a systematic and controlled interface terminology, then clinical content needs to be stored with reference terminology in a clinical data repository or data warehouse, and finally, the clinical content can be converted into a classification for reimbursement and statistical reporting. For electronic health records data collected at the point of care to be used for secondary purposes, it is necessary to map reference terminology with interface terminology and classification. It is necessary to adopt clinical terminology in electronic health records systems to ensure a high level of semantic interoperability.
Dietary Sucrose ; Electronic Health Records ; Health Personnel ; Humans ; Patient Care ; Policy Making ; Public Health ; Semantics