Development of a web-based care networking system to support visiting healthcare professionals in the community.

BACKGROUND: The role of visiting health services has been proven to be effective in promoting the health of older populations. Hence, developing a web system for nurses may help improve the quality of visiting health services for community-dwelling frail older adults. This study was conducted to develop a web application that reflects the needs of visiting nurses. METHODS: Visiting nurses of public health centers and community centers in South Korea participated in the design and evaluation process. Six nurses took part in the focus group interviews, and 21 visiting nurses and community center managers participated in the satisfaction evaluation. Focus group interviews were conducted to identify the needs of visiting nurses with respect to system function. Based on the findings, a web application that can support the effective delivery of home visiting services in the community was developed. An artificial intelligence (AI) algorithm was also developed to recommend health and welfare services according to each patient's health status. After development, a structured survey was conducted to evaluate user satisfaction with system features using Kano's model. RESULTS: The new system can be used with mobile devices to increase the mobility of visiting nurses. The system includes 13 features that support the management of patient data and enhance the efficiency of visiting services (e.g., map, navigation, scheduler, protocol archives, professional advice, and online case conferencing). The user satisfaction survey revealed that nurses showed high satisfaction with the system. Among all features, the nurses were most satisfied with the care plan, which included AI-based recommendations for community referral. CONCLUSIONS: The system developed from the study has attractive features for visiting nurses and supports their essential tasks. The system can help with effective case management for older adults requiring in-home care and reduce nurses' workload. It can also improve communication and networking between healthcare and long-term care institutions.

Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model.

Recently, the population of Seoul has been affected by particulate matter in the atmosphere. This problem can be addressed by developing an elaborate forecasting model to estimate the concentration of fine dust in the metropolitan area. We present a forecasting model of the fine dust concentration with an extended range of input variables, compared to existing models. The model takes inputs from holistic perspectives such as topographical features on the surface, chemical sources of the fine dusts, traffic and the human activities in sub-areas, and meteorological data such as wind, temperature, and humidity, of fine dust. Our model was evaluated by the index-of-agreement (IOA) and the root mean-squared error (RMSE) in predicting PM2.5 and PM10 over three subsequent days. Our model variations consist of linear regressions, ARIMA, and Gaussian process regressions (GPR). The GPR showed the best performance in terms of IOA that is over 0.6 in the three-day predictions.

Identifying prescription patterns with a topic model of diseases and medications.

Wide variance exists among individuals and institutions for treating patients with medicine. This paper analyzes prescription patterns using a topic model with more than four million prescriptions. Specifically, we propose the disease-medicine pattern model (DMPM) to extract patterns from a large collection of insurance data by considering disease codes joined with prescribed medicines. We analyzed insurance prescription data from 2011 with DMPM and found prescription patterns that could not be identified by traditional simple disease classification, such as the International Classification of Diseases (ICD). We analyzed the identified prescription patterns from multiple aspects. First, we found that our model better explain unseen prescriptions than other probabilistic models. Second, we analyzed the similarities of the extracted patterns to identify their characteristics. Third, we compared the identified patterns from DMPM to the known disease categorization, ICD. This comparison showed what additional information can be provided by the data-oriented bottom-up patterns in contrast to the knowledge-based top-down categorization. The comparison results showed that the bottom-up categorization allowed for the identification of (1) diverse treatment options for the same disease symptoms, and (2) diverse disease cases sharing the same prescription options. Additionally, the extracted bottom-up patterns revealed treatment differences based on basic patient information better than the top-down categorization. We conclude that this data-oriented analysis will be an effective alternative method for analyzing the complex interwoven disease-prescription relationship.