Nitrate-nitrogen (NO₃-N) contamination in groundwater is a major problem of drinking and domestic waters in rural areas. This study revealed the influence of land use type on shallow alluvial groundwaters in a typical rural area in South Korea by applying a self-organizing map (SOM), principal component analysis (PCA), and hierarchical cluster analysis (HCA). The uncertainty of spatial information on land use was improved by using a buffer zone of the average influence radius of 32.65 m surrounding wells. Two major land-use types, forests (44.9%) and rice fields (28.8%), occupied a total of 73.7% of the rural area. The higher concentrations of NO₃-N in public facilities and livestock areas were demonstrated to directly recharge groundwater pollutants. NO₃-N contamination in rice paddies, which also contained chlorine (Cl) and sulfate (SO₄), was assessed according to the nutrients and residual salt in the soil. In addition, different NO₃-N concentrations for the same land use indicate various biochemical reactions and NO₃-N recharge types into the groundwater system. The shallow groundwaters in the study area were classified into three clusters according to their chemical constituents and land-use properties, especially NO₃-N concentration, including pH, Cl, and SO₄, using a SOM, PCA, and HCA. Unlike existing studies, we applied a buffer zone based on the Cooper-Jacob equation to obtain an improved SOM model prediction accuracy approximately 10% greater than that using the original dataset.

Telehealth services are time- and cost-saving solutions for disease management for older adults. Minority older individuals with multiple risk factors have an increasing demand for telehealth services. There are insufficient data on patient safety in telehealth services. This study aimed to enhance the quality of telehealth services by reducing errors and creating a safe user environment for low-income older adults. Failure mode and effects analysis tool (FMEA) was adopted to manage potential risks for sustainable digital transformation. An eight-member multidisciplinary team conducted telehealth FMEA to determine risk priority numbers (RPNs). The process included identifying the potential cause and effect failure mode of each step; measuring severity, probability, and detectability scores for RPNs; and generating strategies to decrease potential failures. This study identified 24 risk factors and 34 causes in four major phases with a mean RPN of 90.7: preparation to measure biosignals, measurement of biosignals following instructions from a personal device, confirmation of measurement results, and intervention based on disease or condition type. Risk prioritization revealed four high failure modes and a total RPN of 362.7. Based on fundamental causes, risks were categorized as oblivescence, economic issues, and technology literacy. To correct these failure modes, stabilization of the platform, adding to the providers' manpower, and support for government policies are recommended. FMEA identifies and evaluates the potential risks of telehealth services. The selected priorities reduce the clinical risks of low-income elders who use telehealth services by weighting clinical actions.

Background: The coronavirus pandemic (COVID-19) has also emerged as an infodemic, thereby worsening the harm of the pandemic. This situation has highlighted the need for a deeply rooted understanding of the health information–seeking behaviors (HISBs) of people. Objective: The aim of this paper was to review and provide insight regarding methodologies and the construct of content in HISB surveys by answering the following research question: what are the characteristics of the measurement tools for assessing HISBs in nationally representative surveys around the world? Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was used as the framework for this study. A data search was performed through 5 international and 2 Korean databases covering the years between 2008 and 2020. Initially, studies performed among nationally representative samples were included to discover HISB survey instruments. The methodologies of the studies using HISB surveys were analyzed. For content analysis, 2 researchers reached a consensus through discussion by scrutinizing the contents of each survey questionnaire. Results: A total of 13 survey tools from 8 countries were identified after a review of 2333 records from the search results. Five survey tools (Health Information National Trends Survey, Health Tracking Survey, Annenberg National Health Communication Survey, National Health Interview Survey, and Health Tracking Household Survey) from the United States, 2 instruments from Germany, and 1 tool from each of the countries of the European Union, France, Israel, Poland, South Korea, and Taiwan were identified. Telephone or web-based surveys were commonly used targeting the adult population (≥15 years of age). From the content analysis, the domains of the survey items were categorized as follows: information (information about health and patient medical records), channel (offline and online), and health (overall health, lifestyle, and cancer). All categories encompassed behavioral and attitude dimensions. A theoretical framework, that is, an information-channel-health structure for HISBs was proposed. Conclusions: The results of our study can contribute to the development and implementation of the survey tools for HISB with integrated questionnaire items. This will help in understanding HISB trends in national health care.

Background: The rapid proliferation of artificial intelligence (AI) requires new approaches for human-AI interfaces that are different from classic human-computer interfaces. In developing a system that is conducive to the analysis and use of health big data (HBD), reflecting the empirical characteristics of users who have performed HBD analysis is the most crucial aspect to consider. Recently, human-centered design methodology, a field of user-centered design, has been expanded and is used not only to develop types of products but also technologies and services. Objective: This study was conducted to integrate and analyze users’ experiences along the HBD analysis journey using the human-centered design methodology and reflect them in the development of AI agents that support future HBD analysis. This research aims to help accelerate the development of novel human-AI interfaces for AI agents that support the analysis and use of HBD, which will be urgently needed in the near future. Methods: Using human-centered design methodology, we collected data through shadowing and in-depth interviews with 16 people with experience in analyzing and using HBD. We identified users’ empirical characteristics, emotions, pain points, and needs related to HBD analysis and use and created personas and journey maps. Results: The general characteristics of participants (n=16) were as follows: the majority were in their 40s (n=6, 38%) and held a PhD degree (n=10, 63%). Professors (n=7, 44%) and health care personnel (n=10, 63%) represented the largest professional groups. Participants’ experiences with big data analysis varied, with 25% (n=4) being beginners and 38% (n=6) having extensive experience. Common analysis methods included statistical analysis (n=7, 44%) and data mining (n=6, 38%). Qualitative findings from shadowing and in-depth interviews revealed key challenges: lack of knowledge on using analytical solutions, crisis management difficulties during errors, and inadequate understanding of health care data and clinical decision-making, especially among non–health care professionals. Three types of personas and journey maps—health care professionals as big data analysis beginners, health care professionals who have experience in big data analytics, and non–health care professionals who are experts in big data analytics—were derived. They showed a need for personalized platforms tailored to the user level, appropriate direction through a navigation function, a crisis management support system, communication and sharing among users, and expert linkage service. Conclusions: The knowledge obtained from this study can be leveraged in designing an AI agent to support future HBD analysis and use. This is expected to further increase the usability of HBD by helping users perform effective use of HBD more easily.

The use of shallow geothermal energy systems utilizing groundwater temperature for the air-conditioning of buildings is increasing worldwide. The impact of these systems on groundwater quality has become crucial for environmental regulations and system design. For the long-term operation of geothermal systems, it is important to evaluate their influence on the geochemical properties of groundwater, including precipitation and dissolution of secondary minerals. This research was conducted in a real-scale geothermal system, consisting of a groundwater heat pump (GWHP). Hydrochemical data were obtained from samples collected from an aquifer before heating, during heating, and before cooling operations of the GWHP. The Langelier Saturation Index and Ryznar Stability Index were calculated, and the saturation index was simulated with the PHREEQC program. Evidence from water table variation, temperature change, and ${}^{87}$ Sr/ ${}^{86}$ Sr isotope distribution showed that groundwater flows from a well located on the northwest side of the geothermal well. The saturation index values showed that the pristine groundwater favors carbonate dissolution, however, manganese oxides are more sensitive to temperature than carbonate minerals. In addition, mineral precipitation and dissolution were found to vary with depth and temperature.