Author's summary A distributed research network refers to a research network wherein multiple institutions unite for joint research based on common data model wherein the structure and meaning of the data are standardized. Researchers can only send the analysis code to multiple institutions and get the summarized analysis results. Thus, researchers cannot see any of the individual patient data at any time, and no individual patient data can be leaked from the institutions. The Observational Health Data Sciences and Informatics research network standardized 928 million unique records or 12% of the world’s population from 41 countries.

BACKGROUND Although telehealth is considered a key component in combating the worldwide crisis caused by COVID-19, the factors that influence its acceptance by the general population after the flattening of the COVID-19 curve remain unclear. OBJECTIVE We aimed to identify factors affecting telehealth acceptance, including anxiety related to COVID-19, after the initial rapid spread of the disease in South Korea. METHODS We proposed an extended technology acceptance model (TAM) and performed a cross-sectional survey of individuals aged ≥30 years. In total, 471 usable responses were collected. Confirmatory factor analysis was used to examine the validity of measurements, and the partial least squares (PLS) method was used to investigate factors influencing telehealth acceptance and the impacts of COVID-19. RESULTS PLS analysis showed that increased accessibility, enhanced care, and ease of telehealth use had positive effects on its perceived usefulness (<i>P</i>=.002, <i>P</i>&lt;.001, and <i>P</i>&lt;.001, respectively). Furthermore, perceived usefulness, ease, and privacy/discomfort significantly impacted the acceptance of telehealth (<i>P</i>&lt;.001, <i>P</i>&lt;.001, and <i>P</i>&lt;.001, respectively). However, anxiety toward COVID-19 was not associated with telehealth acceptance (<i>P</i>=.112), and this insignificant relationship was consistent in the cluster (n=216, 46%) of respondents with chronic diseases (<i>P</i>=.185). CONCLUSIONS Increased accessibility, enhanced care, usefulness, ease of use, and privacy/discomfort are decisive variables affecting telehealth acceptance in the Korean general population, whereas anxiety about COVID-19 is not. This study may lead to a tailored promotion of telehealth after the pandemic subsides.

BACKGROUND Although spatial epidemiology is widely used to evaluate geographic variations and disparities in health outcomes, constructing geographic statistical models usually requires a labor-intensive process that limits its overall utility. OBJECTIVE This study aimed to develop open-source software for scalable spatial epidemiological analysis based on standardized geocode and a health care database and to demonstrate its applicability and methodological quality across countries. METHODS We developed Application for Epidemiological Geographic Information System (AEGIS) based on a standardized geocode and common data model (CDM) for health care data. AEGIS was implemented to access the geographic distribution in the incidences and health outcomes of non–communicable and communicable diseases in South Korea and the United States, specifically, the (1) geographical distribution of incident cancers, (2) spatial heterogeneity of 5-year mortality in Korean patients with cancer, and (3) identification of an endemic area of malaria in South Korea and the United States. The results from South Korea were compared with those of previous studies to assess the reliability of AEGIS. RESULTS AEGIS provides two widely used spatial analysis methods for health outcome assessment: disease mapping and detection of concentrated clusters of medical conditions or outcomes. It was possible to describe the spatial distribution, assess the spatial heterogeneity, and detect the focused area of a medical condition or outcome in various databases from different countries. The AEGIS-generated spatial distribution of incident cancer in Korea was consistent with those of previous reports. AEGIS was able to detect the known endemic area of malaria in South Korea. CONCLUSIONS As an open-source, cross-country, spatial analytics solution, AEGIS may globally expedite the assessment of differences in geographic health outcomes through the use of standardized geocode and health care databases.