https://scholars.lib.ntu.edu.tw/handle/123456789/478185
標題: | Web-Based Dashboard for the Interactive Visualization and Analysis of National Risk-Standardized Mortality Rates of Sepsis in the US | 作者: | Lee, M.-T. Lin, F.-C. Chen, S.-T. Hsu, W.-T. Lin, S. Chen, T.-S. FEI-PEI LAI CHIEN-CHANG LEE |
公開日期: | 2020 | 卷: | 44 | 期: | 2 | 起(迄)頁: | 54 | 來源出版物: | Journal of Medical Systems | 摘要: | Sepsis mortality is heavily influenced by the quality of care in hospitals. Comparing risk-standardized mortality rate (RSMR) of sepsis patients in different states in the United States has potentially important clinical and policy implications. In the current study, we aimed to compare national sepsis RSMR using an interactive web-based dashboard. We analyzed sepsis mortality using the National Inpatient Sample Database of the US. The RSMR was calculated by the hierarchical logistic regression model. We wrote the interactive web-based dashboard using the Shiny framework, an R package that integrates R-based statistics computation and graphics generation. Visual summarizations (e.g., heat map, and time series chart), and interactive tools (e.g., year selection, automatic year play, map zoom, copy or print data, ranking data by name or value, and data search) were implemented to enhance user experience. The web-based dashboard (https://sepsismap.shinyapps.io/index2/) is cross-platform and publicly available to anyone with interest in sepsis outcomes, health inequality, and administration of state/federal healthcare. After extrapolation to the national level, approximately 35 million hospitalizations were analyzed for sepsis mortality each year. Eight years of sepsis mortality data were summarized into four easy to understand dimensions: Sepsis Identification Criteria; Sepsis Mortality Predictors; RSMR Map; RSMR Trend. Substantial variation in RSMR was observed for different states in the US. This web-based dashboard allows anyone to visualize the substantial variation in RSMR across the whole US. Our work has the potential to support healthcare transparency, information diffusion, health decision-making, and the formulation of new public policies. ? 2020, Springer Science+Business Media, LLC, part of Springer Nature. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/478185 | ISSN: | 0148-5598 | DOI: | 10.1007/s10916-019-1509-9 | SDG/關鍵字: | Article; clinical outcome; comparative study; data analysis; data base; data visualization; feature selection; health care system; health care utilization; hospitalization; human; information dissemination; Internet; medical decision making; medical informatics; medical information; mortality rate; mortality risk; predictor variable; prognosis; public health service; public policy; sepsis; standardized mortality ratio; total quality management; trend study; United States; web based dashboard; web browser; electronic health record; female; health disparity; hospital mortality; information processing; information retrieval; male; mortality; procedures; risk assessment; sepsis; statistical model; Data Display; Electronic Health Records; Female; Health Status Disparities; Hospital Mortality; Humans; Information Storage and Retrieval; Logistic Models; Male; Outcome and Process Assessment, Health Care; Risk Assessment; Sepsis; United States |
顯示於: | 醫學院附設醫院 (臺大醫院) |
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