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Web-Based Dashboard for the Interactive Visualization and Analysis of National Risk-Standardized Mortality Rates of Sepsis in the US

  • Systems-Level Quality Improvement
  • Published:
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Abstract

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.

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Abbreviations

AHRQ:

Agency for Healthcare Research and Quality

AMI:

Acute Myocardial Infarction

CCS:

Clinical Classification Software codes

CMS:

Centers for Medicare & Medicaid Services

HF:

Heart Failure

HLM:

Hierarchical linear modeling

HHS:

Health and Human Services

ICD-9- CM:

International Classification of Diseases, Ninth Revision, Clinical Modification

NIS:

National Inpatient Sample

NQF:

National Quality Forum

RSMRs:

Risk Standardized Mortality Rates

United States:

US

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Acknowledgements

We thank the staff of the Core Labs, the Department of Medical Research, and National Taiwan University Hospital for technical support. Medical wisdom consulting group for technical assistance in statistical analysis.

Funding

This study is supported by the Taiwan National Science Foundation Grant NSC 102–2314-B-002 -131 -MY3; Taiwan National Ministry of Science and Technology Grants MOST 104–2314-B-002 -039 -MY3, and MOST 105–2811-B-002-031. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

  1. Meng-Tse Lee and Fong-Ci Lin contributed equally to this work.

Authors and Affiliations

  1. Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan

    Meng-Tse Lee & Feipei Lai

  2. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan

    Fong-Ci Lin & Chien-Chang Lee

  3. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Szu-Ta Chen & Wan-Ting Hsu

  4. Department of Pediatrics, National Taiwan University Hospital Yun-Lin Branch, Yunlin County, Taiwan

    Szu-Ta Chen

  5. Department of Pediatrics, National Taiwan University and College of Medicine, Taipei, Taiwan

    Szu-Ta Chen

  6. Graduate Institute of Toxicology, College of Medicine, National Taiwan University, Taipei, Taiwan

    Szu-Ta Chen

  7. Department of Data Sciences, University of California, Berkeley, CA, USA

    Samuel Lin

  8. Department of Information Management, Tunghai University, Taichung, Taiwan

    Tzer-Shyong Chen

  9. Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan

    Feipei Lai

  10. Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

    Feipei Lai

  11. Health Economic Outcomes Research Group and Department of Emergency Medicine, National Taiwan University Hospital, No.7, Chung Shan S. Rd., Zhongzheng Dist, Taipei, 100, Taiwan

    Chien-Chang Lee

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  1. Meng-Tse Lee
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Correspondence to Chien-Chang Lee.

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Lee, MT., Lin, FC., Chen, ST. et al. Web-Based Dashboard for the Interactive Visualization and Analysis of National Risk-Standardized Mortality Rates of Sepsis in the US. J Med Syst 44, 54 (2020). https://doi.org/10.1007/s10916-019-1509-9

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