1
|
Rabiei R, Bastani P, Ahmadi H, Dehghan S, Almasi S. Developing public health surveillance dashboards: a scoping review on the design principles. BMC Public Health 2024; 24:392. [PMID: 38321469 PMCID: PMC10848508 DOI: 10.1186/s12889-024-17841-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 01/22/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards. METHODOLOGY This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted. RESULTS Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure. CONCLUSION Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.
Collapse
Affiliation(s)
- Reza Rabiei
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Peivand Bastani
- College of Business, Government and Law, Flinders University, Adelaide, SA, 5042, Australia
| | - Hossein Ahmadi
- Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK
| | - Shirin Dehghan
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sohrab Almasi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
2
|
Healey CG, Simmons SJ, Manivannan C, Ro Y. Visual Analytics for the Coronavirus COVID-19 Pandemic. BIG DATA 2022; 10:95-114. [PMID: 35049331 DOI: 10.1089/big.2021.0023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The coronavirus disease COVID-19 was first reported in Wuhan, China, on December 31, 2019. The disease has since spread throughout the world, affecting 227.2 million individuals and resulting in 4,672,629 deaths as of September 9, 2021, according to the Johns Hopkins University Center for Systems Science and Engineering. Numerous sources track and report information on the disease, including Johns Hopkins itself, with its well-known Novel Coronavirus Dashboard. We were also interested in providing information on the pandemic. However, rather than duplicating existing resources, we focused on integrating sophisticated data analytics and visualization for region-to-region comparison, trend prediction, and testing and vaccination analysis. Our high-level goal is to provide visualizations of predictive analytics that offer policymakers and the general public insight into the current pandemic state and how it may progress into the future. Data are visualized using a web-based jQuery+Tableau dashboard. The dashboard allows both novice viewers and domain experts to gain useful insights into COVID-19's current and predicted future state for different countries and regions of interest throughout the world.
Collapse
Affiliation(s)
- Christopher G Healey
- Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA
- Institute for Advanced Analytics, North Carolina State University, Raleigh, North Carolina, USA
| | - Susan J Simmons
- Institute for Advanced Analytics, North Carolina State University, Raleigh, North Carolina, USA
| | - Chandra Manivannan
- Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA
| | - Yoonchul Ro
- Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA
| |
Collapse
|
3
|
van Elten HJ, Sülz S, van Raaij EM, Wehrens R. Big Data Health Care Innovations: Performance Dashboarding as a Process of Collective Sensemaking. J Med Internet Res 2022; 24:e30201. [PMID: 35191847 PMCID: PMC8905474 DOI: 10.2196/30201] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 09/10/2021] [Accepted: 12/15/2021] [Indexed: 12/21/2022] Open
Abstract
Big data is poised to revolutionize health care, and performance dashboards can be an important tool to manage big data innovations. Dashboards show the progress being made and provide critical management information about effectiveness and efficiency. However, performance dashboards are more than just a clear and straightforward representation of performance in the health care context. Instead, the development and maintenance of informative dashboards can be more productively viewed as an interactive and iterative process involving all stakeholders. We refer to this process as dashboarding and reflect on our learnings within a large European Union–funded project. Within this project, multiple big data applications in health care are being developed, piloted, and scaled up. In this paper, we discuss the ways in which we cope with the inherent sensitivities and tensions surrounding dashboarding in such a dynamic environment.
Collapse
Affiliation(s)
- Hilco J van Elten
- Erasmus School of Health Policy & Management, Erasmus University, Rotterdam, Netherlands
| | - Sandra Sülz
- Erasmus School of Health Policy & Management, Erasmus University, Rotterdam, Netherlands
| | - Erik M van Raaij
- Erasmus School of Health Policy & Management, Erasmus University, Rotterdam, Netherlands.,Rotterdam School of Management, Erasmus University, Rotterdam, Netherlands
| | - Rik Wehrens
- Erasmus School of Health Policy & Management, Erasmus University, Rotterdam, Netherlands
| |
Collapse
|
4
|
Hu JR, Yo CH, Lee HY, Su CH, Su MY, Huang AH, Liu Y, Hsu WT, Lee M, Chen YC, Lee CC. Risk-standardized sepsis mortality map of the United States. Digit Health 2022; 8:20552076211072400. [PMID: 35096409 PMCID: PMC8796072 DOI: 10.1177/20552076211072400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 12/13/2021] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Sepsis is the leading cause of in-hospital mortality in the United States (US). Quality improvement initiatives for improving sepsis care depend on accurate estimates of sepsis mortality. While hospital 30-day risk-standardized mortality rates have been published for patients hospitalized with acute myocardial infarction, heart failure, and pneumonia, risk-standardized mortality rates for sepsis have not been well characterized. We aimed to construct a sepsis risk-standardized mortality rate map for the United States, to illustrate disparities in sepsis care across the country. METHODS This cross-sectional study included adults from the US Nationwide Inpatient Sample who were hospitalized with sepsis between 1 January 2010 and 30 December 2011. Hospital-level risk-standardized mortality rates were calculated using hierarchical logistic modelling, and were risk-adjusted with predicted mortality derived from (1) the Sepsis Risk Prediction Score, a logistic regression model, and (2) gradient-boosted decision trees, a supervised machine learning (ML) algorithm. RESULTS Among 1,739,033 adults hospitalized with sepsis, 50% were female, and the median age was 71 years (interquartile range: 58-81). The national median risk-standardized mortality rate for sepsis was 18.4% (interquartile range: 17.0, 21.0) by the boosted tree model, which had better discrimination than the Sepsis Risk Prediction Score model (C-statistic 0.87 and 0.78, respectively). The highest risk-standardized mortality rates were found in Wyoming, North Dakota, and Mississippi, while the lowest were found in Arizona, Colorado, and Michigan. CONCLUSIONS Wide variation exists in sepsis risk-standardized mortality rates across states, representing opportunities for improvement in sepsis care. This represents the first map of state-level variation of risk-standardized mortality rates in sepsis.
Collapse
Affiliation(s)
- Jiun-Ruey Hu
- Department of Internal Medicine, Yale School of Medicine, USA
| | - Chia-Hung Yo
- Department of Emergency Medicine, Far Eastern Memorial Hospital, Taiwan
| | - Hsin-Ying Lee
- Department of Medicine, College of Medicine, National Taiwan University, Taiwan
| | - Chin-Hua Su
- Department of Emergency Medicine, National Taiwan University Hospital, Taiwan
| | - Ming-Yang Su
- Department of Surgery, Chang Gung Memorial Hospital, Taiwan
| | - Amy Huaishiuan Huang
- Department of Emergency Medicine, National Taiwan University Hospital, Taiwan
- Department of Internal Medicine, Taipei City Hospital, Renai Branch, Taiwan
| | - Ye Liu
- Department of Health Care Organization and Policy, University of Alabama at Birmingham, School of Public Health, USA
| | - Wan-Ting Hsu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, USA
| | | | - Yee-Chun Chen
- Department of Medicine, College of Medicine, National Taiwan University, Taiwan
- Department of Internal Medicine,National Taiwan University, Taiwan
| | - Chien-Chang Lee
- Department of Emergency Medicine, National Taiwan University Hospital, Taiwan
- Center of Intelligent Healthcare, National Taiwan University Hospital, Taiwan
| |
Collapse
|
5
|
Salehi M, Arashi M, Bekker A, Ferreira J, Chen DG, Esmaeili F, Frances M. A Synergetic R-Shiny Portal for Modeling and Tracking of COVID-19 Data. Front Public Health 2021; 8:623624. [PMID: 33585390 PMCID: PMC7873562 DOI: 10.3389/fpubh.2020.623624] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/16/2020] [Indexed: 11/19/2022] Open
Abstract
The purpose of this paper is to introduce a useful online interactive dashboard (https://mahdisalehi.shinyapps.io/Covid19Dashboard/) that visualize and follow confirmed cases of COVID-19 in real-time. The dashboard was made publicly available on 6 April 2020 to illustrate the counts of confirmed cases, deaths, and recoveries of COVID-19 at the level of country or continent. This dashboard is intended as a user-friendly dashboard for researchers as well as the general public to track the COVID-19 pandemic, and is generated from trusted data sources and built in open-source R software (Shiny in particular); ensuring a high sense of transparency and reproducibility. The R Shiny framework serves as a platform for visualization and analysis of the data, as well as an advance to capitalize on existing data curation to support and enable open science. Coded analysis here includes logistic and Gompertz growth models, as two mathematical tools for predicting the future of the COVID-19 pandemic, as well as the Moran's index metric, which gives a spatial perspective via heat maps that may assist in the identification of latent responses and behavioral patterns. This analysis provides real-time statistical application aiming to make sense to academic- and public consumers of the large amount of data that is being accumulated due to the COVID-19 pandemic.
Collapse
Affiliation(s)
- Mahdi Salehi
- Department of Mathematics and Statistics, University of Neyshabur, Neyshabur, Iran
| | - Mohammad Arashi
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
- Department of Statistics, University of Pretoria, Hatfield, South Africa
| | - Andriette Bekker
- Department of Statistics, University of Pretoria, Hatfield, South Africa
| | - Johan Ferreira
- Department of Statistics, University of Pretoria, Hatfield, South Africa
| | - Ding-Geng Chen
- Department of Statistics, University of Pretoria, Hatfield, South Africa
| | - Foad Esmaeili
- Department of Mathematics and Statistics, University of Neyshabur, Neyshabur, Iran
| | - Motala Frances
- Department of Statistics, University of Pretoria, Hatfield, South Africa
| |
Collapse
|