Developing Public Health Surveillance Dashboards: A Scoping Review on the Design Principles

doi:10.21203/rs.3.rs-3107405/v1

Download PDF

Research Article

Developing Public Health Surveillance Dashboards: A Scoping Review on the Design Principles

https://doi.org/10.21203/rs.3.rs-3107405/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 06 Feb, 2024

Read the published version in BMC Public Health →

You are reading this latest preprint version

Background

Public health dashboards 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 public health dashboards: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between 2010 and 2022. The final search of articles was done on November 30, 2022. Only articles in English language were included. Qualitative synthesis and trend analysis was 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 putting the design principles into practice to improve the functionality of these systems in monitoring and decision making. Taking requirements of users into account, 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.

Public Health

Public Health Informatics

Public Health Surveillance

Dashboard

Public health surveillance is the continuous and systematic collection, analysis, and interpretation of health-related data needed for planning, implementing, and evaluating public health performance. It is a tool for estimating the health and behavior of a society, which can determine the health status and the need for interventions and their effects. Health monitoring aims to empower decision-makers for effective management based on valuable and, up-to-date evidence [1, 2]. Interpreting the obtained results and sharing the information helps the stakeholders take quick and appropriate measures to reduce morbidity and mortality and improve the welfare of society [3]. This process requires the cooperation of many stakeholders, from the community level to the senior management of the health system, who should work in a systematic and complementary manner to promote public health security [3]. Public health goals include preventing epidemics, protecting against environmental hazards, encouraging, and promoting healthy behaviors, managing natural disasters, helping the community recover, and guaranteeing quality and access to health services [3]. One of the essential services public health organizations provide is monitoring health status and identifying community health problems [3].

Overview of surveillance system

For this purpose, the public health monitoring system employs systems that can continuously monitor the health status of the community and uses the data for planning, implementation, and evaluation [4]. Initially, manual or telephone reporting was used to monitor public health. This method creates many challenges in analyzing and extracting valuable information for timely decision-making due to the production of large and complex data sets. Evidence shows that due to the generation of large amounts of data, information overload at high organizational levels was such that data sets were rarely effectively used for decision-making in practice, and the process of reporting, collecting, and analyzing the data would last several weeks, hindering a targeted response on time [5, 6]. After the advent and popularization of the Internet, a suitable platform was provided for the faster collection of society health-related data from a wide range of available electronic data sources. Nowadays, public health monitoring systems can collect all the necessary data from different parts of society, even remote areas, in a short time to obtain the necessary information to identify early events and situational preparation for them [7, 8]. Studies demonstrate that although these systems show clear advantages compared to traditional surveillance systems, they face limitations that still need to be resolved. Because of the large volume of data and the unstructured data sources, it is necessary to use methods that extract, process, and analyze the data and display the results of interpreting the obtained information to users most effectively [9].

Dashboard in public health surveillance

Considering the extensive data sources and the diversity of potential users in public health monitoring systems, dashboards can be a suitable tool to facilitate production and provide information to managers and policymakers in this field. In recent years, with the increase in the spread of infectious diseases worldwide, having the potential to become epidemics and pandemics [10, 11], the importance of using public health dashboards in continuous monitoring of the health status of the community, timely diagnosis, and proper management of these diseases has significantly increased. With the advent of the COVID-19 pandemic and the importance of using real-time data to manage and control this disease at the level of different societies, the role of public health dashboards has become more prominent [12, 13]. Dashboards are data-based decision support tools that provide essential business information to managers in graphic and visual form. They can retrieve and analyze a large amount of data by interacting with various data sources, extracting information from databases, and providing the user with results based on Key Performance Indicators (KPI). Therefore, the users of a dashboard can quickly be informed about the current situation and progress of the business [14–16]. The public health dashboard aims to facilitate the continuous monitoring of the health status of the community and the monitoring and predicting of disease outbreaks by collecting and integrating real-time data from various data sources and help manage and control diseases by displaying KPIs in the form of a well-designed user interface [17]. Therefore, considering the volume of data and the need for real-time monitoring and response in public health situations, it is essential to pay attention to dashboard design principles for public health surveillance [18]. The studies on public health dashboard guidelines are primarily focused on these systems' content and user interface. The design principles suggested in the studies include a customizable, actionable "launch pad" [19, 20], supporting correct data interpretation [20, 21], information aggregation [22], minimalist aesthetics [21], user workload reduction [21], GIS interface [23], minimal cognitive processing, and using temporal trend analysis techniques [24].

Monitoring health, managing epidemic diseases, and taking timely actions require real-time information exchange between health information systems and dashboards. Therefore, considering the volume of the data, the need for real-time monitoring and response in public health situations, and disease monitoring during epidemics, well-designed dashboards are needed to facilitate and accelerate decision-making in epidemics and medical crises by extracting data from various systems and sources and providing timely reports. Accordingly, this study aims to determine the design principles of public health monitoring dashboards.

Study Design

Scoping reviews try to identify, retrieve, and summarize information from studies relevant to a particular topic to find key concepts and are conducted to answer more general questions [25]. One of their advantages is determining the feasibility and necessity of conducting a systematic review in a specific domain [25]. Arksey and O’Malley’s framework (2005), as an influential framework suggested by the JBI guidance, was applied to conduct this scoping review [25]. Six following stages are recommended based on this framework; the first five are compulsory for the robustness and trustworthiness of the review, while the last stage is indicated as an optional one.

Identifying the research question

As per the guideline, the question should incorporate the PCC elements. This study included all the published papers about public health dashboards. The context refers to all the principles and determinants that impact designing such dashboards, and it also refers to applying public health dashboards in decision- making and monitoring the health status. Accordingly, the main research question is defined as: “What are the principles for designing a public health dashboard?”

Identification of relevant studies

Searches were conducted in PubMed, Web of Science, IEEE, and Scopus. A combination of MeSH terms and related keywords was used for the search strategy. The search strategy was carried out with the following keywords.

(("Surveillance"[Title/Abstract] OR "Public Health Surveillance"[Mesh] OR "public health"[Mesh] OR "public health"[Title/Abstract]) AND )dashboard [Title/Abstract] OR “Web-based surveillance system”[Title/Abstract])

The search for articles was carried out between 2010 and 2022. The final search of articles was conducted on November 30, 2022. EndNote version 20.2.1 was applied to manage the articles` inclusion and screening process.

Study selection

For this purpose, first, the retrieved articles were screened based on their title and abstract. All these titles and abstracts were reviewed by two authors independently, and the senior author (RR) finalized the cases of disagreement. After the approval of the remaining articles by the senior author, the articles` full text were independently reviewed by two authors based on the inclusion and exclusion criteria of the study (Textbox 1). Any disagreement regarding the selection of articles was discussed with the senior author. Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review (PRISMA-ScR) guideline [26] was used to manage the eligible articles at this stage.

Charting data

The descriptive data extracted from the articles, including the year of publication, public health category, dashboard implementation location, and dashboard implementation level, was inserted into Microsoft Excel Version 16(Microsoft Corporation, Redmont, WA). for combination and analysis. An online map chart analyzed and displayed the dashboard implementation location in different countries.

In this step, two methods of data analysis, quantitative descriptive analysis and qualitative content analysis, were applied. Excel software (version 16) was used to summarize the distribution and frequency of the included articles based on year of publication, public health category, dashboard implementation location and level. (Fig. 1). Then, the design principles of the public health dashboard were extracted by reviewing the content of the articles (Table 1). Microsoft Office 360 was used to categorize the design principles of dashboards. This scoping review also utilized trend analysis to illustrate the trends of publications in each of the public health category. The number of articles published in different years was drawn using Microsoft Excel (Version 16).

Five hundred and 43 articles were retrieved after searching the databases. The PRISMA flowchart illustrates that after eliminating the duplications and screening the articles, 67 articles were eligible for analysis based on the inclusion and exclusion criteria. (Fig. 2.)

Characteristics of included studies

The geographical distribution of the designed dashboards showed that most of the selected studies were conducted in North America (N = 29. 43%), Europe, Asia, and Africa, respectively. In relation to the studies conducted on public health dashboards, there was an increasing trend in the number of published articles from 2020 to 2022. In terms of implementation scale, the designed dashboards were mainly reported at the national level (58%) (Regional 27%, local 11%, and global 4%). In addition, (N = 23. 30%) of dashboards were designed to monitor and control for COVID-19 followed by dashboards developed for maternal and newborn health (N = 8. 12%) and AIDS (N = 6. 9%) (Fig. 1).

Principles of designing public health dashboards

Considering the objective and target users

First, the purpose of designing a dashboard and the target users should be considered. The dashboard's design, visualization tools, content, and how the information is represented vary based on the dashboard users. The need for compatibility between the content and information displayed by the dashboard and the tasks and needs of users can impact the use of the dashboard [27–29].

Appropriate content

In designing dashboards, it is necessary to choose KPIs that suit users' needs. Appropriate KPIs are selected and organized based on the objectives of the dashboard and its users. KPIs are effective when the dashboard displays indicators matching users' understanding and knowledge. Furthermore, the number of selected KPIs for monitoring by the dashboard is essential [27, 28].

In the reviewed dashboards, KPIs were placed on the dashboard's main page, allowing for timely monitoring and display of the current situation at a glance. KPIs' placement and display in the dashboard is top-down so that macro indicators (global, national) are placed on the main screen. KPIs and global indicators can be compared at this level. Meso (urban, regional) indicators are at the next level, which can compare cities and regions. Micro indicators are on the third level, which are performance indicators at the level of institutions. Managing the amount of information displayed on the dashboard is also essential [27, 28, 30–40].

Interface

The dashboard user interface consists of two parts: interactive tools and visual tools.

Interactive tools

In the reviewed dashboards, the summary view feature was first used to monitor macro indicators at a glance, and unnecessary details were not displayed. This feature helps summarize data and reduce complexity. If needed, the indicators' details can be accessed using the drill-up and drill-down features. The pan-and-zoom feature can be used to magnify or reduce the details. The customizable feature enables users to customize information display based on indicators according to their needs. If real-time monitoring is needed, the reports based on the determined KPIs are displayed in real-time [27, 30, 33, 35–37, 39, 41–49].

Visual tools

Using appropriate visualization techniques based on the nature of KPIs and the experience and skill of users will improve dashboard design. Choosing the correct type of visualization tool that matches the type and nature of KPIs is essential in designing dashboards. In the reviewed dashboards, different visual techniques (including data tables, pie charts, bars, histograms, lines, areas, scatter bubbles, and a series of multiple and interactive maps) were used based on the nature of the indicators. Geographical maps were used to show the geographical location of each dashboard. Correspondingly, data interpretation tools (For example, using color coding to indicate urgent/emergency alerts with red, normal situations with green, and warnings with yellow; minimizing distractions; avoiding unnecessary visual decorations in dashboard design) are essential in dashboard design [27, 28, 33, 34, 36, 41, 43, 46, 50–59].

Considering the types of data analysis and presentation

Data analysis helps users understand the relationships between data and trends in the dashboard. Various types of analysis were in the reviewed dashboards, including analysis at different geographic levels, comparing global and local KPIs, comparing indicators with standard values, and presenting data or reports in the format required by the users, such as Word or PDF [28, 33, 35, 37, 44, 53, 55, 60–62]. Artificial intelligence and data mining techniques can be used to predict trends and patterns in data over time [53, 55, 6–62].

Infrastructure

The infrastructure and implementation of the data warehouse are vital in designing dashboards and facilitating collecting and managing data from different sources. The data warehouse is used to collect and manage data from various sources, and it can be used for reporting, reviewing, and analyzing data if equipped with a dashboard [44, 45, 51, 63]. High-quality data is essential for an effective data warehouse. Having a standard for data transfer and checking the data quality before storing it in the data warehouse is crucial. Data quality aspects in the examined dashboards included data completeness (e.g., missing data), correctness (e.g., accuracy), currency (e.g., timeliness), and provenance (e.g., reliability of the source). The standards included content, transmission, structural, and security [27, 29, 34, 36, 39, 41, 52, 55, 56, 59]. Transferring data between systems and creating interactions between data sources requires attention to security and data access. For security measures, all users are assigned a level based on their performance and duties in the authorization system. Three levels of data security were implemented in the reviewed dashboards, i.e., client level, data transfer level, and server level. At the client level, user authentication is checked every 10 minutes to prevent cyber-attacks and interference in database queries through SQL injection. The client and server data were encrypted through NoSSL open-source software at the data transfer level.

Given that the web server is open to public access, a backup computer in the middle (intermediary computer) is needed for filtering access to the database [27, 29, 39] to ensure proper security standards and protect the central database. This means all requests are passed through the web server to the intermediary computer, then to the central database, and vice versa. The dashboard design should consider easy access to the dashboard via phone, tablet, and laptop for real-time monitoring and checking KPIs at a glance [55, 56, 59].

Table 1

Principles for designing public health dashboard
Main principle	Subsidiary principles		Reference
Considering aim and target users	-Knowing the audience and their information needs -Level (scale) of focus -Responsible organization and type -Languages available -Scope of web page information -Why purpose of use of the dashboard		(27–29)
Appropriate Content	KPI	-Determine the appropriate Key Performance -Indicators (macro, meso, micro level KPI) -Establishing timely and actionability indicators relating to health system capacity -Including relevant data disaggregation options (eg, sex, socioeconomic status) -Managing the type, volume, and flow of displayed information -Disaggregating the information into relevant subgroups	(27, 28, 30–40)
Interface	Interaction techniques	-Provide overview of key performance indicators, change the display size and location information according to the importance -Zoom in and zoom out, pop-up and control commands and warning, customizable and actionability of the dashboard -Allow for switching from a global to local view, drill down to the local regions of the map to explore datasets in greater detail, not used of scrolling	(27, 30, 33, 35–37, 39, 41–49)
Interface	Visualization techniques	-Choosing the right data visualization -Visualization techniques of data tables, pie charts, bar, histogram, line, area, scatter, bubble and a series of multiple and interactive maps, equipped with geographic information system (GIS) software -Using storytelling and visual cues -Supporting Correct Data Interpretation (using colored markers for clients to indicate their status, highlighting urgent/emergency alerts in red, and showing the data lines in the charts as blue (routine and exercise alerts) or red (urgent and emergency alerts) -Minimization of distractions, clichés, and unnecessary embellishments (routine and exercise alerts) or red (urgent and emergency alerts).	(27, 28, 33, 34, 36, 41, 43, 46, 50–59)
Considering types of data analysis and presentation	Trend Analysis , tracking, and forecasting	-Provide real-time analysis -Linking time trends to policy decisions -Geographic levels of analysis - Global and local comparison -Chart selection, mini map, and global information display -Techniques to analyze time trends and viewing past data - Show trends and changes in data over time -Key numbers relating to a region -Assessing performance -Support identification and evaluation of trends over time	(28, 33, 35, 37, 44, 53, 55, 60–62)
	Applies machine intelligence	-Anticipate spread and assess patterns -Allow users to select the time period over which performance indicators are displayed -Support comparison against the national average	(53, 55, 6–62)
	Reporting format	-Reports in Word and PDF	(28, 33, 35, 37, 44, 53, 55, 60–62)
Infrastructure	-Data integration and warehousing	-Proper design of data warehouse and data collection of systems -Data integration with online analytical processing system and data warehouse or other systems -Data warehouse integrated with process data and operational security and data close to real-time -The architecture is based on the service-oriented architecture (SOA)	(44, 45, 51, 63)
	-Integration data Sources and data generation	-Report data sources and methods clearly for trust to the dashboard -Data quality was assessed by examining both accuracy, real-time, and completeness -Data Input, Storage, and Extraction process for the extraction of data warehousing -Provide reliable, accurate, consistent and timely data	(28, 30, 31, 33, 41–43, 47, 50, 59, 64, 65)
	-Data quality	- completeness (e.g., missing data), correctness (e.g., accuracy), currency (e.g., timeliness), and provenance (e.g., reliability of the source)	(27, 29, 36,39)
	- information standards	- Information Exchange standards and content standards. ---Privacy and security standards, Functional standards (Work processes, workflow and dataflow models) -Standard inputs for the dashboard frontend -Standard architecture for the inclusion of new datasets into the dashboard -Standard dataset formats for the generation of data visualizations -Data collection, data fusion logic, data curation and sharing, anomaly detection, data corrections, and the human resources required to support such an effort.	(27, 29, 34, 36, 39, 41, 52, 55, 56, 59)
	-System security	-Methods, techniques, and technologies used to protect data security and attention to system security	(27,29,39)
	-Accessibility	-Web access and mobile access, desktops, laptops, and tablets	(55, 56, 59)

Main findings

This scoping review study aimed to determine dashboards designed in the public health field. The included articles explained the details of the design and development of public health dashboards and their design criteria. The study findings revealed that the production rate of public health dashboards has been increasing in the past few years. The emergence of COVID-19 and the efforts to manage and control the outbreak/pandemic have significantly impacted this increasing trend. Several institutions worldwide have designed and developed COVID-19 dashboards to report epidemiologic statistics on a county, state, or national scale. Almost all states and most major cities in the USA had deployed a COVID-19 dashboard by the end of 2020. By 2021, all dashboards designed for this purpose had been updated to include information on vaccination, or separate dashboards had been created to track COVID-19 vaccination [13]. Due to the massive amount of data and the need for real-time monitoring and response in public health situations, it is essential to pay attention to dashboard design principles to support the goals of public health surveillance [18]. After examining the indicators presented in the reviewed studies, dashboard design objectives and target users, dashboard content, dashboard user interface, data analysis and display, and infrastructure were identified as five general and essential principles in designing public health dashboards. Studies have also discussed the requirements and design principles of public health dashboards. Identifying users and their needs, using narrative information in addition to quantitative information in the dashboard, using a geographic map to display location data better, and stating the source of the data reported by the dashboard are mentioned criteria for designing a dashboard [66].

Likewise, the necessary components to support and facilitate implementing dashboards in public health organizations have been mentioned, including storage and management of data and information from different sources, coordination of data from different sources, standards support, analysis, defining and identifying KPIs, and information visualization [13]. Rasmussen et al suggested four general principles for designing dashboards including presentation format, integration, interface design, and development and implementation. These researchers remarked that inadequate attention to these principles could result in challenges for public health dashboards [67]. Furthermore, Ghazi Saeedi et al mentioned KPI development, data sources, data generation, integration of dashboards to source systems, and information presentation issues as the challenges of implementing public health dashboards [68].

Purpose and users

The purpose of designing a dashboard is to provide a suitable tool for exploring a data set and finding the information the user needs. Therefore, paying attention to the user's needs and designing the appropriate dashboard is particularly important. Considering that a variety of users use dashboards, it is impossible to design a dashboard that fits the personality and ability of each user. However, identifying the primary goal of designing a dashboard and its target user group is the first step in choosing the correct and accurate KPIs, defining appropriate interactive and visual tools, and considering related data analysis methods. Marshal et al have also emphasized the importance of this principle in designing public health dashboards in two separate studies [69].

Content

KPIs are the main content component of a health dashboard. Therefore, choosing the type and number of indicators the dashboard should monitor and display is essential in designing and developing dashboards [70–72]. Every organization must measure the indicators that fit its objectives [73]. After identifying the main objective and target users, it is necessary to determine the appropriate measurement indicators. Determining a specific and adequate number of indicators emphasizes the available information, and users can review all the indicators at a glance. These findings are consistent with Peters et al.'s study in which it was indicated that moderate use of indicators can display information in various ways and effectively guide the user's visual flow by creating a particular order [74, 75]. Serb et al. also suggested the importance of organizing indicators in the dashboard according to the level of use (macro, meso, micro level). Their study showed that at least 15 to 25 indicators are required for monitoring purposes in dashboards [76].

Interface

In user interface design, attention to the principles of information visualization and interaction with the user interface is essential [77, 78]. Uniform techniques were not used to visualize functional indicators in the reviewed studies. Uniform visualization techniques are not effective in dashboard design since it is necessary to consider users' preferences, abilities, knowledge, and skills in visualizing dashboards. Besides, Steichen and Mawad pointed out in separate studies that creating adaptive and personalized visualization systems tailored to users' cognitive and individual abilities can lead to a better understanding of displayed information [79]. The nature of data and human factors such as experience, skill, cognitive styles, and user preferences are also influential in selecting visualization and interactive techniques [80, 81]. In Shneiderman's study, interactive techniques included "overview, zoom, filter, details-on-demand, relate, history, and extract" [82]. In Khan et al 's study, interactive techniques included "zoom and pan, overview and detail, and filtering" [83]. In Dal et al 's study, interactive techniques for the dashboard included controlling the level of detail, filtering, searching, and customizing the display [84]. In Yi et al 's study, interactive features included "select, explore, reconfigure, encode, abstract/elaborate, filter, and connect" [77].

Types of analysis and data presentation

The main application of dashboards is data analysis to provide appropriate insights into the regional distribution of disease burden and help allocate resources correctly. This analysis can help policymakers and healthcare providers make appropriate decisions. In most studies, timely data reporting and a suitable time trend in data analysis have been proposed as essential indicators in dashboard design. These findings align with Curriero et al's research results, emphasizing the importance of providing up-to-date data reports [85]. Another critical indicator in dashboard design is the ability to analyze data based on geographic location, age, gender, social status, ethnicity, and race. Without collecting, registering, and using data related to meaningful subgroups of the population, these critical (and changeable) differences might be missed. Brehaut et al also showed that these indicators-as far as infrastructure limitations and legal barriers allow-are vital and should be considered in designing a dashboard. Finally, some studies used descriptive approaches, machine learning prediction models, and simulations to predict future situations. This indicator can be helpful in controlling diseases, especially pandemics [86]. This issue was also raised as one of the indicators that can help increase the efficiency of these dashboards in Brehaut's research [87].

Infrastructure

Infrastructure is the backbone of every system, and the successful adoption of any eHealth system depends on the infrastructural arrangements [88].

The findings of this study revealed that a high percentage of studies had mentioned data warehousing and appropriate web service architecture as necessary infrastructures for dashboard design [67, 89]. Given the diversity of systems and data in different formats, the dashboard infrastructure's main challenge is data integration, and creating data warehouses is an appropriate solution to this challenge [90, 91]. Access to appropriate software and hardware, use of modern technology, sharing reliable and up-to-date data, and the need for a capable workforce to create and maintain dashboards are other identified components related to dashboard infrastructure [92].

In addition, the necessary infrastructure for creating a dashboard includes access to modern IT software and hardware, continuous and reliable data sharing, and the need for a capable workforce to create and maintain dashboards [13]. Among the challenges associated with public health dashboards are data quality, big data, information architecture, privacy, and security [93]. The quality of stored data is also one of the critical issues in dashboard infrastructure. Given the importance of data in decision-making at the public health level, the quality of stored data is also an essential prerequisite for dashboard infrastructure. Fadahunsi et al also considered data quality as an essential components of dashboard infrastructure in two separate studies [94].

Informativeness (accuracy, completeness, interpretability, plausibility, provenance, and relevance), availability (accessibility, portability, security, and timeliness), and usability (conformance, consistency, and maintainability) are key features indicated in these two studies [94, 95]. Transparency about data sources and how indicators are calculated are critical for reports' overall quality, credibility, and reliability. Identifying the sources used and calculation of indicators in public health dashboards are essential for transparency about data collection and would help to understand the logic behind the reports [74, 96].

Regarding infrastructure, information security was also one of the issues mentioned in a considerable number of sources. Given the integration of various systems at the organizational level and their connection to the dashboard, using data exchange standards for system interaction is an issue that should be considered [97]. These findings were in line with a study by Li Y-CJ et al who considered electronic data exchange in standard data formats as essential for improving data accessibility. Moreover, this study showed that these standards preserve data security, reduce resource waste, and improve the quality of care [98]. In theory, based on the importance and quality of the disclosed information, access control should exist at multiple levels of security/privacy [99].

Implications for policy, practice, and future research

This study extracts the public health dashboard's design criteria and tries to propose some design principles based on the available knowledge in the area. Given the large volume of data and the need for quick response in public health situations, this study is a potentially vital source for helping policymakers, developers, public healthcare organizations, and managers to design and develop public health dashboards as a prerequisite for early response particularly during the probable pandemic. As pandemic response requires early and robust verifications, identifying this potentiality of dashboards in data management can be helpful. Lesson learned from COVID-19 pandemic clearly indicates that public health organizations need to equip themselves with dashboards for emerging pandemics and many other vital activities for public health promotion. In other words, investing in dashboard software tools and systems, processes, and people who support public health dashboards, could be a tailored practice and intervention for the public health policymakers. Exchanging information between healthcare providers and public health organizations, as well as developing an appropriate infrastructure for data exchange are critical for more effective monitoring of epidemic diseases. Clinical information systems should exchange information in real-time at a national level to effectively use dashboards at the public health level for monitoring and managing epidemic diseases and taking timely actions. Therefore, it is suggested that government examines the technical infrastructure (data architectures, structural and content standards, data exchange, security, and data resources) for appropriate data exchange between various clinical systems and the dashboard.

Strengths and limitations

The present study addresses the principles of designing public health dashboards and provides a comprehensive view of designing dashboards. In addition, this study investigated all aspects of public health dashboard design, including purposes, content, user interface, types of analysis, and infrastructure, and proposed sub-criteria for each criterion. However, the study needed further access to the full text of some articles and was restricted the search to articles published in English.

Although the scoping reviews are mainly designed to help policymakers to figure out the key concepts underpinning a research area and help them to have a clear working definitions, and/or the conceptual boundaries of a topic, results of this study need to be customized and tailored based on the local public health priorities of the countries through Focus Group Discussions (FGDs) and feasibility assessment panels before applying at the implementation phases.

Monitoring health, managing epidemics, and taking timely action requires real-time information exchange between clinical information systems and public health dashboards. Therefore, given the volume of data, the need for real-time monitoring and response in public health situations, and disease surveillance during epidemics, it is necessary to pay attention to dashboard design principles to achieve public health surveillance goals.

GIS Geographic Information System

KPI Key Performance Indicators

AIDS Acquired Immunodeficiency Syndrome

SOA Service-Oriented Architecture

FGDs Focus Group Discussions

Acknowledgements

Not Applicable.

Authors' contributions

The initial project idea was conceived, managed, and supervised by S.A. and R.R. All authors then contributed to the refinement of the idea, scoping review process, and to the formal analysis of the results. S.A., R.R., and S.D. conducted the initial and full-text screenings. S.A. and S.D. drafted the first version of the manuscript with significant revisions and feedback from R.R., H.A., and P.B. All authors contributed to discussions on the direction of the scoping review, and subsequent manuscript revisions and all agreed to the final manuscript version.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sector.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

Ethics approval and consent to participate

Not Applicable.

Consent for publication

Not Applicable.

Competing interests

The authors declare that they have no competing interests.

Jajosky RA, Groseclose SL. Evaluation of reporting timeliness of public health surveillance systems for infectious diseases. BMC Public Health. 2004;4:29. 10.1186/1471-2458-4-29.
Thacker SB, Qualters JR, Lee LM. Centers for Disease Control and Prevention. Public health surveillance in the United States: evolution and challenges. MMWR Suppl. 2012;61(3):39.
Turoff M, Chumer M, Van de Walle B, Yao X. The Design of a Dynamic Emergency Response Management. Annual Review of Network Management Security. J Inform Technol Theory Application (JITTA). 2006;5(4):1–35.
Ahn E, Liu N, Parekh T, Patel R, Baldacchino T, Mullavey T, et al. A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study. JMIR Public Health Surveill. 2021;7(3):e14837. 10.2196/14837.
McNabb SJ, Chungong S, Ryan M, Wuhib T, Nsubuga P, Alemu W, et al. Conceptual framework of public health surveillance and action and its application in health sector reform. BMC Public Health. 2002;2(1):1–9. 10.1186/1471-2458-2-2.
Caban JJ, Gotz D. Visual analytics in healthcare–opportunities and research challenges. J Am Med Inform Assoc. 2015;22(2):260–2. 10.1093/jamia/ocv006.
Chunara R, Freifeld CC, Brownstein JS. New technologies for reporting real-time emergent infections. Parasitology. 2012;139(14):1843–51. 10.1017/S0031182012000923.
Polgreen PM, Chen Y, Pennock DM, Nelson FD, Weinstein RA. Using internet searches for influenza surveillance. Clin Infect Dis. 2008;47(11):1443–8. 10.1086/593098.
Choi J, Cho Y, Shim E, Woo H. Web-based infectious disease surveillance systems and public health perspectives: a systematic review. BMC Public Health. 2016;16(1):1238. 10.1186/s12889-016-3893-0.
Smith KF, Goldberg M, Rosenthal S, Carlson L, Chen J, Chen C, et al. Global rise in human infectious disease outbreaks. J R Soc Interface. 2014;11(101):20140950. 10.1098/rsif.2014.0950.
Kostkova P, Garbin S, Moser J, Pan W. Integration and visualization public health dashboard: the medi + board pilot project. Proceedings of the 23rd International Conference on World Wide Web, April 7–11, 2014, Seoul, Korea.doi: 10.1145/2567948.2579276.
Seow HY, Sibley LM. Developing a dashboard to help measure and achieve the triple aim: a population-based cohort study. BMC Health Serv Res. 2014;14:363. 10.1186/1472-6963-14-363.
Dixon BE, Dearth S, Duszynski TJ, Grannis SJ. Dashboards Are Trendy, Visible Components of Data Management in Public Health: Sustaining Their Use After the Pandemic Requires a Broader View. Am J Public Health. 2022;112(6):900–3. 10.2105/AJPH.2022.306849.
Wilbanks BA, Langford PA. A review of dashboards for data analytics in nursing. Comput Inf Nurs. 2014;32(11):545–9. 10.1097/CIN.0000000000000106.
Almasi S, Rabiei R, Moghaddasi H, Vahidi-Asl M. Emergency Department Quality Dashboard; a Systematic Review of Performance Indicators, Functionalities, and Challenges. Arch Acad Emerg Med. 2021;9(1):e47. 10.22037/aaem.v9i1.1230.
Rabiei R, Almasi S. Requirements and challenges of hospital dashboards: a systematic literature review. BMC Med Inform Decis Mak. 2022;22(1):287. 10.1186/s12911-022-02037-8.
Dasgupta N, Kapadia F. The Future of the Public Health Data Dashboard. American Public Health Association. Am J Public Health. 2022;886–8. 10.2105/AJPH.2022.306871.
Kawamoto T, Mathers B. Key success factors for a performance dashboard. Inform Manage. 2007;17(7):20.
Cheng CK, Ip DK, Cowling BJ, Ho LM, Leung GM, Lau EH. Digital dashboard design using multiple data streams for disease surveillance with influenza surveillance as an example. J Med Internet Res. 2011 Oct;14(4):e85. 10.2196/jmir.1658.
Morgan MB, Branstetter BF 4th, Lionetti DM, Richardson JS, Chang PJ. The radiology digital dashboard: effects on report turnaround time. J Digit Imaging. 2008;21(1):50–8. 10.1007/s10278-007-9008-9.
Gao T, Kim MI, White D, Alm AM. Iterative user-centered design of a next generation patient monitoring system for emergency medical response. AMIA … Annual Symposium proceedings. AMIA Symposium, Washington, DC,USA. 2006:284–288. PMID: 17238348; PMCID: PMC1839401.
Cheng CK, Ip DK, Cowling BJ, Ho LM, Leung GM, Lau EH. Digital dashboard design using multiple data streams for disease surveillance with influenza surveillance as an example. J Med Internet Res. 2011;13(4):e85. 10.2196/jmir.1658.
Zhan FB, Lu Y, Giordano A, Hanford EJ. Geographic information system (GIS) as a tool for disease surveillance and environmental health research. Proceedings of ICSSSM '05. 2005 International Conference on Services Systems and Services Management, 2005., Chongquing, China, 2005, 1465–1470. doi: 10.1109/ICSSSM.2005.1500242.
Lechner B, Fruhling A. Towards public health dashboard design guidelines. 1st International Conference on HCI in Business, HCIB 2014 - Held as Part of 16th International Conference on Human-Computer Interaction, HCI International 2014 - Heraklion, Crete, Greeces; 2014: 49–59 Springer. doi:10.1007/978-3-319-07293-7_5.
Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32. 10.1080/1364557032000119616.
Tricco A, Lillie E, Zarin W, O’Brien K, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018;169(7):467–73. 10.7326/M18-0850.
Bos VLLC, Jansen T, Klazinga NS, Kringos DS. Development and Actionability of the Dutch COVID-19 Dashboard: Descriptive Assessment and Expert Appraisal Study. JMIR Public Health Surveill. 2021;7(10):e31161. 10.2196/31161.
Ivankovi D, Barbazza E, Bos V, Brito F, Jamieson Gilmore s, Jansen K. Features Constituting Actionable COVID-19 Dashboards: Descriptive Assessment and Expert Appraisal of 158 Public Web-Based COVID-19 Dashboards. J Med Internet Res. 2021;23(2):e25682. 10.2196/25682.
Barbazza E, Ivanković D, Davtyan K, Poldrugovac M, Yelgezekova Z, Willmington C, et al. The experiences of 33 national COVID-19 dashboard teams during the first year of the pandemic in the World Health Organization European Region: A qualitative study. Digit Health. 2022;8:20552076221121154. 10.1177/20552076221121154.
Jung E, Schnipper JL, Li Q, Linder JA, Rose AF, Li R et al. The coronary artery disease quality dashboard: a chronic care disease management tool in an electronic health record. AMIA Annu Symp Proc. 2007:999.
McLeod B, Zaver F, Avery C, Martin DP, Wang D, Jessen K, et al. Matching capacity to demand: a regional dashboard reduces ambulance avoidance and improves accessibility of receiving hospitals. Acad Emerg Med. 2010;17(12):1383–9. 10.1111/j.1553-2712.2010.00928.x.
Schweickert B, Feig M, Schneider M, Willrich N, Behnke M, Diaz LAP, et al. Antibiotic consumption in Germany: first data of a newly implemented web-based tool for local and national surveillance. J Antimicrob Chemother. 2018;73(12):3505–15. 10.1093/jac/dky345.
Concannon D, Herbst K, Manley E. Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings. JMIR Form Res. 2019;3(2):e11342. 10.2196/11342.
Gourevitch MN, Athens JK, Levine SE, Kleiman N, Thorpe LE. City-Level Measures of Health, Health Determinants, and Equity to Foster Population Health Improvement: The City Health Dashboard. Am J Public Health. 2019;109(4):585–92. 10.2105/AJPH.2018.304903.
Barone S, Chakhunashvili A, Comelli A. Building a statistical surveillance dashboard for COVID-19 infection worldwide. Qual Eng. 2020;32(4):754–63.
Lee MT, Lin FC, Chen ST, Hsu WT, Lin S, Chen TS, 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. 2020;44(2):54. 10.1007/s10916-019-1509-9.
Wissel BD, Van Camp PJ, Kouril M, Weis C, Glauser TA, White PS, et al. An interactive online dashboard for tracking COVID-19 in U.S. counties, cities, and states in real time. J Am Med Inform Assoc. 2020;27(7):1121–5. 10.1093/jamia/ocaa071.
Barbazza E, Ivankovi D, Wang S, Gilmore JK, Poldrugovac M, Willmington C, et al. Exploring Changes to the Actionability of COVID-19 Dashboards Over the Course of 2020 in the Canadian Context: Descriptive Assessment and Expert Appraisal Study. J Med Internet Res. 2021;23(8):e30200. 10.2196/30200.
Kaul S, Coleman C, Gotz D. A rapidly deployed, interactive, online visualization system to support fatality management during the coronavirus disease 2019 (COVID-19) pandemic. J Am Med Inform Assoc. 2020;27(12):1943–8. 10.1093/jamia/ocaa146.
Hutchinson-Colas JA, Balica A, Chervenak FA, Friedman D, Locke LS, Bachmann G, et al. New Jersey maternal mortality dashboard: an interactive social-determinants-of-health tool. J Perinat Med. 2022;51(2):188–96. 10.1515/jpm-2021-0673.
Rolfhamre P, Grabowska K, Ekdahl K. Implementing a public web based GIS service for feedback of surveillance data on communicable diseases in Sweden. BMC Infect Dis 2004 Jun 10;4:17. doi: 10.1186/1471-2334-4-17.
Linder JA, Jung E, Housman D, Eskin MS, Schnipper JL, Middleton B et al. The Acute Respiratory Infection Quality Dashboard: a performance measurement reporting tool in an electronic health record. AMIA Annu Symp Proc. 2007:1035.
Liu Y, Tsai Y, Chang C, Chang T, Kuo H, Chuang J. An Intelligent Web-Based Query System for Spatiotemporal Visualization of Infectious Diseases Surveillance Data in Taiwan. Int J Infect Dis. 2008;12:e189. 10.1016/j.ijid.2008.05.470.
Cheng CK, Ip DK, Cowling BJ, Ho LM, Leung GM, Lau EH. Digital dashboard design using multiple data streams for disease surveillance with influenza surveillance as an example. J Med Internet Res. 2011;13(4):e85. 10.2196/jmir.1658.
Dagliati A, Sacchi L, Tibollo V, Cogni G, Teliti M, Martinez-Millana A et al. A dashboard-based system for supporting diabetes care. J Am Med Inform Assoc. 2018 May 1;25(5):538–547. doi: 10.1093/jamia/ocx159.
Fareed N, Swoboda CM, Jonnalagadda P, Griesenbrock T, Gureddygari HR, Aldrich A. Visualizing Opportunity Index Data Using a Dashboard Application: A Tool to Communicate Infant Mortality-Based Area Deprivation Index Information. Appl Clin Inform. 2020;11(4):515–27. 10.1055/s-0040-1714249.
Meng Y, Zhang Y, Wang S, Wu S, Zhou H, Ke C, et al. Lessons Learned in the Development of a Web-based Surveillance Reporting System and Dashboard to Monitor Acute Febrile Illnesses in Guangdong and Yunnan Provinces, China, 2017–2019. Health Secur. 2020;18(S1):14–S22. 10.1089/hs.2019.0079.
Ahn E, Liu N, Parekh T, Patel R, Baldacchino T, Mullavey T, et al. A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study. JMIR Public Health Surveill. 2021;7(3):e14837. 10.2196/14837.
Lechner C, Rumpler M, Dorley MC, Li Y, Ingram A, Fryman H. Developing an Online Dashboard to Visualize Performance Data-Tennessee Newborn Screening Experience. Int J neonatal Screen. 2022;8(3):49. 10.3390/ijns8030049.
Cantrell J, Ganz O, Ilakkuvan V, Tacelosky M, Kreslake J, Moon-Howard J, et al. Implementation of a Multimodal Mobile System for Point-of-Sale Surveillance: Lessons Learned From Case Studies in Washington, DC, and New York City. JMIR Public Health Surveill. 2015;1(2):e20. 10.2196/publichealth.4191.
Kamadjeu R, Gathenji C. Designing and implementing an electronic dashboard for disease outbreaks response - Case study of the 2013–2014 Somalia Polio outbreak response dashboard. Pan Afr Med J. 2017;27(Suppl 3):22. 10.11604/pamj.supp.2017.27.3.11062.
Liabsuetrakul T, Prappre T, Pairot P, Oumudee N, Islam M. Development of a web-based epidemiological surveillance system with health system response for improving maternal and newborn health: Field-testing in Thailand. Health Inf J. 2017;23(2):109–23. 10.1177/1460458216628312.
Sprague AE, Dunn SI, Fell DB, Harrold J, Walker MC, Kelly S, et al. Measuring quality in maternal-newborn care: developing a clinical dashboard. J Obstet Gynaecol Can. 2013;35(1):29–38. 10.1016/s1701-2163(15)31045-8.
Nsabimana PA, Uzabakiriho B, Kagabo MD, Nduwayo J, Fu Q, Eng A, et al. Bringing Real-Time Geospatial Precision to HIV Surveillance Through Smartphones: Feasibility Study. JMIR Public Health Surveill. 2018;4(3):e11203. 10.2196/11203.
Florez H, Singh S. Online dashboard and data analysis approach for assessing COVID-19 case and death data. F1000Res. 2020;9:570. 10.12688/f1000research.24164.1.
Aldosery A, Musah A, Birjovanu G, Moreno G, Boscor A, Dutra L, et al. MEWAR: Development of a Cross-Platform Mobile Application and Web Dashboard System for Real-Time Mosquito Surveillance in Northeast Brazil. Front Public Health. 2021;9. 10.3389/fpubh.2021.754072.
Curriero FC, Wychgram C, Rebman AW, Corrigan AE, Kvit A, Shields T, et al. The Lyme and Tickborne Disease Dashboard: A map-based resource to promote public health awareness and research collaboration. PLoS ONE. 2021;16(12):e0260122. 10.1371/journal.pone.0260122.
Su W, Fu W, Kato K, Wong ZS, "Japan. LIVE Dashboard" for COVID-19: A Scalable Solution to Monitor Real-Time and Regional-Level Epidemic Case Data. Stud Health Technol Inform. 2021;286:21–5. 10.3233/SHTI210629.
Dong E, Ratcliff J, Goyea TD, Katz A, Lau R, Ng TK, et al. The Johns Hopkins University Center for Systems Science and Engineering COVID-19 Dashboard: data collection process, challenges faced, and lessons learned. Lancet Infect Dis. 2022;22(12):e370–6. 10.1016/S1473-3099(22)00434-0.
Iswari L, Fudholi D, Aditya S. Dashboarding the maternal and child health profiles for health supporting system. IOP Conference Series: Materials Science and Engineering, Volume 482, International Conference on Information Technology and Digital Applications (ICITDA 2018), Manila City, Philippines, 2018;482:012013.doi: 10.1088/1757-899X/482/1/012013.
Tchoualeu DD, Elmousaad HE, Osadebe LU, Adegoke OJ, Nnadi C, Haladu SA, et al. Use of a district health information system 2 routine immunization dashboard for immunization program monitoring and decision making, Kano State, Nigeria. Pan Afr Med J. 2021;40(Suppl 1):2. 10.11604/pamj.supp.2021.40.1.17313.
Udugama B, Kadhiresan P, Kozlowski HN, Malekjahani A, Osborne M, Li VYC, et al. Diagnosing COVID-19: The Disease and Tools for Detection. ACS Nano. 2020;14(4):3822–35. 10.1021/acsnano.0c02624.
Joshi A, Amadi C, Katz B, Kulkarni S, Nash D. A Human-Centered Platform for HIV Infection Reduction in New York: Development and Usage Analysis of the Ending the Epidemic (ETE) Dashboard. JMIR Public Health Surveill. 2017;3(4):e95. 10.2196/publichealth.8312.
Fu C, Lopes S, Mellor S, Aryal S, Sovannaroth S, Roca-Feltrer A. Experiences from developing and upgrading a web-based surveillance system for malaria elimination in Cambodia. JMIR Public Health Surveill. 2017;3(2):e30. 10.2196/publichealth.6942.
Damari B, Maghsoudlou M, Heidari A, Mirzaei H. Designing and Launching Health Observatory Dashboard of Islamic Republic of Iran. Med J Islamic Repub Iran. 2022;36:9. 10.47176/mjiri.36.9.
Dasgupta N, Kapadia F, The Future of the Public Health Data Dashboard. Am Public Health Association. 2022;886–8. 10.2105/AJPH.2022.306871.
Rasmussen R, editor. Electronic whiteboards in emergency medicine: A systematic review. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium; Miami Florida USA: Association for Computing Machinery, 2012: 483–492.doi: 10.1145/2110363.2110418.
Ghazisaeidi M, Safdari R, Torabi M, Mirzaee M, Farzi J, Goodini A. Development of Performance Dashboards in Healthcare Sector: Key Practical Issues. Acta Inf Med. 2015;23(5):317–21. 10.5455/aim.2015.23.317-321.
Marshall MN, Romano PS, Davies HT. How do we maximize the impact of the public reporting of quality of care? Int J Qual Health Care. 2004;16(Suppl 1):i57–63. 10.1093/intqhc/mzh013.
Klazinga N, Stronks K, Delnoij D, Verhoeff A. Indicators without a cause. Reflections on the development and use of indicators in health care from a public health perspective. Int J Qual Health Care. 2001;13(6):433–8. 10.1093/intqhc/13.6.433.
Hoekzema G, Abercrombie S, Carr S, Gravel JW, Hall KL, Kozakowski S, et al. Residency "dashboard": family medicine GME's step towards transparency and accountability? Ann Fam Med. 2010;8(5):470. 10.1370/afm.1181.
McLeod B, Zaver F, Avery C, Martin DP, Wang D, Jessen K, et al. Matching capacity to demand: a regional dashboard reduces ambulance avoidance and improves accessibility of receiving hospitals. Acad Emerg Med. 2010;17(12):1383–9. 10.1111/j.1553-2712.2010.00928.x.
Johnson CD, Miranda R, Osborn HH, Miller JM, Prescott SL, Aakre KT, et al. Designing a safer radiology department. AJR Am J Roentgenol. 2012;198(2):398–404. 10.2214/AJR.11.7234.
Peters E, Dieckmann N, Dixon A, Hibbard JH, Mertz CK. Less is more in presenting quality information to consumers. Med care Res review: MCRR. 2007;64(2):169–90. 10.1177/10775587070640020301.
Hibbard JH, Peters E. Supporting informed consumer health care decisions: data presentation approaches that facilitate the use of information in choice. Annu Rev Public Health. 2003;24:413–33. 10.1146/annurev.publhealth.24.100901.141005.
Serb C. Effective dashboards. What to measure and how to show it. Hospitals health networks. 2011;85(6):8 p following 40, 2–8 p following, 2.
Yi JS, Kang Ya, Stasko J, Jacko JA. Toward a Deeper Understanding of the Role of Interaction in Information Visualization. IEEE Trans Vis Comput Graph. 2007;13(6):1224–31. 10.1109/TVCG.2007.70515.
Engelbrecht L, Botha A, Alberts R. Information Visualisation View Design: Principles and Guidelines. The International Conference on Computer Science, Computer Engineering, and Social Media (CSCESM2014): Metropolitan College, Thessaloniki, Greece, 2014.
Steichen B, Fu B. Towards Adaptive Information Visualization - A Study of Information Visualization Aids and the Role of User Cognitive Style. Front Artif Intell. 2019;2:22doi. 10.3389/frai.2019.00022.
Mawad F, Trías M, Giménez A, Maiche A, Ares G. Influence of cognitive style on information processing and selection of yogurt labels: Insights from an eye-tracking study. Food Res Int. 2015;74:1–9. 10.1016/j.foodres.2015.04.023.
Ziemkiewicz C, Ottley A, Crouser RJ, Chauncey K, Su SL, Chang R. Understanding visualization by understanding individual users. IEEE Comput Graph Appl. 2012;32(6):88–94. 10.1109/MCG.2012.120.
Shneiderman B, editor. The eyes have it: a task by data type taxonomy for information visualizations. Proceedings 1996 IEEE Symposium on Visual Languages, Boulder, CO, USA; 1996;3–6.doi: 10.1109/VL.1996.545307.
Khan M, Khan S. Data and Information Visualization Methods, and Interactive Mechanisms: A Survey. Int J Comput Appl. 2011;34(1):1–14.
Dal C, Freitas C, Luzzardi P, Cava R, Winckler M, Pimenta M et al. Evaluating Usability of Information Visualization Techniques. Comput Sci. 2002.
Curriero FC, Wychgram C, Rebman AW, Corrigan AE, Kvit A, Shields T, et al. The Lyme and Tickborne Disease Dashboard: A map-based resource to promote public health awareness and research collaboration. PLoS ONE. 2021;16(12):e0260122. 10.1371/journal.pone.0260122.
Brehaut JC, Colquhoun HL, Eva KW, Carroll K, Sales A, Michie S, et al. Practice Feedback Interventions: 15 Suggestions for Optimizing Effectiveness. Ann Intern Med. 2016;164(6):435–41. 10.7326/M15-2248.
Ploem C, Suurmond J. Registering ethnicity for covid-19 research: is the law an obstacle? BMJ. 2020;370:m3367. 10.1136/bmj.m3367.
Qureshi QA, Shah B, Kundi GM, Nawaz A, Miankhel AK, Chishti KA, et al. Infrastructural barriers to e-health implementation in developing countries. Eur J Sustainable Dev. 2013;2(1):163–70. 10.14207/ejsd.2013.v2n1p163.
Cabitza F, Sarini M, Simone C, Telaro M, editors. When once is not enough: the role of redundancy in a hospital ward setting. Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work, ACM Press, New York; 2005: 158–167.doi: 10.1145/1099203.1099234.
Eckerson WW. Performance dashboards: measuring, monitoring, and managing your business. John Wiley & Sons; 2010.
Kroch E, Vaughn T, Koepke M, Roman S, Foster D, Sinha S, et al. Hospital boards and quality dashboards. J Patient Saf. 2006;2(1):10–9.
Papazoglou MP, Van Den Heuvel W-J. Service oriented architectures: approaches, technologies and research issues. VLDB J. 2007;16(3):389–415. 10.1007/s00778-007-0044-3.
Magnuson J, Dixon BE. Public health informatics and information systems. Springer; 2020.
Fadahunsi KP, O'Connor S, Akinlua JT, Wark PA, Gallagher J, Carroll C, et al. Information Quality Frameworks for Digital Health Technologies: Systematic Review. J Med Internet Res. 2021;23(5):e23479. 10.2196/23479.
Fadahunsi KP, Wark PA, Mastellos N, Neves AL, Gallagher J, Majeed A, et al. Assessment of Clinical Information Quality in Digital Health Technologies: International eDelphi Study. J Med Internet Res. 2022;24(12):e41889. 10.2196/41889.
van den Berg MJ, Kringos DS, Marks LK, Klazinga NS. The Dutch Health Care Performance Report: seven years of health care performance assessment in the Netherlands. Health Res Policy Syst. 2014;12:1. 10.1186/1478-4505-12-1.
McLellan S, Muddimer A, Peres SC. The effect of experience on System Usability Scale ratings. J usability Stud. 2012;7(2):56–67.
Li YC, Yen JC, Chiu WT, Jian WS, Syed-Abdul S, Hsu MH. Building a national electronic medical record exchange system - experiences in Taiwan. Comput Methods Programs Biomed. 2015;121(1):14–20. 10.1016/j.cmpb.2015.04.013.
Tøndel IA, editor. Visualization control for event-based public display systems used in a hospital setting. Proceedings of the 16th Nordic conference on Information Security Technology for Applications, Springer, Berlin, Heidelberg. 2011: 240–255. doi: 10.1007/978-3-642-29615-4_17.

No competing interests reported.

Download PDF

Journal Publication

published 06 Feb, 2024

Read the published version in BMC Public Health →

Editorial decision: Major revision
26 Sep, 2023
Reviews received at journal
21 Aug, 2023
Reviewers agreed at journal
21 Aug, 2023
Reviewers invited by journal
21 Aug, 2023
Editor assigned by journal
27 Jul, 2023
Editor invited by journal
13 Jul, 2023
Submission checks completed at journal
13 Jul, 2023
First submitted to journal
25 Jun, 2023

You are reading this latest preprint version

Developing Public Health Surveillance Dashboards: A Scoping Review on the Design Principles

Status:

Journal Publication

Version 1

Abstract

Background

Methodology

Results

Conclusion

Figures

Introduction

Overview of surveillance system

Dashboard in public health surveillance

Methodology

Study Design

Identifying the research question

Identification of relevant studies

Study selection

Charting data

Results

Characteristics of included studies

Principles of designing public health dashboards

Considering the objective and target users

Appropriate content

Interface

Interactive tools

Visual tools

Considering the types of data analysis and presentation

Infrastructure

Discussion

Main findings

Purpose and users

Content

Interface

Types of analysis and data presentation

Infrastructure

Implications for policy, practice, and future research

Strengths and limitations

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1