Beyond Information Design: Designing Health Care Dashboards for Evidence-Driven Decision-Making

With health care systems experiencing a deluge of exponentially expanding performance measures,[1] [2] dashboards (a graphical report of essential data relevant to a particular objective or process, such as the World Health Organization's Coronavirus Dashboard)[3] have become common to efficiently consolidate monitoring large numbers of clinical performance and related health care measures across multiple domains. As a result, overcrowded, ineffective dashboards abound.[4]

Much has been written about how to design more visually pleasing, navigable, and interpretable dashboards (known in human factors research as “information design”). Information design, however, assumes dashboard designers know what information needs to be presented and to whom. Further, dashboards assume a certain level of numeracy and graph literacy of their consumers to be effective.[5] Various frameworks to aid in information design have been proposed,[6] such as an ontology of performance summary display[7] and the BEhavior and Acceptance fRamework (BEAR)[8] for the design of clinical decision support systems, which consolidates the propositions of four frameworks (including, e.g., the Human, Organization, and Technology-fit framework [HOT-fit][9] and the Unified Theory of Acceptance and Use of Technology [UTAUT][10]) and 10 literature reviews to provide a comprehensive view of the factors needed in successfully designing and implementing clinical decision support systems and information dashboards. Frameworks such as these provide a comprehensive panorama of the domain of information design and implementation that researchers can use for expanding generalizable knowledge; however, such frameworks can be overwhelming and unwieldy for the field designer trying to solve a concrete problem for a health care practice by means of a dashboard. What is needed is a straightforward procedure or set of rules for identifying the content to be presented on a dashboard that will yield the most benefit for the problem in question. The literature on performance metric development can yield useful insight on this matter.

Hysong et al[11] proposed asking three simple questions to help decision-makers select appropriate quality improvement and performance metrics:

1. What is the purpose of the metric?

2. Who is the consumer (or audience) of the metric?

3. Who is the intended subject, that is, who is being evaluated in this metric?

Just as lacking clear answers to these questions can hinder appropriate performance metric generation and selection, these three factors—unclear purpose, unclear or wrong consumer, and wrong subject—can pose barriers to successful dashboard design and implementation. Below we describe these in more detail and present a case example illustrating the use and benefits of this framework for dashboard design.

Publication History

Received: 01 November 2022

Accepted: 30 March 2023

Accepted Manuscript online:
04 April 2023

Article published online:
14 June 2023

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