Dashboard Meta-Model for Knowledge Management in Technological Ecosystem: A Case Study in Healthcare

Vázquez-Ingelmo, Andrea; García‐Holgado, Alicia; García‐Peñalvo, Francisco José; Therón, Roberto

doi:10.3390/proceedings2019031044

Cited by 11 publications

(12 citation statements)

References 36 publications

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“…There is also a broader ecosystem of medical authority to include entities that are not involved in their making. Recent studies have defined the healthcare "ecosystem" [95,128,137], including "physicians, patients, medical societies, hospital systems, software developers, the health information technology industry, and governmental regulatory agencies" [95]. However, these studies mainly focused on clinical practice rather than consumerfacing settings.…”

Section: 1mentioning

confidence: 99%

The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology

You

Kou

Ding

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Recently, consumer-facing health technologies such as Artificial Intelligence (AI)-based symptom checkers (AISCs) have sprung up in everyday healthcare practice. AISCs solicit symptom information from users and provide medical suggestions and possible diagnoses, a responsibility that people usually entrust with real-person authorities such as physicians and expert patients. Thus, the advent of AISCs begs a question of whether and how they transform the notion of medical authority in people's everyday healthcare practice. To answer this question, we conducted an interview study with thirty AISC users. We found that users assess the medical authority of AISCs using various factors including AISCs' automated decisions and interaction design patterns, associations with established medical authorities like hospitals, and comparisons with other health technologies. We reveal how AISCs are used in healthcare delivery, discuss how AI transforms conventional understandings of medical authority, and derive implications for designing AI-enabled health technology.CCS CONCEPTS • Human-centered computing~Human computer interaction (HCI)~Empirical studies in HCI

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Section: 1mentioning

confidence: 99%

The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology

You

Kou

Ding

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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“…This paper discusses the main factors that need to be accounted for in dashboard design and extends a dashboard meta-model that identifies core relationships and entities within this complex domain [18][19][20]. The previously developed meta-model aimed at formalizing a structure for defining information dashboards based on a set of factors, such as the data structure or users' goals, preferences, domain knowledge, visualization literacy, etc.…”

Section: Introductionmentioning

confidence: 99%

Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

et al. 2020

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Information dashboards are everywhere. They support knowledge discovery in a huge variety of contexts and domains. Although powerful, these tools can be complex, not only for the end-users but also for developers and designers. Information dashboards encode complex datasets into different visual marks to ease knowledge discovery. Choosing a wrong design could compromise the entire dashboard’s effectiveness, selecting the appropriate encoding or configuration for each potential context, user, or data domain is a crucial task. For these reasons, there is a necessity to automatize the recommendation of visualizations and dashboard configurations to deliver tools adapted to their context. Recommendations can be based on different aspects, such as user characteristics, the data domain, or the goals and tasks that will be achieved or carried out through the visualizations. This work presents a dashboard meta-model that abstracts all these factors and the integration of a visualization task taxonomy to account for the different actions that can be performed with information dashboards. This meta-model has been used to design a domain specific language to specify dashboards requirements in a structured way. The ultimate goal is to obtain a dashboard generation pipeline to deliver dashboards adapted to any context, such as the educational context, in which a lot of data are generated, and there are several actors involved (students, teachers, managers, etc.) that would want to reach different insights regarding their learning performance or learning methodologies.

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“…Vazquez-Ingelmo et al [35,37,38] conduct a series of studies and also proved the meta-model's usability in different disciplines when building a dashboard. For example, in [35], they propose a dashboard meta-model that mainly elaborates the user, the size of the container, and the components in a healthcare knowledge management case. In [38], the authors model interaction patterns by using XML specifications in a learning analytics case.…”

Section: Model-based Gui-based More Than 22 Featuresmentioning

confidence: 99%

Mod2Dash: A Framework for Model-Driven Dashboards Generation

Jiang,

Tran,

Babar

2022

Preprint

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The construction of an interactive dashboard involves deciding on what information to present and how to display it and implementing those design decisions to create an operational dashboard. Traditionally, a dashboard's design is implied in the deployed dashboard rather than captured explicitly as a digital artifact, preventing it from being backed up, version-controlled, and shared. Moreover, practitioners have to implement this implicit design manually by coding or configuring it on a dashboard platform. This paper proposes Mod2Dash, a software framework that enables practitioners to capture their dashboard designs as models and generate operational dashboards automatically from these models. The framework also provides a GUIdriven customization approach for practitioners to fine-tune the auto-generated dashboards and update their models. With these abilities, Mod2Dash enables practitioners to rapidly prototype and deploy dashboards for both operational and research purposes. We evaluated the framework's effectiveness in a case study on cyber security visualization for decision support. A proof-of-concept of Mod2Dash was employed to model and reconstruct 31 diverse real-world cyber security dashboards. A human-assisted comparison between the Mod2Dash-generated dashboards and the baseline dashboards shows a close matching, indicating the framework's effectiveness for real-world scenarios. CCS Concepts: • Human-centered computing → Visualization systems and tools; User centered design; Web-based interaction; • General and reference → Design; • Information systems → Data analytics.

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Dashboard Meta-Model for Knowledge Management in Technological Ecosystem: A Case Study in Healthcare

Cited by 11 publications

References 36 publications

The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology

The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology

Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

Mod2Dash: A Framework for Model-Driven Dashboards Generation

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