Evaluating interactive data systems

Rahman, Protiva; Jiang, Lilong; Nandi, Arnab

doi:10.1007/s00778-019-00589-2

Cited by 16 publications

(10 citation statements)

References 122 publications

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“…Although this work is based on our experiences, we have done our best to do a targeted interdisciplinary review that can serve as a guide to clinical data projects. Our work is related to previous surveys in visual analytics in health care [ 188 ] and interactive systems [ 137 ]. Our survey is unique in that it provides a taxonomy on designing systems for amplifying expertise and focuses on the clinical data pipeline.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, amplifying domain experts’ abilities in the analysis stage requires interactive data systems using a combination of statistical algorithms and compelling visualizations. Moreover, these systems need to follow design-study principles [ 137 ]. They need to allow interaction with domain experts for a needs assessment and an empirical evaluation to ensure that correct information is portrayed effectively.…”

Section: Challenges In the Data Pipelinementioning

confidence: 99%

“…For empiric antibiotic recommendation [ 196 ], this can involve allowing the health care provider to edit model features. Providing interaction comes at the cost of maintaining strict latency constraints since experts expect to see the results of interaction almost immediately [ 137 ]. Techniques for maintaining interactive performance include sampling [ 197 ] and predictive prefetching [ 198 ].…”

Section: Taxonomy Of Expertise Amplificationmentioning

confidence: 99%

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Amplifying Domain Expertise in Clinical Data Pipelines

2020

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Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.

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Section: Discussionmentioning

confidence: 99%

Section: Challenges In the Data Pipelinementioning

confidence: 99%

Section: Taxonomy Of Expertise Amplificationmentioning

confidence: 99%

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Amplifying Domain Expertise in Clinical Data Pipelines

2020

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“…Data are at the center of scientific discovery as well as of scholarship and scholarly communication (Borgman, 2015). The growing role of data is also witnessed by the ever-increasing importance of data science and related research fields concerning the search (Chapman et al, 2020), provenance (Cheney, Chiticariu & Tan, 2009), citation (Silvello, 2018), re-use (Wynholds et al, 2012), and exploration (Rahman, Jiang & Nandi, 2020) of data.…”

Section: Introductionmentioning

confidence: 99%

Search, access, and explore life science nanopublications on the Web

Giachelle¹,

Dosso²,

Silvello³

2021

PeerJ Computer Science

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Nanopublications are Resource Description Framework (RDF) graphs encoding scientific facts extracted from the literature and enriched with provenance and attribution information. There are millions of nanopublications currently available on the Web, especially in the life science domain. Nanopublications are thought to facilitate the discovery, exploration, and re-use of scientific facts. Nevertheless, they are still not widely used by scientists outside specific circles; they are hard to find and rarely cited. We believe this is due to the lack of services to seek, find and understand nanopublications’ content. To this end, we present the NanoWeb application to seamlessly search, access, explore, and re-use the nanopublications publicly available on the Web. For the time being, NanoWeb focuses on the life science domain where the vastest amount of nanopublications are available. It is a unified access point to the world of nanopublications enabling search over graph data, direct connections to evidence papers, and scientific curated databases, and visual and intuitive exploration of the relation network created by the encoded scientific facts.

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“…We then engaged in a design improvement cycle in collaboration with software programmers, which resulted in agreeing to implement some of the recommendations as well as additional enhancements that went beyond the initial ideas in a more integrated approach. Finally, we used the revised interface to elicit priorities for translation of useful concepts into practice in the hospital setting in the emergency department, intensive care unit, or infectious diseases based off a user-centric interactive approach (Rahman et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Usefulness and Usability of a Clinical Decision Support Prototype for Antibiotic Stewardship

Patterson

DiLoreto

Vanam

et al. 2020

Proceedings of the International Symposium on Human Factors and

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Human factors engineering can enhance software usefulness and usability. We describe a multi-method approach to improve clinical decision support (CDS) for antibiotic stewardship. We employed a heuristic review to generate recommendations to improve the usability of a prototype CDS to support empiric antibiotic prescribing in the hospital setting. We then engaged in a design improvement cycle in collaboration with software programmers, which resulted in additional enhancements to our prototype. Finally, we used the revised prototype during three walkthrough demonstration interviews with physician and pharmacist subject matter experts. These walkthrough interviews generated recommendations to improve the interface, functionality, and tailoring for groups of users. We discuss common elements of the recommendations for models for using clinical decision support in general.

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Evaluating interactive data systems

Cited by 16 publications

References 122 publications

Amplifying Domain Expertise in Clinical Data Pipelines

Amplifying Domain Expertise in Clinical Data Pipelines

Search, access, and explore life science nanopublications on the Web

Enhancing Usefulness and Usability of a Clinical Decision Support Prototype for Antibiotic Stewardship

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