ConText: Supporting the Pursuit and Management of Evidence in Text-based Reporting Systems

Tabassum Kakar, Xiao Qin, Elke Rundensteiner, Lane Harrison, Sanjay Sahoo, Suranjan De, Thang La

2022

Abstract

Instance-based Incident Analysis (IIA) – a labor intensive and error-prone task – requires analysts to review text-based reports of incidents, where each may be evidence of a larger problem that requires regulatory action. Given the rise of reporting systems in many organizations, there is a need to explore tools that may aid IIA analysts in exploring, evaluating, and generalizing findings across a large set of independently produced reports in a unified workflow – currently not supported by existing tools. In this work, we contribute a design study conducted in collaboration with Pharmacovigilance experts at the US Food and Drug Administration. Following a series of interviews and discussions focused on workflows and toolsets, we develop a prototype, ConText, which combines domain-informed computational methods with interactive visual displays to support evidence identification, collection, and management for IIA. We evaluate ConText via case studies and follow-up semi-structured interviews, depicting its effectiveness in performing IIA tasks of evidence collection and monitoring. We discuss insights derived from the design and evaluation of ConText that may be valuable for designing future interactive analytic systems for life-critical IIA workflows.

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Paper Citation


in Harvard Style

Kakar T., Qin X., Rundensteiner E., Harrison L., Sahoo S., De S. and La T. (2022). ConText: Supporting the Pursuit and Management of Evidence in Text-based Reporting Systems. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, ISBN 978-989-758-555-5, pages 38-50. DOI: 10.5220/0010768500003124


in Bibtex Style

@conference{ivapp22,
author={Tabassum Kakar and Xiao Qin and Elke Rundensteiner and Lane Harrison and Sanjay Sahoo and Suranjan De and Thang La},
title={ConText: Supporting the Pursuit and Management of Evidence in Text-based Reporting Systems},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,},
year={2022},
pages={38-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010768500003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,
TI - ConText: Supporting the Pursuit and Management of Evidence in Text-based Reporting Systems
SN - 978-989-758-555-5
AU - Kakar T.
AU - Qin X.
AU - Rundensteiner E.
AU - Harrison L.
AU - Sahoo S.
AU - De S.
AU - La T.
PY - 2022
SP - 38
EP - 50
DO - 10.5220/0010768500003124