AI Bibliography
AI Bibliography |
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Xu, S., Rogers, T., Fairweather, E., Glenn, A., Curran, J., & Curcin, V. (2018). Application of data provenance in healthcare analytics software: Information visualisation of user activities. AMIA Summits on Translational Science Proceedings, 2018, 263. |
| Resource type: Journal Article BibTeX citation key: Xu2018 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, Engineering, General, Mathematics, Medical science Subcategories: Analytics, Big data, Decision making, Human decisionmaking, Human factors engineering, Informatics, Machine learning, Psychology of human-AI interaction Creators: Curcin, Curran, Fairweather, Glenn, Rogers, Xu Publisher: Collection: AMIA Summits on Translational Science Proceedings |
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Data provenance is a technique that describes the history of digital objects. In health data settings, it can be used to deliver auditability and transparency, and to achieve trust in a software system. However, implementing data provenance in analytics software at an enterprise level presents a different set of challenges from the research environments where data provenance was originally devised. In this paper, the challenges of reporting provenance information to the user is presented. Provenance captured from analytics software can be large and complex and visualizing a series of tasks over a long period can be overwhelming even for a domain expert, requiring visual aggregation mechanisms that fit with complex human cognitive activities involved in the process. This research studied how provenance-based reporting can be integrated into a health data analytics software, using the example of Atmolytics visual reporting tool.
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