Using the work system theory to bring big data analytics to the inpatient point of care

Tsipi Heart, Ofir Ben-Assuli, Nir Shlomo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Big data analytic tools can significantly improve clinical decisions, yet they are difficult to use at the point of care. A big data tool should show strong predictive power to be useful at the point of care, as well as harmoniously integrate within the clinical processes. In this paper we report on a prediction tool for CHF patients' readmission or death, and show its strength. We chose the WST and the WSLC as the theory underlying the design and implementation processes aimed at bringing such a tool to the point of care. In future research, we will further improve our prediction tool by adding classification algorithms, and elaborate on relationships among the WST elements, for the WS to be in harmony. We hypothesize that the methods employed for the tool development and the lessons derived from the WST adaptation would be generalizable to similar medical clinics.

Original languageEnglish
Title of host publicationInternational Conference on Information Systems 2018, ICIS 2018
PublisherAssociation for Information Systems
ISBN (Electronic)9780996683173
StatePublished - 1 Jan 2018
Externally publishedYes
Event39th International Conference on Information Systems, ICIS 2018 - San Francisco, United States
Duration: 13 Dec 201816 Dec 2018

Publication series

NameInternational Conference on Information Systems 2018, ICIS 2018

Conference

Conference39th International Conference on Information Systems, ICIS 2018
Country/TerritoryUnited States
CitySan Francisco
Period13/12/1816/12/18

Keywords

  • Congestive heart failure
  • Data analytics
  • Predictive analytics
  • Work system theory

ASJC Scopus subject areas

  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences
  • Applied Mathematics

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