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Modeling the longitudinality of user acceptance of technology with an evidence-adaptive clinical decision support system

  • University of Michigan, Ann Arbor
  • Carnegie Mellon University

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents multiple innovations associated with an electronic health record system developed to support evidence-based medicine practice, and highlights a new construct, based on the technology acceptance model, to explain end users' acceptance of this technology through a lens of continuous behavioral adaptation and change. We show that this new conceptualization of technology acceptance reveals a richer level of detail of the developmental course whereby individuals adjust their behavior gradually to assimilate technology use. We also show that traditional models such as technology acceptance model (TAM) are not capable of delineating this longitudinal behavioral development process. Our TAM-derived analysis provides a lens through which we summarize the significance of this project to research and practice. We show that our application is an excellent exemplar of the "end-to-end" IS design realization process; it has drawn upon multiple disciplines to formulate and solve challenges in medical knowledge engineering, just-in-time provisioning of computerized decision-support advice, diffusion of innovation and individual users' technology acceptance, usability of human-machine interfaces in healthcare, and sociotechnical issues associated with integrating IT applications into a patient care delivery environment.

Original languageEnglish
Pages (from-to)444-453
Number of pages10
JournalDecision Support Systems
Volume57
Issue number1
DOIs
StatePublished - Jan 2014

ASJC Scopus Subject Areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

Keywords

  • Clinical decision support system
  • Clinical workflow
  • Electronic health records
  • Innovation adoption
  • Social network analysis
  • Usability assessments
  • User interface design

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