IS in Healthcare

Track Description

Health information systems (HIS) are a broad class of applications that use a variety of advanced information, mobile, social media and wearable technologies to collect, store, manage, process and transmit health information. HIS can aid many interdependent stakeholders, such as patients, care providers, payers, policymakers, technology vendors, platform creators and researchers. Digitization enabled through HIS has the potential to deliver better, cost-efficient and patient-centric healthcare through widespread sharing of authorized data, process transformation and proactive involvement by patients to sustain their own well-being. There is early evidence that HIS, in isolation and in combination, impact care provision and administrative processes, enhance care quality, reduce healthcare costs and facilitate information sharing across organizational boundaries. However, researchers have not found consistent results and the context of examination is still central in understanding research findings. Additionally, national and regional governments worldwide have introduced several initiatives around technology use, data integration, privacy, payment models and access to care, and the commercial sector has launched several innovations in the consumer sector, which make it easier to track and consolidate individual-level data. Increasing standardization in the healthcare industry and the widespread use of HIS among health care providers, payers and consumers have enabled the creation of large datasets, which lend themselves well to predictive modeling.
Track Chairs
Lars Mathiassen, Georgia State University,
Abhay Mishra, Iowa State University,
Sudha Ram, University of Arizona,

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Monday, December 14th

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Carolin Auschra, Freie Universität Berlin
Stefan Klein, University of Muenster

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Mahdi Javadi, Kaiserslautern University of Technology
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Qidi Xing, University of Oklahoma
Shaila M. Miranda, University of Oklahoma
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Arin Brahma, Loyola Marymount University
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Christian Meske, Freie Universität Berlin
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Using NLP to Extract Predicate History from Medical Device Approvals

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Alexander Everhart, University of Minnesota
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Lucas Baier, Karlsruhe Institute of Technology
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Work Design in Healthcare Artificial Intelligence Applications: The Role of Advice Provision Timing

Jiamin Yin, National University of Singapore
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