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Business Analytics Assists Transitioning Traditional Medicine to Telemedicine at Virtual Radiologic

Körpeoğlu, E; Kurtz, Z; Kılınç-Karzan, F; Kekre, S; Basu, PA; (2014) Business Analytics Assists Transitioning Traditional Medicine to Telemedicine at Virtual Radiologic. Interfaces , 44 (4) pp. 393-410. 10.1287/inte.2014.0752. Green open access

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Abstract

Virtual Radiologic (vRad), the largest teleradiology company in the United States, faces the difficult problem of matching more than 400 radiologists with time-varying and seasonal demand. In addition to the constraints that traditional medical facilities face, vRad is subject to supply and demand requirements that are unique to the teleradiology business environment. In this paper, we present a forecasting and capacity-planning model that more accurately assesses demand and plans system capacity to provide better service to vRad’s customers. We discuss the underlying reasons for improvement and quantify the impact on vRad’s entire system. We explain managerial insights that will help both vRad and other companies in the service sector with similar service-response requirements and demand patterns. We also highlight the implementation challenges our teams faced.

Type: Article
Title: Business Analytics Assists Transitioning Traditional Medicine to Telemedicine at Virtual Radiologic
Open access status: An open access version is available from UCL Discovery
DOI: 10.1287/inte.2014.0752
Publisher version: https://doi.org/10.1287/inte.2014.0752
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: business analytics, capacity planning, forecasting, optimization, healthcare industry
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management
URI: https://discovery.ucl.ac.uk/id/eprint/10083818
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