Optimal Staffing Policy and Technology Investment in a Telemedicine Hospital
We study a specialty hospital providing traditional face-to-face consultations by experts and telemedicine services by tele-specialists. As accuracy of diagnosis and treatment by tele-specialists are paramount in such a setting (unlike call center management), our main focus is to determine the opti...
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sg-smu-ink.lkcsb_research-21072010-09-23T06:24:04Z Optimal Staffing Policy and Technology Investment in a Telemedicine Hospital Tarakci, Hakan Ozdemir, Zafar MOOSA, Sharafali We study a specialty hospital providing traditional face-to-face consultations by experts and telemedicine services by tele-specialists. As accuracy of diagnosis and treatment by tele-specialists are paramount in such a setting (unlike call center management), our main focus is to determine the optimal investment level in telemedicine technology with the trade off being between accuracy/quality and cost. Using a heuristic proposed in queuing theory, we provide the optimal investment in telemedicine technology together with the staffing policy, considering the various cost components, including staffing, technology investment, incorrect treatment, and waiting. The model also incorporates buy-in by the patients in the form of the arrival (show-up) rate dependent on the technology level established. We find that under certain conditions the hospital should not invest in telemedicine. Finally, we provide the optimal tele-specialist policy of the ratio of patients to treat via telemedicine and to refer to the face-to-face consultation. Our model also suggests that a policy of treating all patients via telemedicine is never optimal. 2009-01-01T08:00:00Z text https://ink.library.smu.edu.sg/lkcsb_research/1108 info:doi/10.1016/j.dss.2008.08.001 Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University Business Administration, Management, and Operations |
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Business Administration, Management, and Operations Tarakci, Hakan Ozdemir, Zafar MOOSA, Sharafali Optimal Staffing Policy and Technology Investment in a Telemedicine Hospital |
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We study a specialty hospital providing traditional face-to-face consultations by experts and telemedicine services by tele-specialists. As accuracy of diagnosis and treatment by tele-specialists are paramount in such a setting (unlike call center management), our main focus is to determine the optimal investment level in telemedicine technology with the trade off being between accuracy/quality and cost. Using a heuristic proposed in queuing theory, we provide the optimal investment in telemedicine technology together with the staffing policy, considering the various cost components, including staffing, technology investment, incorrect treatment, and waiting. The model also incorporates buy-in by the patients in the form of the arrival (show-up) rate dependent on the technology level established. We find that under certain conditions the hospital should not invest in telemedicine. Finally, we provide the optimal tele-specialist policy of the ratio of patients to treat via telemedicine and to refer to the face-to-face consultation. Our model also suggests that a policy of treating all patients via telemedicine is never optimal. |
format |
text |
author |
Tarakci, Hakan Ozdemir, Zafar MOOSA, Sharafali |
author_facet |
Tarakci, Hakan Ozdemir, Zafar MOOSA, Sharafali |
author_sort |
Tarakci, Hakan |
title |
Optimal Staffing Policy and Technology Investment in a Telemedicine Hospital |
title_short |
Optimal Staffing Policy and Technology Investment in a Telemedicine Hospital |
title_full |
Optimal Staffing Policy and Technology Investment in a Telemedicine Hospital |
title_fullStr |
Optimal Staffing Policy and Technology Investment in a Telemedicine Hospital |
title_full_unstemmed |
Optimal Staffing Policy and Technology Investment in a Telemedicine Hospital |
title_sort |
optimal staffing policy and technology investment in a telemedicine hospital |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2009 |
url |
https://ink.library.smu.edu.sg/lkcsb_research/1108 |
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1770569803886493696 |