A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility
We consider the work flow in a medical teaching facility, examining the process that involves an initial patient exam by a resident physician, a subsequent conference between the resident and the attending physician, and the attending physician's visit with the patient. We create an analytical...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98764 http://hdl.handle.net/10220/17654 |
| _version_ | 1811694437077614592 |
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| author | Dobson, Gregory Lee, Hsiao-Hui Sainathan, Arvind Tilson, Vera |
| author2 | Nanyang Business School |
| author_facet | Nanyang Business School Dobson, Gregory Lee, Hsiao-Hui Sainathan, Arvind Tilson, Vera |
| author_sort | Dobson, Gregory |
| collection | NTU |
| description | We consider the work flow in a medical teaching facility, examining the process that involves an initial patient exam by a resident physician, a subsequent conference between the resident and the attending physician, and the attending physician's visit with the patient. We create an analytical model of a tandem queue with finite buffer space to analyze the impact of different work prioritization policies on the throughput and the flow time of patients in the facility—measures that influence both the facility's finances and patients' satisfaction. We derive throughput-optimal policies and show that these policies involve dynamic batching. This finding is interesting because our model does not include any setup times, and setup times normally imply batching; rather it is the uncertain service times and the requirement for simultaneous service in the conference step that make batching optimal. The optimal dynamic batching policy is complex, so we consider a simpler static batching policy. We show that, in systems with limited buffer space, large batches can sometimes degrade efficiency by simultaneously increasing flow time and decreasing throughput. However, in general, both flow time and throughput increase with batch size. Flow time increases at a faster rate than throughput, so hospital management may want to consider what batch size is optimal given the value it places on the two measures. |
| first_indexed | 2024-10-01T07:07:33Z |
| format | Journal Article |
| id | ntu-10356/98764 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:07:33Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/987642023-05-19T06:44:43Z A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility Dobson, Gregory Lee, Hsiao-Hui Sainathan, Arvind Tilson, Vera Nanyang Business School DRNTU::Business::Operations management We consider the work flow in a medical teaching facility, examining the process that involves an initial patient exam by a resident physician, a subsequent conference between the resident and the attending physician, and the attending physician's visit with the patient. We create an analytical model of a tandem queue with finite buffer space to analyze the impact of different work prioritization policies on the throughput and the flow time of patients in the facility—measures that influence both the facility's finances and patients' satisfaction. We derive throughput-optimal policies and show that these policies involve dynamic batching. This finding is interesting because our model does not include any setup times, and setup times normally imply batching; rather it is the uncertain service times and the requirement for simultaneous service in the conference step that make batching optimal. The optimal dynamic batching policy is complex, so we consider a simpler static batching policy. We show that, in systems with limited buffer space, large batches can sometimes degrade efficiency by simultaneously increasing flow time and decreasing throughput. However, in general, both flow time and throughput increase with batch size. Flow time increases at a faster rate than throughput, so hospital management may want to consider what batch size is optimal given the value it places on the two measures. 2013-11-15T02:51:59Z 2019-12-06T19:59:26Z 2013-11-15T02:51:59Z 2019-12-06T19:59:26Z 2012 2012 Journal Article Gregory, D., Lee, H. H., Sainathan, A., & Tilson, V. (2012). A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility. Manufacturing and service operations management, 14(4). https://hdl.handle.net/10356/98764 http://hdl.handle.net/10220/17654 10.1287/msom.1120.0380 en Manufacturing and service operations management |
| spellingShingle | DRNTU::Business::Operations management Dobson, Gregory Lee, Hsiao-Hui Sainathan, Arvind Tilson, Vera A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility |
| title | A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility |
| title_full | A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility |
| title_fullStr | A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility |
| title_full_unstemmed | A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility |
| title_short | A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility |
| title_sort | queueing model to evaluate the impact of patient batching on throughput and flow time in a medical teaching facility |
| topic | DRNTU::Business::Operations management |
| url | https://hdl.handle.net/10356/98764 http://hdl.handle.net/10220/17654 |
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