| Title |
RFID-based information visibility for hospital operations: exploring its positive effects using discrete event simulation
|
|---|---|
| Published in |
Health Care Management Science, October 2016
|
| DOI | 10.1007/s10729-016-9386-y |
| Pubmed ID | |
| Authors |
Daniel A. Asamoah, Ramesh Sharda, Howard N. Rude, Derek Doran |
| Abstract |
Long queues and wait times often occur at hospitals and affect smooth delivery of health services. To improve hospital operations, prior studies have developed scheduling techniques to minimize patient wait times. However, these studies lack in demonstrating how such techniques respond to real-time information needs of hospitals and efficiently manage wait times. This article presents a multi-method study on the positive impact of providing real-time scheduling information to patients using the RFID technology. Using a simulation methodology, we present a generic scenario, which can be mapped to real-life situations, where patients can select the order of laboratory services. The study shows that information visibility offered by RFID technology results in decreased wait times and improves resource utilization. We also discuss the applicability of the results based on field interviews granted by hospital clinicians and administrators on the perceived barriers and benefits of an RFID system. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 68 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 19% |
| Researcher | 7 | 10% |
| Student > Ph. D. Student | 7 | 10% |
| Other | 4 | 6% |
| Student > Doctoral Student | 4 | 6% |
| Other | 14 | 21% |
| Unknown | 19 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 17 | 25% |
| Computer Science | 9 | 13% |
| Business, Management and Accounting | 6 | 9% |
| Nursing and Health Professions | 5 | 7% |
| Decision Sciences | 3 | 4% |
| Other | 7 | 10% |
| Unknown | 21 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 December 2018.
All research outputs
#14,864,294
of 22,893,031 outputs
Outputs from Health Care Management Science
#167
of 285 outputs
Outputs of similar age
#192,058
of 319,855 outputs
Outputs of similar age from Health Care Management Science
#6
of 11 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 285 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.