| Title |
Keeping pace with the ebbs and flows in daily nursing home operations
|
|---|---|
| Published in |
Health Care Management Science, March 2018
|
| DOI | 10.1007/s10729-018-9442-x |
| Pubmed ID | |
| Authors |
René Bekker, Dennis Moeke, Bas Schmidt |
| Abstract |
Nursing homes are challenged to develop staffing strategies that enable them to efficiently meet the healthcare demand of their residents. In this study, we investigate how demand for care and support fluctuates over time and during the course of a day, using demand data from three independent nursing home departments of a single Dutch nursing home. This demand data is used as input for an optimization model that provides optimal staffing patterns across the day. For the optimization we use a Lindley-type equation and techniques from stochastic optimization to formulate a Mixed-Integer Linear Programming (MILP) model. The impact of both the current and proposed staffing patterns, in terms of waiting time and service level, are investigated. The results show substantial improvements for all three departments both in terms of average waiting time as well as in 15 minutes service level. Especially waiting during rush hours is significantly reduced, whereas there is only a slight increase in waiting time during non-rush hours. |
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 % |
|---|---|---|
| Canada | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 20% |
| Student > Master | 4 | 20% |
| Student > Ph. D. Student | 2 | 10% |
| Student > Bachelor | 1 | 5% |
| Unspecified | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 6 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 4 | 20% |
| Business, Management and Accounting | 3 | 15% |
| Nursing and Health Professions | 2 | 10% |
| Computer Science | 1 | 5% |
| Unspecified | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 7 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 18 September 2019.
All research outputs
#13,650,377
of 23,313,051 outputs
Outputs from Health Care Management Science
#145
of 287 outputs
Outputs of similar age
#171,746
of 333,428 outputs
Outputs of similar age from Health Care Management Science
#2
of 3 outputs
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So far Altmetric has tracked 287 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 49th percentile – i.e., 49% of its peers scored the same or lower than it.
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