Abstract
This research develops a novel data-integrated simulation to evaluate nurse-patient assignments (SIMNA) based on a real data set provided by a northeast Texas hospital. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree models, data mining tools for prediction and classification, were used to develop five tree structures: (a) four classification trees from which transition probabilities for nurse movements are determined, and (b) a regression tree from which the amount of time a nurse spends in a location is predicted based on factors such as the primary diagnosis of a patient and the type of nurse. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Results obtained from SIMNA to evaluate nurse-patient assignments in Medical/Surgical unit I of the northeast Texas hospital are discussed.
| Original language | English |
|---|---|
| Pages (from-to) | 252-268 |
| Number of pages | 17 |
| Journal | Health Care Management Science |
| Volume | 12 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2009 Jul 1 |
| Externally published | Yes |
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Keywords
- Nurse workload
- Nurse-patient assignment
- Simulation
ASJC Scopus subject areas
- Health Professions(all)
- Medicine (miscellaneous)
Cite this
A data-integrated simulation model to evaluate nurse-patient assignments. / Sundaramoorthi, Durai; Chen, Victoria C P; Rosenberger, Jay M.; Kim, Seoung Bum; Buckley-Behan, Deborah F.
In: Health Care Management Science, Vol. 12, No. 3, 01.07.2009, p. 252-268.Research output: Contribution to journal › Article
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TY - JOUR
T1 - A data-integrated simulation model to evaluate nurse-patient assignments
AU - Sundaramoorthi, Durai
AU - Chen, Victoria C P
AU - Rosenberger, Jay M.
AU - Kim, Seoung Bum
AU - Buckley-Behan, Deborah F.
PY - 2009/7/1
Y1 - 2009/7/1
N2 - This research develops a novel data-integrated simulation to evaluate nurse-patient assignments (SIMNA) based on a real data set provided by a northeast Texas hospital. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree models, data mining tools for prediction and classification, were used to develop five tree structures: (a) four classification trees from which transition probabilities for nurse movements are determined, and (b) a regression tree from which the amount of time a nurse spends in a location is predicted based on factors such as the primary diagnosis of a patient and the type of nurse. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Results obtained from SIMNA to evaluate nurse-patient assignments in Medical/Surgical unit I of the northeast Texas hospital are discussed.
AB - This research develops a novel data-integrated simulation to evaluate nurse-patient assignments (SIMNA) based on a real data set provided by a northeast Texas hospital. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree models, data mining tools for prediction and classification, were used to develop five tree structures: (a) four classification trees from which transition probabilities for nurse movements are determined, and (b) a regression tree from which the amount of time a nurse spends in a location is predicted based on factors such as the primary diagnosis of a patient and the type of nurse. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Results obtained from SIMNA to evaluate nurse-patient assignments in Medical/Surgical unit I of the northeast Texas hospital are discussed.
KW - Nurse workload
KW - Nurse-patient assignment
KW - Simulation
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UR - http://www.scopus.com/inward/citedby.url?scp=70349307477&partnerID=8YFLogxK
U2 - 10.1007/s10729-008-9090-7
DO - 10.1007/s10729-008-9090-7
M3 - Article
VL - 12
SP - 252
EP - 268
JO - Health Care Management Science
JF - Health Care Management Science
SN - 1386-9620
IS - 3
ER -