Adequate nurse staffing and managerial support foster better patient care and reduce nurse dissatisfaction and burnout

2003 ◽  
Keyword(s):  
Patient Care ◽  
Nurse Staffing ◽  
Managerial Support ◽  
Nurse Dissatisfaction

Surge or submerge? Predicting nurse staffing, medical hold capacity, and maximal patient care capabilities in the combat environment

2019 ◽  
Vol 87 ◽  
pp. S152-S158
Author(s):  
Jessica Cassidy ◽  
Dana Munari ◽  
Damon Forbes ◽  
Kyle Remick ◽  
Matthew J. Martin
Keyword(s):  
Patient Care ◽  
Nurse Staffing ◽  
Combat Environment

scholarly journals Right cot, right place, right time: improving the design and organisation of neonatal care networks – a computer simulation study

2015 ◽  
Vol 3 (20) ◽  
pp. 1-128 ◽  
Author(s):  
Michael Allen ◽  
Anne Spencer ◽  
Andy Gibson ◽  
Justin Matthews ◽  
Alex Allwood ◽  
...  
Keyword(s):  
Patient Care ◽  
National Level ◽  
Nurse Staffing ◽  
Network Capacity ◽  
Neonatal Care ◽  
Neonatal Unit ◽  
Good Precision ◽  
Spare Capacity ◽  
Home Location ◽  
Travel Distances

BackgroundThere is a tension in many health-care services between the expertise and efficiency that comes with centralising services and the ease of access for patients. Neonatal care is further complicated by the organisation of care into networks where different hospitals offer different levels of care and where capacity across, or between, networks may be used when local capacity is exhausted.ObjectivesTo develop a computer model that could mimic the performance of a neonatal network and predict the effect of altering network configuration on neonatal unit workloads, ability to meet nurse staffing guidelines, and distance from the parents’ home location to the point of care. The aim is to provide a model to assist in planning of capacity, location and type of neonatal services.DesignDescriptive analysis of a current network, economic analysis and discrete event simulation. During the course of the project, two meetings with parents were held to allow parent input.SettingThe Peninsula neonatal network (Devon and Cornwall) with additional work extending to the Western network.Main outcome measuresAbility to meet nurse staffing guidelines, cost of service provision, number and distance of transfers, average travel distances for parents, and numbers of parents with an infant over 50 km from home.Data sourcesAnonymised neonatal data for 7629 infants admitted into a neonatal unit between January 2011 and June 2013 were accessed from Badger patient care records. Nurse staffing data were obtained from a daily ring-around audit. Further background data were accessed from NHS England general practitioner (GP) Practice Profiles, Hospital Episode Statistics, Office for National Statistics and NHS Connecting for Health. Access to patient care records was approved by the Research Ethics Committee and the local Caldicott Guardian at the point of access to the data.ResultsWhen the model was tested against a period of data not used for building the model, the model was able to predict the occupancy of each hospital and care level with good precision (R2 > 0.85 for all comparisons). The average distance from the parents’ home location (GP location used as a surrogate) was predicted to within 2 km. The number of transfers was predicted to within 2%. The model was used to forecast the effect of centralisation. Centralisation led to reduced nurse requirements but was accompanied by a significant increase in parent travel distances. Costs of nursing depend on how much of the time nursing guidelines are to be met, rising from £4500 per infant to meet guidelines 80% of the time, to £5500 per infant to meet guidelines 95% of the time. Using network capacity, rather than local spare capacity, to meet local peaks in workloads can reduce the number of nurses required, but the number of transfers and the travel distance for parents start to rise significantly above ≈ 70% network capacity utilisation.ConclusionsWe have developed a model that predicts performance of a neonatal network from the perspectives of both the service provider and the parents of infants in care.Future workApplication of the model at a national level.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

Nurse Staffing and Patient Care Costs in Acute Inpatient Nursing Units

Medical Care ◽  
2011 ◽  
Vol 49 (8) ◽  
pp. 708-715 ◽  
Author(s):  
Yu-Fang Li ◽  
Edwin S. Wong ◽  
Anne E. Sales ◽  
Nancy D. Sharp ◽  
Jack Needleman ◽  
...  
Keyword(s):  
Patient Care ◽  
Nurse Staffing ◽  
Inpatient Nursing ◽  
Care Costs

NURSE STAFFING UNDER DEMAND UNCERTAINTY TO REDUCE COSTS AND ENHANCE PATIENT SAFETY

2014 ◽  
Vol 31 (01) ◽  
pp. 1450005 ◽  
Author(s):  
ASHLEY DAVIS ◽  
SANJAY MEHROTRA ◽  
JANE HOLL ◽  
MARK S. DASKIN
Keyword(s):  
Patient Safety ◽  
Intensive Care ◽  
Patient Care ◽  
Demand Uncertainty ◽  
Nurse Staffing ◽  
Cost Savings ◽  
Staffing Level ◽  
Safe Patient Care ◽  
Demand Distribution ◽  
Using Data

Hospitals must maintain safe nurse-to-patient ratios in patient care units to offer adequate and safe patient care. Since the patient demand is highly variable, during high patient demand periods temporary or overtime nurses are hired to ensure safe nurse-to-patient ratios. These overtime nurses incur higher expense, and are often less effective. We study the problem of permanent nurse staffing level estimation under demand uncertainty as a newsvendor model. Our models are based on limited moment information of the demand distribution. Additionally, we introduce the use of asymmetric cost functions representing overstaffing and understaffing nursing costs. Findings using data from the general surgery and intensive care units at hospitals in Chicago, IL and Augusta, GA are presented. Computational results based on publically available cost data show that 3.1% and 7.3% annual cost savings result by introducing salvage value and newsvendor optimization in intensive care and general care units respectively. This new staffing scheme also improves patient safety as shifts are staffed with more permanent nurses.

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