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Optimizing nurse-staffing strategies for inpatient settings using a stochastic modeling approach
Eimanzadeh, Parisa
Eimanzadeh, Parisa
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2015-04-24
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Eimanzadeh, Parisa. Optimizing Nurse-staffing Strategies for Inpatient Settings Using a Stochastic Modeling Approach. --In Proceedings: 11th Annual Symposium on Graduate Research and Scholarly Projects. Wichita, KS: Wichita State University, p. 41
Abstract
The Health Resources and Services Administration (HRSA) projects a shortage of 5,900
registered nurses in the state of Kansas and a deficit of 36 percent nationwide by 2020. The
national nurse shortage along with rising patient acuity levels have led to an increase in nurse
workload, causing nurse workforce to experience high levels of burnout. There is growing
concern that nurse burnout could adversely impact the quality of care provided. Consistent
evidence from observational studies suggests that inadequate nurse staffing in hospitals and
heavy nurse workload threaten patient safety and quality of care. According to the Agency for
Healthcare Research and Quality (AHRQ), every additional registered nurse per patient is
associated with a risk reduction in hospital-related mortality by 9 and 16 percent in intensive care
and surgical units, respectively. To address this issue, hospitals often use recommended nurse-to patient ratios to staff different inpatient units. However, patients in a unit may have different
acuity levels based on the severity of care needed. This may impact the staffing needs of the unit
potentially rendering a fixed nurse-to-patient ratio ineffective. In this study, we quantify the
impact of patient acuity on staffing needs of an inpatient unit and develop nurse-staffing
strategies that take this effect into account. In particular, a stochastic model is proposed and
solved to quantify the trade-off between the staffing level of the inpatient unit and different
performance metrics such as the probability of excessive delays in providing care, which are
used to measure the extent of timely delivery of patient care. Healthcare managers can use the
information provided by the model to identify the staffing level that yields the desired trade-off
between all metrics for a given patient mix. The proposed model will capture the uncertainty
associated with the volume and duration of care for different acuity levels. This will be achieved
through the application of queueing theory and discrete-event simulation techniques. The results
obtained from applying the model to an inpatient unit demonstrate that patient acuity may greatly
impact the staffing needs and that fixed nurse-to-patient ratios can lead to inadequate staffing
levels.
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Presented to the 11th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Heskett Center, Wichita State University, April 24, 2015.
Research completed at Department of Industrial and Manufacturing Engineering, College of Engineering
Research completed at Department of Industrial and Manufacturing Engineering, College of Engineering
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Wichita State University. Graduate School
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GRASP
v.11
v.11