Nurse-patient assignment models considering patient acuity metrics and nurses' perceived workload.
Patient classification systems (PCSs) are commonly used in nursing units to assess how many nursing care hours are needed to care for patients. These systems then provide staffing and nurse-patient assignment recommendations for a given patient census based on these acuity scores. Our hypothesis is that such systems do not accurately capture workload and we conduct an experiment to test this hypothesis. Specifically, we conducted a survey study to capture nurses' perception of workload in an inpatient unit. Forty five nurses from oncology and surgery units completed the survey and rated the impact of patient acuity indicators on their perceived workload using a six-point Likert scale. These ratings were used to calculate a workload score for an individual nurse given a set of patient acuity indicators. The approach offers optimization models (prescriptive analytics), which use patient acuity indicators from a commercial PCS as well as a survey-based nurse workload score. The models assign patients to nurses in a balanced manner by distributing acuity scores from the PCS and survey-based perceived workload. Numerical results suggest that the proposed nurse-patient assignment models achieve a balanced assignment and lower overall survey-based perceived workload compared to the assignment based solely on acuity scores from the PCS. This results in an improvement of perceived workload that is upwards of five percent.
Health Systems Engineering, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery Mayo Clinic, Rochester, MN, United States. Electronic address: Sir.Mustafa@mayo.edu.
Mustafa Y
MY
Sir
Industrial and Manufacturing Systems Engineering University of Missouri, Columbia, MO, United States. Electronic address: bd5zc@mail.missouri.edu.
Bayram
B
Dundar
School of Nursing University of Wisconsin - Madison, Madison, WI, United States. Electronic address: LSteege@wisc.edu.
Linsey M
LM
Barker Steege
Health Systems Engineering, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery Mayo Clinic, Rochester, MN, United States. Electronic address: PasupathyK@mayo.edu.
Kalyan S
KS
Pasupathy
D001291:Attitude of Health Personnel; D017146:Models, Organizational; D020380:Needs Assessment / Q000458:organization & administration; D009723:Nurse-Patient Relations; D009726:Nurses / Q000706:statistics & numerical data; D009730:Nursing Assessment; D009732:Nursing Care / Q000458:organization & administration*; D009741:Nursing Staff, Hospital / Q000458:organization & administration*; D010361:Patients / Q000145:classification*; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration*; D014481:United States; D016526:Workload / Q000706:statistics & numerical data*
Nurse–patient assignment; Patient acuity indicators; Workload