A Computation Model Quantifying Nurse Staffing to Care Needs

Wednesday, 24 July 2013: 3:30 PM

Taina Pitkäaho, PhD, RM1
Merja Miettinen, PhD, RN, RM2
Juhani Kouri, MD3
Katri Vehviläinen-Julkunen, PhD, RN, RM1
(1)Department of Nursing Science, University of Eastern Finland, Kuopio, Finland
(2)Administration Centre, Kuopio University Hospital, Kuopio, Finland
(3)Kuopio University Hospital, Kuopio, Finland

Learning Objective 1: The learner will be able to implement presented computation model of nurse staffing on a basic level.

Learning Objective 2: The learner will be able to understand that the model is part of a large and complex health care system, and adjustments are required.

Purpose: The computational model for nurse staffing was innovated in the Nurse Staffing Management Development (NSMD) project as part of the B11 project designing a new building to the Kuopio University Hospital in Finland. Problems in existing facilities, decreasing nursing staff, and increasing demands of caring for an ageing population formed the background for the B11 project. The purpose of the NSMD project was to define nurse staffing needs in units that will move into the new building in 2015. 

Methods: Data consisted of information on 108 864 patient episodes (inpatient and outpatient visits, procedures or deliveries) and administrative information on 464 nurses. The data were collected in 15 units. The data on the patient episodes were used in determining care needs. The WHO’s Workload Indicators of Staffing Need (WISN) tool was applied in computing the available working time of nurses. Both subjects, the care needs and the nurse resources, were transformed into hours. The basic formula for the unit’s computational nurse staffing need was: [(need of care* acuity coefficient) / (available working time of nurses)]. The computational nurse staffing need was proportioned to the unit’s number of nurse vacancies. The ratio provided information on the usage level of nurse resources in care processes.

Results: When the ratio was below 80–85 %, the unit’s nurse staffing was considered adequate, and when it was between 85–100 % it indicated a call for developing the processes. The units’ ratio of nurse staffing needs and vacancies varied between 43.7–107.3 %. In all but one unit, there were enough nurses to carry out units’ scenarios in the new premises.

Conclusion: The ratio of the computational nurse staffing need and vacancies does not in itself change anything, but it offers transparent and comparable information for planning, following up, and evaluating nurse staffing.