Predicting the effect of nurse-patient ratio on nurse workload and care quality using discrete event simulation

J Nurs Manag. 2019 Jul;27(5):971-980. doi: 10.1111/jonm.12757. Epub 2019 Apr 25.

Abstract

Aim: A novel nurse-focused discrete event simulation modelling approach was tested to predict nurse workload and care quality.

Background: It can be challenging for hospital managers to quantify the impact of changing operational policy and technical design such as nurse-patient ratios on nurse workload and care quality. Planning tools are needed-discrete event simulation is a potential solution.

Method: Using discrete event simulation, a demonstrator "Simulated Care Delivery Unit" model was created to predict the effects of varying nurse-patient ratios. Modelling inputs included the following: patient care data (GRASP systems data), inpatient unit floor plan and operating logic. Model outputs included the following: nurse workload in terms of task-in-queue, cumulative distance walked and Care quality in terms of task in queue time, missed care.

Results: The model demonstrated that as NPR increases, care quality deteriorated (120% missed care; 20% task-in-queue time) and nursing workload increased (120% task-in-queue; 110% cumulative walking distance).

Conclusions: DES has the potential to be used to inform operational policy and technical design decisions, in terms of impacts on nurse workload and care quality.

Implications for nursing management: This research offers the ability to quantify the impacts of proposed policy changes and technical design decisions, and provide a more cost-effective and safe alternative to the current trial and error methodologies.

Keywords: discrete event simulation; human factors; nurse management; nurse-patient ratio; quality of care.

MeSH terms

  • Computer Simulation
  • Humans
  • Nurse-Patient Relations
  • Nurses / standards
  • Nurses / supply & distribution*
  • Organizational Policy
  • Personnel Staffing and Scheduling / standards*
  • Quality of Health Care / standards*
  • Quality of Health Care / statistics & numerical data
  • Workload / psychology
  • Workload / standards*
  • Workload / statistics & numerical data