Presentation
HE3 - A Workload and Task Allocation Time-Study for Addressing Burnout and Job Dissatisfaction Among Clinical Nurses in a Hospital Setting
SessionPoster Session 1
DescriptionBurnout and job dissatisfaction among clinical nurses in a hospital setting has multiple causes, including understaffing, excessive and uncontrolled workloads, and patient safety concerns. While teaching staff to be more resilient may work in the short term, a better long-term solution is to address the underlying issues. This paper will detail combined objective and subjective methods to assess task allocation among clinical nurses in support of an overall program to address and mitigate burnout and job dissatisfaction through implementation of a team-based approach.
The method we developed is a combination of collecting observational data, transforming the data for use with a task model and constructing a tool to allow for data-driven exploration of staffing options. Specifically, the procedure involved identifying nursing tasks and task groupings, and then collecting time and frequency of performing each task through direct observation of nurse activities. This data was used to assess the current workload level of the nurses during specific work shifts and determine how much total time is spent performing each task during those shifts. There was significant value in the process of collecting the data, as it helped delineate patient contact/non-contact tasks and how much time was spent with each type of task. In parallel, we also developed the optimal distribution of tasks among nursing positions (e.g., bedside nurse, LPN, and nursing assistants) based on the level of training for each nursing position. The two sets of data were then combined into a single tool allowing hospital staffing decision-makers to explore different options for task allocation among nursing positions and the composition distribution of nursing position types for any given shift.
Using the tool to explore “what-if” scenarios demonstrated that a team care concept was not only possible but could result in more sustainable distribution of staff types, and improved job satisfaction through increased patient contact time and more appropriate workload levels.
The model results were used to refine the details of the proposed team-based approach and the quantitative underpinning could justify its phased implementation across different units. Initial feedback confirmed the model predictions of reduced and even workload distribution along with improved satisfaction. Over the past year, the program has been expanded among more units with continuing positive feedback and results.
We feel that while this data-driven process is only a part of the overall program involving hiring and retention expectations, educating/training both management and staff as well as carefully addressing patient care concerns, the quantitative underpinning could reduce the perceived risk among managers and decision makers. The model can be used to concentrate on potential tasking and staffing permutations that are predicted to provide the greatest value making it easier to accept with a trial-based implementation of the team-based approach as a valid alternative to individual care.
The method we developed is a combination of collecting observational data, transforming the data for use with a task model and constructing a tool to allow for data-driven exploration of staffing options. Specifically, the procedure involved identifying nursing tasks and task groupings, and then collecting time and frequency of performing each task through direct observation of nurse activities. This data was used to assess the current workload level of the nurses during specific work shifts and determine how much total time is spent performing each task during those shifts. There was significant value in the process of collecting the data, as it helped delineate patient contact/non-contact tasks and how much time was spent with each type of task. In parallel, we also developed the optimal distribution of tasks among nursing positions (e.g., bedside nurse, LPN, and nursing assistants) based on the level of training for each nursing position. The two sets of data were then combined into a single tool allowing hospital staffing decision-makers to explore different options for task allocation among nursing positions and the composition distribution of nursing position types for any given shift.
Using the tool to explore “what-if” scenarios demonstrated that a team care concept was not only possible but could result in more sustainable distribution of staff types, and improved job satisfaction through increased patient contact time and more appropriate workload levels.
The model results were used to refine the details of the proposed team-based approach and the quantitative underpinning could justify its phased implementation across different units. Initial feedback confirmed the model predictions of reduced and even workload distribution along with improved satisfaction. Over the past year, the program has been expanded among more units with continuing positive feedback and results.
We feel that while this data-driven process is only a part of the overall program involving hiring and retention expectations, educating/training both management and staff as well as carefully addressing patient care concerns, the quantitative underpinning could reduce the perceived risk among managers and decision makers. The model can be used to concentrate on potential tasking and staffing permutations that are predicted to provide the greatest value making it easier to accept with a trial-based implementation of the team-based approach as a valid alternative to individual care.
Event Type
Poster Presentation
TimeMonday, March 314:45pm - 6:15pm EDT
LocationFrontenac Foyer
Digital Health (DH)
Simulation and Education (SE)
Hospital Environments (HE)
Medical and Drug Delivery Devices (MDD)
Patient Safety and Research Initiatives (PS)