Alternative models to predict nurse attrition

Authored by: CalmWave Data Science Team

Nurse attrition is a significant problem in the healthcare industry, as it can lead to a shortage of qualified nurses. Without nurses, there is no healthcare. Attrition refers to the loss of employees from an organization, and in the case of nurses, it can be caused by a variety of factors.

Some of the key causes of nurse attrition include:

  1. Burnout: Nurse burnout, characterized by feelings of exhaustion, detachment, and a lack of accomplishment, can lead to nurses leaving their jobs.
  2. Heavy workload and long hours: Nurses often work long and physically demanding shifts, which can lead to mental, physical, and emotional fatigue.
  3. Lack of support: Nurses may feel unsupported by their colleagues or supervisors, leading to a lack of job satisfaction and a desire to leave their current position.
  4. Inadequate staffing levels: A shortage of nurses can lead to increased workload and a lack of resources, contributing to nurse attrition.
  5. Poor working conditions: Factors such as a lack of access to breaks, inadequate equipment, and poor communication can all contribute to a negative work environment and increase the likelihood of nurses leaving their jobs.

Although numerous causes for nurse attrition have been identified, few have actually been measured. To date, there are two main tools that are being utilized to measure nurse workload, burnout, and attrition risk. These are the Maslach Burnout Inventory and the Nursing Work Index. 

Maslach Burnout Inventory

To address the problem of nurse attrition, it is important to have effective tools for measuring and addressing the factors that contribute to it. While the Maslach Burnout Inventory (MBI) is a widely used tool for assessing burnout in healthcare professionals, it may not be the most effective tool for measuring the full range of factors that contribute to nurse attrition.

The MBI survey measures burnout across three dimensions: emotional exhaustion, depersonalization, and reduced personal achievement. However, it lacks a validated cutoff. Thresholds for what is normal or a major crisis are arbitrarily selected. The MBI also has no clear association of burnout with negative outcomes at the various cutoff points. There has been no study indicating increased costs and any specific level of the MBI. 

Nursing Work Index-Revised

One alternative tool is the Nursing Work Index-Revised (NWI-R), which assesses a variety of factors that can impact nurse retention, including job satisfaction, organizational commitment, and intentions to leave the profession. By using the NWI-R to assess the well-being of nurses, healthcare organizations can identify potential issues and implement strategies to improve retention and prevent nurse attrition.

The NWI-R is a questionnaire that asks nurses to rate their experience on activities during their work shift, including patient care, documentation, etc. It performs well in measuring nurse work experience across hospitals, departments, and floors. However, there are several drawbacks to this subjective measure of satisfaction. The questionnaire is long, consisting of 57 items. This creates a huge hurdle in adoption of the tool amongst an already busy set of professionals. The survey does not take into account patient-specific demands. Nurse workload measures have so far been based on tools like surveys and basic calculations. However, there is potential to collect data to very precisely measure various aspects of nursing and match nurses’ desires to patient needs, while maintaining a fair balance on overall workload. 

The future of nurse attrition models

While the MBI and NWI-R are great starting points for measuring nurse attrition, there is definitely room for improvement. These tools lack validated cutoffs, contain arbitrary thresholds, and are generally too long to be completed efficiently by busy clinicians. There seems to be one clear solution for creating an effective attrition model: aggregating existing data to draw objective conclusions. Compiling workload data such as clinician shifts, number of patients cared for, number of alarms attended to, etc. in conjunction with any survey-related information will allow for a more comprehensive insight into which nurses may be at high risk for attrition. With the help of artificial intelligence (AI) software, this data can be aggregated to efficiently produce objective measures of Operations Health such as nurse burnout and workload scores. This effectively fills in the gaps from the MBI and NWI-R by utilizing data as it was meant to be used to create objective measurements, thresholds, and benchmarks. This will ultimately provide reliable, objective insight to decrease nurse attrition. 

Progress begins today

It’s evident that nurse attrition is a significant problem that can have negative consequences for both nurses and patients. To address this issue, it is important to identify the key causes of attrition and use tools like the MBI and NWI-R to measure and address the factors that contribute to it. However, these tools are just starting points. They’ve made progress in tracking the well-being of employees, but are still not the most scalable and reliable methods to broadly implement. CalmWave is taking steps to recognize nurse attrition risk factors and implement strategies that work to improve the well-being of nurses and ultimately provide better care for patients. With the help of proprietary AI-based technology, CalmWave provides objective measurements of nurse workload and burnout to allow hospitals to intervene before nurses succumb to attrition. Schedule a demo today at calmwave.ai to view how AI can provide comprehensive insight to alleviate nurse attrition. 

References:

  • Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Occupational Behaviour, 2(2), 99-113.
  • Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J., & Silber, J. H. (2002). Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. Jama, 288(16), 1987-1993.
  • McCloskey, J. C., & Bulechek, G. M. (2005). Nursing interventions classification (NIC) (5th ed.). St. Louis, MO: Mosby Elsevier.