A predictive data model can provide insights into risk factors impacting nurse attrition and help organizations develop data-driven strategies to mitigate risks.
American healthcare organizations face serious personnel shortages across the field of nursing, with the current (and projected) demand for registered nurses (RNs) far exceeding the supply of qualified candidates.
The U.S. Bureau of Labor Statistics projects that by the year 2022, the U.S. job market will need 1.1 million new registered nurses (RNs) to replace retirees and fill new positions created by changing patient demographics (including the aging baby-boomer population).
High RN vacancy rates are not only expensive, but they can trigger a vicious, perpetual cycle of negative impacts; including overburdening current staff as well as the exorbitant costs associated with backfilling and hiring contract staff. These conditions, if left unchecked, will ultimately lead to negative impacts on patient care and customer service.
One of the most effective ways to minimize the impact of nurse attrition on your organization is by building a data model that uses artificial intelligence (AI) with machine learning capabilities to produce predictive analytics on nurse attrition. This technology uses computer algorithms that can extract hidden patterns, predict future outcomes, and continuously improve the accuracy of predictions based on experience (additional data collected).
Although predictive modeling is not a crystal ball that can tell the future, it can forecast the degree of nurse attrition that your organization can expect in the future and even predict – with high accuracy – which employees are likely to leave.
A properly constructed predictive data model can provide unprecedented insights into the major risk factors impacting nurse attrition and help organizations like yours develop data-driven strategies to mitigate the key risks.
Building a Predictive Model: The Value of Lagging and Leading Indicators
To begin properly constructing a predictive data model, it is necessary to understand the common forces that drive attrition within a healthcare organization. This requires you to develop and analyze two different types of indicators associated with nurse attrition: lagging and leading indicators.
Lagging indicators are “hindsight” indicators that describe what has happened in the past. They are derived from data collected on employees who have already left the organization, such as information gleaned through personnel records or acquired through employee exit interviews or surveys.
Some common lagging indicators include:
- Reliable/honest data on what caused the decision to leave
- Data on precursors to the decision to leave
- Demographics of employee, hospital, location/city, etc.
- Data on external factors that caused employees to leave (e.g. data on competing organizations, local construction projects impacting major traffic thoroughfares, etc.)
- Job Type or Department (some job types or departments have historically higher attrition rates)
Lagging indicators are easy to measure (e.g. How many nurses left in the previous year as the result of salary issues?) and difficult to influence (these employees have already left the organization).
Leading indicators are “insight” indicators that describe a current condition. They are derived from data collected on current employees who have not separated from the organization.
Leading indicators help us to determine how known risk factors are affecting the current workforce and can be compiled from a variety of data sources including personnel records, job satisfaction surveys, current workload data (for example, average weekly/monthly hours, etc.).
Access to robust leading indicators may require additional data collection methods, such as survey development and focus groups to understand the stressors impacting current nursing staff. Common leading indicators include:
- Level of Job Satisfaction
- Time Elapsed Since Last Performance Evaluation (in months or years).
- Average weekly/monthly hours (and any recent spikes related to being short staffed).
- Number of Years on Staff
- Pay Raises: Has the employee had a pay raise in the last year?
- Promotion in last 5 years: Whether the employee was promoted in the last five years.
- Salary: Relative level of salary compared to industry averages
Leading indicators are more challenging to measure because of the uncertainty associated with them, but they are much easier to influence since there is still an opportunity to develop and apply intervention strategies.
“Triaging” Intervention Strategies to Encourage Nurse Retention
Developing a predictive model that uses both lagging and leading indicators enables your organization to get a better idea of the likely rate of attrition in the future (with all conditions remaining constant) but can also help identify areas where intervention strategies and remedial actions can be applied.
For example, let us imagine that our model predicts that your organization will experience a 32% nurse attrition rate within the next fiscal year, and also identifies related risk factors among the “attrition subset” such as lack of job promotions or high overtime rates contributing to “nurse burnout.”
Identifying the specific risk factors linked to nurse attrition provides an opportunity for your organization to develop and apply targeted intervention strategies focused on groups and individuals. On the group level, this data may indicate that you should re-examine hiring practices and/or overhaul performance review processes.
Based on data analytics, you may decide to encourage retention by offering certified training programs or other benefits designed to improve the overall quality and value of the workplace. At the individual level, employees who have not been promoted within the last five years can be offered career incentives, access to (and funding for) external certification programs, or other strategies that may help them to feel valued and upwardly mobile within the organization.
By understanding the specific risk factors that create the most adverse impacts on your current workforce, you can develop targeted and prioritized strategies that help to mitigate those risks and encourage nurse retention.
Final Thoughts
Building a predictive data model based on artificial intelligence and machine learning technologies can significantly impact your healthcare organization’s ability to manage nurse attrition by refining available data to make data-driven decisions.
The sooner you can identify the risk factors that are contributing to attrition among nursing staff, the sooner you can take action to minimize those risks by developing and applying prioritized, targeted intervention strategies.
