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Diagnosing and Preventing Nurse Burnout Using People Analytics

Nurse burnout is bad for patients and bad for business, so why is it so hard to avoid? Here are four data-driven strategies you can use to reduce it.

Nurse burnout–a state of physical, mental and emotional exhaustion caused by chronic stress–is a common problem in healthcare, one that came up often when I served as Director of Workforce Analytics and Special Projects at Vancouver Coastal Health, one of the largest healthcare organizations in British Columbia.

It’s a prevalent problem industry-wide. According to a 2010 study of California’s policy by Linda Aiken, et al., 29% of nurses in California experienced high burnout, compared with 34% of nurses in New Jersey and 36% of nurses in Pennsylvania.

Nurse burnout has impacts that can be subtle–a nurse who has a lack of empathy for their patient because they are simply too tired to be able to be kind–or severe–a nurse who is off work and seeking counselling to recover their mental health before being able to go return to the hospital floor.

“Nurses are nurses because they genuinely want to take care of people,” says Dr. Stacy Sprague, PhD, R Psych, Executive Director, Employee Wellness at Vancouver Coastal Health. “They feel compassion for their patients, but years of exposure to long hours, strenuous shifts where they are often tasked to run short, and difficult patients can cause compassion fatigue, which is a really clear indicator of nurse burnout. Compassion fatigue can look like a nurse feeling detached, and cynical about their job, and that they are failing as a nurse. This can result in a higher level of moral distress for a nurse and can compromise their ability to give their usual high level of patient care and can push them towards taking long-term leave. This in turn puts even more strain on an already taxed system.”

HR needs to take a compassionate approach to reducing nurse burnout and, strange as it may sound, people analytics and data can truly help in the effort to see the issues from the nurse’s point of view.

What’s Driving Nurses to Burnout?

According to a 2017 study by Maura MacPhee, PHD RN, Professor, University of British Columbia School of Nursing: “Burnout has been linked to higher rates of absenteeism than the general population and to increased nurse turnover and decreased job satisfaction.”

McPhee also found that “nurses who experienced heavy workloads on a daily basis were 3.5x more likely to report high emotional exhaustion than nurses who experienced heavy workloads less frequently.”

Clearly, nurse burnout is bad for patients and bad for business, so why is it so hard to avoid? There are certain forces that put pressure on nurses to work past their limits:

Systemic forces

As baby boomers reach post-retirement age, their demands on the healthcare system increase. Additionally, as the older generation of nursing staff reach retirement age, the supply of experienced nurses reduces, especially in areas that require higher levels of training such as critical care (e.g. intensive care unit nurses). Put simply, there aren’t enough nurses to serve the patient population.

24/7/365 scheduling

Scheduling and deployment of nurses are based on patient needs. This means that even though nurses are professionals, they are deployed as shift workers. In order to meet patient needs, 12-hour and 8-hour shifts operate on a rotational pattern. Nurses on 12-hour shifts experience higher levels of chronic fatigue, cognitive anxiety and emotional exhaustion and are also 2.5x more likely to burnout. However, nurses often choose 12-hour shifts because it provides them with a shorter work week (a “4 days on/4 days off” shift pattern).

The significant systemic and personal pressures that nurses face can make it difficult to make rational choices, especially in an industry where empathy and compassion are so critical. This is where people analytics can make a difference.

How to Leverage Data as Your Diagnostic Tool

People analytics uses data to illustrate behavior in the workplace and can uncover issues such as chronic overwork. This empowers the business to devise a plan to effectively solve workplace issues.

Adds Dr. Sprague, “To me using people analytics is a whole new way of doing business. We used to say that finances drove a business, but I think people analytics shows us that behavior also drives a business, and the more we can understand our business using people analytics, the more successful we will be at providing it.”

Here are four data-driven strategies you can use to reduce nurse burnout. 

1. Determine the metrics that will shed insight into overwork and use them to inform a fatigue policy

Many healthcare organizations have metrics such as overtime compared to straight time worked. These help to illustrate how much a person is working above the job that they have taken.

I propose a total burden-of-work metric that includes straight time work that the employee has agreed to (e.g. I am part-time so expect to work 0.6 FTE or 40hours X 0.6 = 24 hours per week), plus whatever actual overtime hours, on-call hours, etc.

Comparing the amount that employees are actually working compared to the amount they are contractually obligated to perform is very helpful in uncovering issues. Identifying these issues (e.g. unusually high burden of on-call shifts) and engaging in collaborative planning can lead to staffing solutions which distribute the workload better or determine creative ways to off-load this burden, and can increase efficiency (e.g. lower overtime hours and costs which also benefits employers).

2. Link data from all your disparate HR and business systems to gain insight

Reducing nurse burnout requires data to come together from multiple sources. This gives you a more accurate picture of the employee lifecycle, so you can identify where to focus your efforts. For example, determine which nursing group is experiencing the most burnout or may be on the verge of burnout (based on historical trends) by pulling HRMS records to get job and turnover information, as well as actual compensation or timekeeping data to get hours worked.

Use analytics-driven workforce planning to create forecasts and scenario models that will help you fill the staffing gaps. Bring in talent acquisition data to set achievable targets for recruiters to find already trained staff and dive into learning analytics to set education targets for how many highly skilled nurses you can expect to train. Link the learning data to job records and turnover to ensure your training spend is achieving ROI – that nurses are staying in the jobs you are training them for.

3. Implement a data-driven scheduling model

Likewise, having regular and easy access to all the data in your employee lifecycle enables you to proactively build more creative and flexible scheduling systems, ones that affords more frequent opportunities to choose a level of work that is sustainable on the individual’s side while meeting the scheduling needs of the employer. Test different scheduling models with outcome measures – employee groups with lower turnover and sick rates are probably working healthier schedules.

This can be as simple as implementing a break relief schedule where the data indicates employees are missing meal breaks or as complicated as forming a relief staffing strategy for areas with more pervasive, longer standing issues.

Using analytics, identify the most effective way to backfill for vacancies, long-term leaves, and short-term predictable leaves (yes, it is possible to predict sick time with the right data sources and analytical solutions).

4. Get ahead of turnover

Voluntary turnover in the form of resignation is a strong indicator that there is an issue with burnout in a particular unit. Nurses may choose to change their full-time status moving to part time or even casual status in order to exert more control over their work life and have more choice over which shifts they work.

Monitoring data on the number of hours being worked or time off for sickness may also provide earlier warning signs. Engagement or culture surveys offer a rich source of data as well that can provide forewarning of systemic issues.

I cannot stress enough the importance of moving beyond reporting into analysis to see the hidden patterns that are driving turnover and if possible, move to machine learning-based predictive analytics. Putting these together with a collaborative workforce planning tool will give your management teams insight into whether the actions they are undertaking are having an actual effect on improving the well-being of nurses.

A laptop screen showing the Visier People dashboard key employee retention metrics

Pondering Future Uses of People Analytics in Healthcare

A solution such as a people strategy platform enables you to set and control access to people analytics within your organization. Usually, access is given to the HR team and in increasing cases, directly to people leaders, but it’s also worth imagining a future where analytics is used even more broadly, such as by the nurses themselves.

For example, sharing data in a way that would help nurses see patterns in their work-life balance. Or what if each employee had a personal dashboard where they could see the amount of overtime, straight time, sick time, on-call that they were working in comparison to the distribution of their peers?

I can imagine that this data would be very helpful in conversations with their manager to identify longer term patterns that are not necessarily apparent in the moment. This could range from realizations of ‘I seem to be calling in sick more than my peers’ or ‘I’m taking on more overtime than my peers’ leading to a productive discussion of what wellness and scheduling programs can be used to help the employee.

I don’t mean to over simplify the situation here and I realize these discussions are complex. I also realize each person is different and can and may want to choose a different level of work. The important point is that transparency and data can help to illustrate patterns that are important discussion topics between the employee and the employer–and lead to a healthier workforce and industry overall. 

About the author: Kevin MacDuff

Kevin MacDuff, CPA is self-described as a 'math guy in HR'. He studied mathematics for his undergrad and has worked in HR in healthcare for the past 12 years during which time he completed his CPA designation. He now works with customers of Visier to ensure they are getting the most value out of the awesome capabilities it has to unlock insight from HR data.

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