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Social Psychology Analytics of Employee Stress – Through the Lens of Clinician Burnout

Social Psychology Analytics of Employee Stress – Through the Lens of Clinician Burnout

Kaiser.pngIn this blog series, we’ll first examine how some organizations can mature their workplace stress programs and support improved work environments for their employees through the use of discovery analytics. Our second installment will focus on workplace safety and leadership, and the progressive means of improving the measurement of success as an employer. We are particularly appreciative of the insights of Dr. Christina Maslach, Professor of Psychology and Vice Provost at UC Berkeley, who was featured in the film, The Stanford Prison Experiment.

An expert in the field of the “burnout experience,” Dr. Christina Maslach calls to mind the expense of employee burnout as a result of stress: “Employee burnout costs in terms of the quality of service or the product. There are so many statistics on ways in which this happens that you can’t ignore the impact. People are now recognizing that to get the best and keep the best, working in their company, you do have to pay attention to making sure that the environment is going to attract them and hold them to do their best and want to stay.”

Stress Leads to Burnout and Risk of Disease, Costing Individuals and Employers

In addition to burnout, workplace stress has a rippling effect on increasing individual risk of many diseases, notably heart disease, hypertension, reduced immunity, migraines, depression, suicidality and certain cancers.1 2

Despite the overwhelming evidence of cost associated with stress, a study by the Illinois Workplace Wellness Study suggests that many employers underestimate the commitment it takes to create an environment and program for decreasing workplace stress, and that this lack of commitment has resulted in poor return on investment.3

Workplace wellness programs are certainly not without controversy, given they can simply be a vehicle for employer leadership to push the responsibility of health costs, sometimes considered discriminatorily, onto individuals who are less healthy by a factor of up to 30 percent of premiums (as implemented through the Affordable Care Act). Success in cost savings for the organization and benefit for the employer have traditionally not come hand in hand either, and oftentimes, programs developed end up losing money for the employer and resulting in nominal health improvement. Finally, wellness programs may neglect to factor in other causes of stress, such as underemployment, financial debt, and planning for retirement. A recent survey by PwC revealed that 54 percent of employees are stressed about their finances and 64 percent of respondents say they are impacted by student debt. Other common social risks, such as traffic, work-life balance, bereavement and other personal management issues are often under evaluated.4

Consider the purpose of meaningful, “internal adaptive” stress programs at companies: understand the mental anguish of your employers, deriving from both external factors such as bereavement, and internal factors, such as a poor management match between a manager and an employee. Employee mental wellness research in the UK surveyed over 44,000 people and drew some remarkable conclusions. More than 84 percent of employees would continue to go to work even when experiencing poor mental health, while only just over half (58 percent) would go to work when experiencing poor physical health. Additionally, only two fifths (42 per cent) of all employees surveyed felt their manager would be able to spot the signs that they were struggling with poor mental health. Employees are clearly not expecting much from their employer when displaying stress at work.5

Formulating Effective Wellness Programs, Using Analytics

Let’s first examine the challenges of improving stress wellness at scale through a couple of scenarios. The first is a personal account; I had the benefit of directing the strategy of one of the pioneer accountable care organizations based in Boston. In efforts to formulate our preventative care wellness models, the physician stakeholder paired up in 2014 to talk through patient cohorts and analyze the relationships between patient costs, time invested by clinicians, and payer reimbursement. Naturally we looked at claims information – quite possibly the lowest form of discovery analytics well known in capitation efforts—to confirm our most costly populations. But we added additional subsets of data to the conversation, and in doing so, could hypothesize about the style of the wellness program to enact. We wanted to be on the front end of prevention for our community, we realized that our efforts should be focused on influencing positive health habits.

The initial four wellness programs we ended up designing internally were obesity reduction, diabetes management, back pain reduction and medication adherence. In our second year, we expanded to six programs to include mental health and, perhaps our greatest design challenge, workplace stress. After the third year, we realized significant results in improved medication adherence by greater than 4 percent in our elderly populations, as well as observing improved obesity and diabetes management through our performance improvement studies. However, we had not been able to crack the code on our corporate wellness campaign, observing consistent levels of morbidity in our employee base where stress is a common causation. We realized a large component to solving the problem, instilling positive habits tied to a reward, had not been initiated.

Finding this reward is key to developing positive habits. In our second scenario, we focus on an obvious motivational factor in activating employees to participate in wellness programs: money. Scott Halpern, an associate professor and steering committee member of Penn’s Center for Health Incentives and Behavioral Economics (CHIBE), found that all types of financial incentive schemes were more successful than standard smoking cessation care. Certain reward-based programs were more than 6 times more acceptable to employees who smoke, indicating that, despite deposit-based programs ultimately being successful, reward programs will be more successful overall.6

“The new study drives forward previous research by showing that even among smokers who are cherry-picked on the basis of their motivation to quit, financial incentives still triple quit rates, whereas offering free conventional cessation aid or free e-cigarettes accomplishes nothing at all,” said Mr. Halpern. The success of such programs underscores the need to understand the reward necessary to the desired change.

Shifting our focus now towards clinician burnout, as Dr. Maslach points out, rewards are often psychological in nature:7 “In social psychology, we’re interested in the individual in a social context. What is it about the environment you’re in that affects the choices you make and how you behave? With job burnout, what we’ve been finding is a lot of that. So it’s not that people are experiencing burnout because they’re weak, or don’t know how to cope with life, or have mental problems. They are dealing with a social environment, in their workplace, that is creating certain kinds of stressors and constraints.”  The reward, therefore, can take the form of alleviation of these factors.

Back in Boston, with clinician burnout in mind, my physician partner and I went through a second set of interviews. We noted themes of these very stressors and constraints, in the forms of compliance, medical record documentation, and bureaucratic tasks, excessive paperwork, a lack of collaboration between their department and administrative leadership, areas of poor training and low levels of communication. Taking these somewhat abstract components into account, measuring these nuances requires a great deal of information and observation. This is where successful measurement models for wellness are critical as a foundation for the program.

Measuring Employee Wellness

For example, the Bergen Burnout Inventory (BBI) focuses specifically on assessing three dimensions of burnout: cynicism toward the meaning of work, exhaustion at work, and sense of inadequacy at work. By combining this insight with an engagement factor, such as in the Utrecht Work Engagement Scale (UWES), organizations can begin to analyze (through tools such as nPath and prescriptive and predictive event-based analytics) and directly correlate with HR benchmarking. This benchmarking should include a personalized culture scoring mechanism, such as an employee Net Promotor Score (eNPS).

So how do organizations actually input these psychologic models and analyze the information? Not all employee assessments produce such useful information. Performance improvement assays, department observation, and in some cases, IoT galvanic stress data can drive information into your employee wellness analytic programs. However, well planned, periodic employee surveys should certainly continue to serve as a starting point for input.

Surveys are a critical interface for organizations to advance their progress in assessing the culture of their organization and informing their discovery analytics toolsets. Maturing a program to support mental challenges faced, organizations should include social risks questions to gain a greater understanding of the daily hurdles employees face in their day to day routines, such as how long they sit in traffic, how much work they end up performing when at home, and how satisfied they are about how their income and personal finances align. With Leikert-scaled surveys for employee departments, organizations can begin to move towards an informed model, measuring improvements over event pathing tools, such as Teradata’s nPath, to assess stressor relationships or initiative improvements, or Hidden Markov models to discover program correlations amongst employee behaviors.

Human resource departments can leverage Teradata’s discovery analytics as a means to analyze social and psychologic events and patterns in the workplace. By observing the adoption of positive habits, and the reward of such habits, organizations can establish an analytic foundation for lowering individual and environmental stress.

In the subsequent article, we’ll examine applying proven employee wellness models to advanced analytic tools, including artificial intelligence and wellness pathing, to measure three components of an employee wellness in the clinical setting: burnout, safety and culture. With advanced analytics on psychometric and operational throughput data, we’ll show how to help your organization pivot departments, groups and individuals towards a functional path of improved stress management.

[2] C.L. Cooper and R. Payne, Current Concerns in Occupational Stress (New York: John Wiley).

Portrait of Dan Ulatowski

Dan Ulatowski

Mr. Ulatowski, an epidemiologist formerly of the Department of Health Science Research at Mayo Clinic, focuses on applying analytic techniques for a variety of client strategic initiatives, primarily centered on patient and member insight programs for healthcare and life science organizations. His accomplishments include leading the design of quality outcomes programs for two accountable care organizations, genetic engineering and research in therapy design for treatment of West Nile virus, and implementing a human factors engineering program assessing adoption of digital technology across health centers supporting underserved patients for a large west coast provider organization. His current initiatives focus on merging statistical analysis of both the social science and clinical data. Educated at the University of Wisconsin-Madison, his interests include understanding the impacts of human behavior on both epidemiologic studies and health policy.

View all posts by Dan Ulatowski

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