Introduction
Workpartners® used its advanced data analytics to identify drivers of potential turnover among essential health care workers.
Problem
Acquiring and retaining front-line workers is critical for any organization, but for those in the 24/7 health care industry, it’s essential. Losing essential health care workers causes downstream issues in scheduling, backfilling and replacing workers, patient care, and more. Our large health care client faced high turnover among its bedside nurses and call center workers.
Solution
Workpartners developed a retention risk predictive model to identify bedside nurses and call center workers who were likely to leave within six months. To build the model, Workpartners integrated data across a variety of potential factors. An accurate prediction can’t be attained using just human resource, health, or demographic data alone. As such, Workpartners took a broader, integrated approach in analyzing all of the human capital data available from the client, including:
- HR and people data (tenure, job characteristics).
- Demographics.
- Compensation and rewards.
- Self-reported stress, job satisfaction, and employee engagement.
- Medical and pharmacy claims.
- Disability and absence claims.
- Time off on job schedules.
- Safety and workers’ compensation.
- Health programs and interventions.
- Department characteristics.
Workpartners broke down existing data silos and built an integrated data warehouse to synthesize all the information and run detailed analytics. The results allowed the client to fully understand its business challenges, identify potential risks, and predict the human capital costs of the problem.
Results
Workpartners’ model was 90-percent accurate at identifying call center workers who would leave in 90 to 180 days. It also identified nurses who were three times as likely to leave within that 90- to 180-day window. This allowed the client’s leaders to evaluate which factors were driving turnover among these employees, then develop interventions to address the problems on an individual and departmental level. During the seven months that followed, the outreach and intervention plans resulted in a 17.5 percent reduction in monthly turnover compared to a 4.6 percent decrease in other departments with high turnover but no interventions. Considering that the estimated cost of recruiting, hiring, and training a new employee is $2 million per 1,000 hires, this reduction in turnover has led to a significant savings for the client.