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Rethinking RIF Workforce Optimization in Healthcare
Healthcare organizations have recently been stretched extremely thin to serve as front-line response to accommodate care during the COVID-19 pandemic. This has caused massive changes in workforce stability, ranging from increased turnover and mass furloughs to a large increase in contract labor.
While volumes of COVID-19 patients may be difficult to plan for in the near and medium term, provider organizations are faced with more healthcare workforce decisions to be made than ever before. However, we must keep in mind that workforce optimization struggles precede the pandemic, and our future decisions should avoid repeating or worsening the trends of the past.
Labor Expense: A Constant Concern
Expense growth is outpacing revenue growth in many healthcare organizations across the country. Before COVID-19, salary and benefits combined were the largest expense line item and often accounted for more than 50 percent of total operating costs for healthcare organizations. With revenue loss and financial pressure caused by the pandemic, most of the nation’s provider organizations are looking for a way to reduce labor expense.
Rather than immediately begin planning for a Reduction in Force (RIF), it is increasingly important that organizations start by properly managing their workforces. Not only can workforce optimization programs save money, they can also avoid the negative publicity related to staff layoffs. Effective workforce optimization programs manage full-time employee (FTE) counts and increase staff productivity and engagement. Improved engagement can decrease staff turnover, a large and often unnecessary cost.
Reductions in Force
RIFs are a widespread issue, and especially so as a result of the ongoing COVID-19 pandemic, which has affected nearly every healthcare organization in some way. Becker’s Healthcare reports that 221 hospitals have defaulted to large-scale RIFs in response to COVID-19. However, even when accounting for potential skews due to COVID-19, we see that some geographic areas are significantly more prone to turnover and layoffs than others.