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The vast majority of nurses are overworked because of an ever-growing skills gap in the healthcare sector, and nursing managers are not immune. The more time spent on administrative tasks, the less time spent on critical patient care. Without an efficient labor management plan in place, it is ultimately the patient who suffers.

When scheduling healthcare workers, managers must account for many variables, such as patient population, staff shortages, existing worker availability, appropriate resource management, and more. To address this problem, many healthcare organizations are turning to automation solutions. When major components of staffing and scheduling are automated, nurses can focus on more pressing concerns, such as patient care.

“Optimizing the process helps reduce some of the common factors that can exacerbate resource constraints,” says Jackie Larson, president of healthcare labor management firm Avantas. “These common factors include not scheduling staff to their full-time equivalents, allowing imbalanced trade practices, and the overarching issue of not managing at the enterprise level.”

Healthcare Staffing in the Age of Machine Learning

Thanks to advances in artificial intelligence (AI) and related technologies, it has become possible in recent years to automate certain management functions in order to drive efficiencies and allow managers to focus on core competencies.

Using machine learning, many organizations can now better forecast staffing needs as far as 120 days in advance.

“[Creating] better, more accurate schedules sooner result[s] in a much more efficient and effective workforce with less expense and a lot less aggravation for staff,” Larson explains.

While automation solutions can help solve staffing issues in the long term, it is important to understand that they will not fix an operation overnight. As the name implies, machine learning requires time to learn before it can make any major improvements in a company’s staffing practices.

“Predictive analytics is not a plug and play solution,” Larson says. “It continues to get better over time. It can’t predict what has never happened, but it looks at years of data to continually update and enhance predictions. The more data we have, the more accurate the predictions will be.”

Larson also notes that, while predictive analytics can offer an accurate labor forecast, a forecast is not enough on its own to address all of a healthcare organization’s staffing woes.

“You need to have the right policies and procedures in place, the right culture embedded, an enterprise-staffing mentality, and a commitment from leaders and staff alike to work toward achieving your KPI goals,” she adds.

Addressing Staffing Pain Points

There is one major problem with automation across many sectors: It makes people nervous. Managers worry that automating processes will affect their roles or they ways they relate to the workforce. Employees worry they’ll lose their jobs to a series of ones and zeros.

However, the reality of automation is often quite different from these anxious expectations.

“One common misconception is that departments will lose control of the decision-making process relative to staffing,” Larson says. “Some unit managers and directors might fear the centralized resource management center (RMC) is too detached from what is happening in their department — that they won’t understand their needs and the thousand other intricacies directors and managers know through their years of experience.”

In reality, the degree of separation between units and RMCs allows for an enterprise-level view of the organization’s needs, which leads to “a more objective and strategic approach to placing resources that will benefit the entire organization,” Larson explains.

“RMC staff do not make clinical decisions,” she continues. “Those decisions are made by the assigned clinical decision-makers. The clinical decision-maker works with the RMC and unit managers and directors to gain an understanding of the entire staffing picture and makes decisions based on a complete understanding of all the issues and all the resources in play.”

The fact of the matter is that the healthcare sector, like so many others, faces a serious skills shortage. Resources are spread too thin, and there don’t appear to be any easy solutions on the horizon. Unless healthcare managers leverage automated workforce management technologies to meet staffing needs, the lack of talent will harm what matters most: patient care.



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