Striving for Improvement in Staffing, Not Perfection

By | 2019-06-11T14:49:17+00:00 June 11th, 2019|

An inpatient staffing manager is on a constant quest to ensure proper staffing ratios are met, and that his or her unit meets staff productivity goals. Staffing levels are often set weeks in advance and then adjusted the day-of to deal with unexpected call-offs and to better align with the current census.

These day-of staffing adjustments can be costly, both financially and in terms of staff satisfaction. Late shift additions often come with a premium pay grade, and excess staff who are sent home result in a wage paid regardless of the productive hours worked. When last minute adjustments are required, staff are often moved to units with which they are not familiar, sent home, or forced to work with sub-optimal staffing ratios, all of which can have a negative impact on staff satisfaction.

There is no doubt any staffing manager would be thrilled to have a crystal ball that recommended optimal staff counts a few days in advance. This would ease the pain of making day-of adjustments, ensure proper staffing ratios, and limit the costs mentioned above.

Whitepaper: Using Predictive Analytics and AI for Proactive Resource Planning and Management

In a recent conversation with an inpatient nursing director, we asked how accurate a crystal ball staffing recommendation would need to be for her to rely on it to make staffing decisions.

“Perfect,” was her response.

Regardless of industry, whether it be the airline industry, stock market, politics, or hospital census levels, there is no crystal ball. This, however, is where progressive nursing leaders need to learn from leaders in other industries. Predictions do not need to be perfect to be valuable, they just need to be better than whatever is currently being used.

While no two hospitals are the same, current practices often use an “Average Daily Census” or a “Budgeted Census” to set staffing levels, and these staff counts are frequently left unchecked until the day-of. These values are often based on midnight census and are updated infrequently, which means staffing managers are regularly staffing to a census number that does not accurately represent the optimal staff required.

At Hospital IQ, our solution applies advanced data analytics and machine learning to historical data, the surgical schedule, current patient population, and many other data sources to forecast demand and recommend optimal staff counts days ahead of time. Our collaborations with early innovators have shown that recommended RN counts made 3 days ahead of time are up to 20% closer to the optimal value than current staffing practices. On average, a unit that normally operates with 6-8 nurses would be 1.6 nurses closer to optimal staff count than they are with current staffing practices. Customers using our solution to make staffing adjustments 3 days in advance are reducing staffing churn by up to 200 shifts per unit in a 6-week staffing period. Imagine the positive impact this reduction can have on nurse satisfaction across your team.

While every staffing manager may dream of having a perfect crystal ball, the focus must be on improving their current staffing methods – especially regarding the use of average daily census. We see that by pairing our solutions with the expertise and leadership of progressive staffing managers, hospitals will see a reduction in staffing costs, an improvement in staff satisfaction, and less time without proper care coverage.

For more on effective staff management, download our whitepaper: Using Predictive Analytics and AI for Proactive Resource Planning and Management.

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