Patient Flow

Staffing the Shift: The Human Impact of Getting it Right

Hospitals, Staffing, Artificial Intelligence, Machine Learning, Patient Flow

“They just don’t care about us,” goes the refrain. When the shift is understaffed, nurses can feel betrayed by the managers and executives responsible for filling shifts.

As an emergency physician, I work at a wide range of Emergency Departments (“ERs”), from large referral centers with four or more providers working around the clock, to tiny ERs with eight beds and a single doc. Regardless of the size or sophistication of the ER, when nurses feel stretched beyond their limits, it’s common to hear them voice distress or to wonder who is looking out for them.

Having worked both at the front lines as a clinician as well as an administrator, I know that often enough leaders do care, and care a great deal, about filling those shifts. But sometimes it can seem impossible to find someone even for a half shift.

At the other extreme, many times I’ve been on an overnight shift at a small ER that has emptied out in the small hours of the morning. Nobody complains about those shifts. And in fact, when there is bitterness about working understaffed shifts, these overstaffed hours can seem like a well-earned respite.

The dream is, obviously, to staff every shift such that staffing precisely meets demand. But there are real-world limits to doing so. Some of these are not amenable to change. There are costs of readiness. Even if a small ER empties out for a few hours overnight, there still needs to be a doctor, a couple of nurses, and enough other staff to run a code.

However, some of these limits are imposed by a lack of information. If we are facing the prospect of staffing some nights with 10 nurses, and others with eight, because there simply aren’t enough available people, wouldn’t it be great to know which nights to staff down?

Of course some of this can be done using historical data. For example, you may know that Monday nights are always the busiest, and thus focus on using more of those staffing hours on Monday nights, leaving others possibly more short staffed.

But leveraging advanced data analytics can extend the value of historical data considerably, by finding patterns not only by day of week, but across seasons, special dates in the calendar and other events. With the right tools, shifts can be tailored to more precisely match staffing to predicted demand, hour by hour.

And still more advanced approaches can go beyond historical patterns, to forecast spikes and dips in arrivals at the ER days ahead of time. For managers, this means that if they can round up some additional hours of nursing coverage, and they know where best to put those hours to use. It also means they are not finding themselves in a position where they are making calls to ask nurses to drop their plans and come in to work that very day. Armed with several days’ advance notice, they can make the necessary staffing arrangements ahead of time, giving nurses more opportunity to make themselves available.

As hospitals adopt these powerful analytical tools, there may be fewer staff working these “slow” hours, but it can also means not getting pummeled on understaffed, “busy” shifts. I believe most of the nurses with whom I work would agree that was a good tradeoff. These cutting edge approaches will give managers the tools to look after the nurses they work with, and show them just how much they care.

This post originally appeared on the Hospital IQ website.

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