Overtime Meets its Match in Staffing Automation

Kathy Douglas RN, MPH-HA, Chief Clinical Officer with ShiftHound

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With so much attention on data strategies, big data, data scientists, data ownership, data centralization, data integration, data mining, data ethics (the list could go on) it can be easy to over look the power in the use of some very simple data sets. One of those is the data that surrounds and supports staffing.

Decisions that are made in the process of staffing have significant financial implication. The use of overtime is one of the more visible drains on staffing budgets and tends to get the most attention.

How to better manage overtime can be best understood by looking at the decision making process. There are 2 general reasons an overtime decision is made. The decision maker:

  • Did not know the person would be going into overtime.
  • Knew about the overtime and made the decision anyway.

While setting policies on overtime use and approval processes can help address overtime usage, an alternative and more structurally powerful solution is to equip decision makers with the data they need to make informed staffing decisions.

Something as simple as displaying an alert that a person is or will be in overtime could inspire the decision maker to look for other options. When data like overtime is readably available, at the point of decision, better decision are made. If overtime data is sitting in a report somewhere, it is less likely to be used. This raises the important point that data alone is not the answer. However, easy access to meaningful data that is displayed within the natural flow of work and associated with other relevant data can empower effective staffing decisions.

Lets take an example. John is a manager in the ICU and he is looking at the staffing for the next shift. He has had two admits, the unit is busy; he has his hands full and evenings is short 1 RN.

Scenario 1 — John asks one of the nurses from days to stay over. Expensive, but efficient.

Scenario 2 — John opens his iPad and instantly sees staffing for his unit showing that he is down 1RN. With one click he gets a view of staffing in the other Intensive Care Units and sees that none of them have extra staff to offer. He clicks back to his shift and is shown a list of available nurses, two of the three have an indicator letting him know that they would be in overtime if selected. He clicks on the third and instantly a text is sent offering her the shift.

John had easy access to the data he needed to make a cost effective decision. He could see staffing in other units that staffed with the same skill type he needed, he could see who was available, he could see who was in overtime, who was last called off, and seniority at a glance. All of this and he could contact a nurse to come in all in a matter of seconds. John’s decision was not just effective in managing overtime costs. He had data that allowed him to be fair, in compliance with the seniority policy and he minimized risk by avoiding having an over fatigued nurse giving care.

With the availability and affordability of the new generation of staffing solutions on the market, there is not longer any way to justify staffing practices that are not supported by data. The large number of organizations using paper based scheduling, spreadsheets or outdated staffing technologies are missing out on the powerful financial and quality impact that can come from a small data set that is well designed to empower effective staffing decisions.

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