When we have historically examined the issues of daily census and staffing; i.e. nurse staffing, it continues to be one of the most major work environment concerns for nurses. No different than it has always been, during the interview process 9 times out of 10 the candidate is going to ask about staffing and what will their workload be. There is a newer approach to nurse staffing that hospitals, for the most part, haven't really picked up on yet, one that should replace the long-standing model of calculating staffing needs based on average daily patient census at midnight, using hours per patient day (HPPD). This newer approach provides an opportunity to, literally, allow a nurse to schedule her days and hours for six months, perhaps even for an entire year. It is the power of predictive analytics that will allow us to finally do things differently in staffing. Let’s talk about “big data” and how that can be used to help with staffing.
Why does appropriate levels of nurse staffing matter? Because if it's not done right it’s a big dis-satisfier for both nurses and patients. Staff don't like floating to other units and do not appreciate being “called off” a regularly scheduled shift for low census days. During COVID, staff were not allowed to take PTO, resulting in way too much overtime and in turn increased levels of stress, fatigue and burnout. Nurse staffing typically is still based on the average daily patient census at midnight. But since there's a lot of “churn” on nursing units, the workload actually required for that 24 hour shift rarely aligns with what the census is at midnight. Scientific data confirms that staffing absolutely impacts the quality of patient care and care equality. So using logistics and operations research and applying “big data” to understand the complexities of nurse staffing is the best means of assuring quality patient outcomes and nurse satisfaction.
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Part of the challenge of creating the nurse staffing budget has always been data siloes—HRIS system, payroll system, finance budgeting system, a staffing and scheduling system—plus the operational realities of contract labor, a float pool, and a variety of shift and premium pay options. The data silos typically do not “talk” to one another, making for a really complicated logistics problem: who do you have, how, where and when do you deploy them, and what is the optimal staffing solution to employ?
1947 was the beginning of research that studied how operations in an organization occur. The availability of more powerful computers led to the invention of linear programming to understand complex models. An interesting comparison is with the military: how does it assure enough soldiers with enough uniforms, enough food, enough guns, ammunition and equipment to the right place at the right time when needed? That level of complexity is really no different than that with nurse staffing. Operations research has been examining many of the same complex problems: what are the logistics required for the optimal flow of people, goods, information and resources, from point A to point B, when needed at the right time. Thankfully, a much more scientific, precise approach to nurse staffing than ADC is available, because when nursing units are staffed based on an average, the result is inevitably an over or under; i.e. rarely an optimum number of staff.
There are the five “rights” of staffing: the right number of staff, the right skills, right location, right time and at the right cost—to achieve the best patient outcome. More powerful predictive analytics allows for an examination of this supply and demand over a period of three years: what a unit census has been, who’s on FMLA leave, how many full-time, part-time and float RNs have been available, when has “churn” happened on a unit, etc. Utilizing linear programming, computers, and big data to solve for best staffing has proven to be very powerful. Think of the analogy of a Rubik's cube and how many spins it takes to create the perfect alignment. Nurse staffing entails the same level of complexity and interconnectedness.
The initial challenge is do you have enough core staff—full-time, part-time and per diem. Then, how large is and how much flexibility is in your float pool, followed by availability of that next layer, contract labor. When using a scientific approach for nurse staffing and by looking at 3 years of data’s long-term trends, patterns emerge that you might not see when examining a single year of data. To optimize the nursing workforce one needs to understand precisely which staff are needed where and when, along with how many core, float and contract labor are required to meet patient care demands. Nurse staffing is a complex, complicated supply and demand problem, well suited for much more sophisticated solutions than the traditional HPPD and ADC.
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