At Axsium where we deal with all things “Workforce Management,” I am interested in and gravitate to innovations and the development of new ideas in healthcare, especially those related to hospital staffing. There is much room for growth and enhancement, so I’m excited to hear what is happening in hospitals and how it is evolving in real time.

Recently, I had the opportunity to go visit with a teaching hospital. The nursing leaders there understand the importance of research and innovation and were willing to review with me a pilot initiative related to “Nursing Intensity.” An approach has been developed over the past few years to better align the nurse-to-patient ratios in the hospital units.

Nursing intensity is a term that is being used to determine what level of effort is required by a nurse to deliver care to a patient. This is different than traditional “acuity” measures. Although there may be slightly different definitions depending on who you ask, in general terms, acuity is the measure of how sick a patient is. What nursing intensity measures is how much time and effort is required of a nurse for the patients.

Acuity reflects the patient’s sickness level.
Nursing intensity reflects the load on the nurse.

This hospital is a Kronos WFC customer, which we talk about on this blog from time to time. The nursing intensity initiative, however, happens upstream from Kronos in Cerner.

Here is how it works: At a patient’s bedside or at the nursing station, nurses enter ratings for patients directly into a Cerner iView custom screen. These ratings are for key areas that most significantly impact the time a nurse will spend with a patient (like “Medication & Fluid Management”). In addition, the nurse will also rate the patient’s ADL (activities of daily living) Dependency. The system then calculates and returns a rating for each of the two categories and a combined numeric value for the patient. Within a range of 1 to 11, the “Total Nursing Intensity Score” provides a snapshot of the realistic nursing time and effort that patient will need.

With this data being gathered on patients multiple times a day – at the start of the shift for some units and as frequent as every four hours in others like an ICU – a valuable data asset has been created for the nursing leadership.

The immediate benefits are obvious to me with my hospital scheduling hat on. First I would create staffing ratios that align to the numerical nursing intensity scores. From there, the data can be imported into a staffing matrix in Kronos WFC Advanced Scheduling for healthcare to drive staffing levels for the unit. With frequent recording of data and subsequent populating of the scheduling system, an accurate picture of the real time nursing needs of the unit can be seen. When this happens for multiple units, a bigger picture emerges of where staff deployment needs to occur and if floating is needed or additional staff needs to be brought in.

In my experience, the nursing intensity score as a data driver for staffing ratios has the potential for a higher level of accuracy in terms of real time needs when compared to other more traditional statistical drivers.

But the potential benefits are even more than driving staffing ratios. The numbers can create a picture of the patients in each unit from the perspective of how well equipped the nurses in that unit are to deal with them. For example (my example – not a real scenario!), think about an ICU dedicated to post-surgical patients (SICU). One day, due to volume reasons, they have an influx of additional cardiac patients who need an ICU as well (a CCU) but the CCU is full. From an acuity perspective, the patients all need an ICU. But from a care perspective, the SICU is not necessarily skilled in dealing with cardiac patients so their nursing intensity level goes up. The nursing intensity data not only drives a need for additional staff but also tells hospital leadership about how their units are being utilized.

At the hospital I visited with, nursing leadership thinks about units who typically handle patients with a nursing intensity score in the mid-range as “progressive” units. If progressive units are filled with patients who have progressive ratings, then their nurse-to-patient staffing ratios should be accurate and effective, based on benchmarks. This gives practical and measured data points when assessing requests for additional FTE’s from the unit.
Personally, I find this to be very innovative in terms of empowering the nurses to provide data that accurately reflects their needs so that patient safety is not negatively impacted and so that hospitals can best manage not only their labor budgets but also their distribution of patients in the units.

Right now, this nursing intensity initiative is still in pilot mode. When talking about next steps, like many other hospitals, the medical center anticipates that change management and user adoption of new techniques will be a challenge. (And what hospital change isn’t a challenge?!) Fortunately, the team knows this and can leverage the data gathered to date to show how, for each unit, it is in their best interest and the interest of their patients to capture this data.

I’ll be looking forward to seeing how this spreads and what other innovations come out of this idea of nursing intensity.