Advanced data analytics can help identify correlations to ensure optimal staff scheduling, improving patient flow and efficiency.
Recent research shows that lower nurse staffing levels can negatively affect performance metrics in emergency departments (EDs). One study, for example, suggests a correlation between adequate nurse staffing and patients’ length of stay and with the number of patients leaving without being seen. But suggesting that any one element can negatively impact such a dynamic environment can be dangerous. What other conditions might be affecting performance metrics, and how can an advanced data analytics solution help identify and optimize those conditions?
The nurse staffing study, published in the Western Journal of Emergency Medicine, looked at the electronic health record (EHR) database from an urban public hospital with an average of 290 ED visits per day. It compared daily nursing hours with the percentage of patients who left without being seen, door-to-discharge length of stay, and door-to-admission length of stay.
The study makes a case for a direct link between negative outcomes and nurses’ role in affecting performance metrics. When nursing staffing hours were at their lowest quartile:
- There was an increase of almost 30 minutes in door-to-discharge length of stay per patient.
- Nine more patients a day left without being seen by a provider.
The study concluded that scheduling fewer nurses increases wait times and impacts how many patients may be seen per day. This may not only be detrimental to patient safety and satisfaction scores, but it also fails to use ED resources efficiently and decreases revenue.
Improving Physician Productivity
The big picture is not that simple, of course. There’s also evidence that improving physician productivity can reduce unmet patient demand (the number of patient arrivals considered above the average physician productivity) by up to 69 percent.
An article in Canadian Journal of Emergency Medicine, based on a study on “Developing emergency department physician shift schedules optimized to meet patient demand,” indicates that performance metrics could be improved by aligning physician productivity with patient arrivals at the ED. The study looked at patterns in patient arrival rates “to determine the appropriate number of shift schedules in an acute care area and a fast-track clinic.” It also looked at the possibility of optimizing physician scheduling so that patient arrivals are aligned with the productivity rates of the attending ED physicians.
The results showed that using the planning model improved the shift schedules, reducing the unmet patient demand in three areas:
- 19 percent in the schedule suggested by the planning model
- 39 percent in the schedule with an additional acute care physician
- 69 percent in the schedule with an additional fast-track clinic physician
Reducing Numbers Per Shift
Interestingly, other research showed that fewer nurses could actually be optimal, which would save on ED staffing costs. A case study from February 2017 focused on optimizing the number of ED nurses by implementing a linear programming (LP) model. The study found the estimated number of nurses used within the LP model was less than the number of ED nurses typically working any given shift:
- The number of nurses on the morning shift went from 26 to 17.
- Evening shift staffing was reduced from 24 to 17.
- Staffing on the two night shifts went from 34 to 28.
The results suggested that optimization could smooth this discrepancy, provided there’s an understanding of what factors are affecting the allocation and distribution of ED nurses.
Looking at just one correlation can be misleading.
While research clearly shows a correlation between lower nursing hours and some negative performing metrics, nursing hours alone might not impact the metrics the researchers point to. Because of the dynamic nature of the ED environment, considering any correlation in isolation can be misleading.
Instead, HCOs should be asking themselves what else might be going on at their locations. One possibility — also backed by the research — is that perhaps most of the negative performance is happening during certain peak times, and that nursing and physician hours should be dynamically allocated.
d2i’s Scheduling Optimization Can Help
d2i can help identify when lower nursing hours impact performance the most. Using advanced analytics allows EDs to improve patient satisfaction and other outcomes. We can help HCOs track arrivals by hour of day, day of week, and by acuity to provide insight into how best to schedule providers, thus improving throughput and minimizing the impact of a poorly staffed shift.