The healthcare sector is awash with data pouring in from every corner of the care spectrum. From prescription drug trials to emergency room visits, analytics is proving to be a powerful tool in solving complex problems that have a direct impact on the quality of care a patient receives, including managing the staff that delivers that care.
Labor analytics can be used in any industry, but in the case of healthcare, it’s helping hospitals make strides in streamlining the scheduling, workflows and retention rates of nursing staff.
A smart approach to analytics includes the tools and team necessary to get the most out of the data, the depth of which many providers don’t even realize they have.
According to a 2016 report from McKinsey Global Institute, the healthcare industry has yet to capture even 30% of the potential value from data available five years prior, when the institute released its initial report highlighting the potential of Big Data across a number of industries.
Analyzing the healthcare workforce is about as complex as you might expect. It takes a good amount of planning and a clear idea of what sort of insights a company wants to glean from the data, as well as a willingness to drill down to data on a micro level, such as specific nursing units versus a hospital wide approach.
The process of making the most out of an organization’s data comes down to establishing effective labor metrics.
A report from Healthcare Insights highlights the importance of measuring an organization’s labor cost ratio, calculated by “dividing salaries, wages, fringe benefits and contract labor by total net revenue.”
Successful hospitals, for example, typically have a labor compensation ratio below 40%. Getting there, however, does not mean firing people. Rather, it requires a careful examination of overtime use, agency use, the mix of skills present on the floor at any given time, pay rates and shift assignments.
Another important metric is full time equivalent (FTE) leakage. This term, coined by labor management technology service Avantas, refers to the number of hours a full-time staff member has not worked, but was supposed to. This can happen for many reasons, including call-outs without time being made up and scheduling by a manager that doesn’t see nurses fulfilling their full-time commitments.
Identifying leakage amounts can be difficult, but it’s worth the time as it also reduces the amount of agency workers used to fill in for these nurses missing their shifts.
One example, profiled by Health Leaders Media, comes from St. Louis based Mercy Health Systems. The company created its own scheduling tool for nursing staff using aggregated data from previous tools and combining it with predictive analytics models to calculate the number of FTE hours that would be needed based on predicted patient volume and historical data, including hospital activity during certain times of the year.
This can help predict where leakages are likely to occur. It also looks at staff cancellations and allows schedulers to get nurses back into the schedule during the pay period to ensure they are fulfilling their full-time commitment. The result was $4.3 million in savings in the first year of the program being in operation.
Nurse scheduling issues often stem from a lack of awareness regarding patient forecasts. By using analytics to determine which times nurses are the busiest, healthcare organizations can beef up their staffing levels as well as having members of the staff who may possess a specific skillset on duty at the right times.
Some analytics platforms, when given the proper data, can provide forecasts for patient demand as much as four months in advance of the scheduled shifts. This provides ample time for a facility to ensure that its shifts are covered, and a proper understanding of the needed staff structure is in place for each shift.
According to a report from AMN Healthcare, the benefits for facilities employing an analytics approach to nurse scheduling has been significant, including having 75% of open shift hours filled more than two weeks in advance, increases in staff satisfaction and anywhere from 4-7% savings in labor spending.
One Eye on Workflow
Incidental worked time is an area that may be necessary, but can also be reduced to diminish cost. This is defined as “any instance where staff members clock in before their scheduled shift, or have to work beyond the scheduled hours,” according to Avantas. In some cases, these hours are necessary. But research from Avantas shows that about 60% of this time can be avoided by improving workflows.
That 60% can add up quickly. In a facility with around 300 beds and 20 nursing units, trimming the excess incidental worked time could lead to as much as $560,000 in savings. Some methods of doing so include examining workflow processes to ensure smooth transitions from one shift to another.
New technology can provide any number of hurdles for a nursing staff to overcome. Considering nursing workflows in the design and implementation phase can help smooth out some of the issues nurses may face during the course of a shift, as well as providing education and training sessions to the staff required to use the new technology.
Additionally, with an analytical approach being applied, specialty staff that typically handles non-nursing tasks should be on hand to prevent nurses having to put in extra hours to lend a hand in these areas, another aspect of poorly organized schedules that leads to job dissatisfaction and high turnover rates.
More Efficiency Leads to Happy Nurses, Low Turnover
One of the major costs many providers find themselves battling is that of staff turnover. According to a study from Nursing Solutions Inc., the average cost of turnover for a bedside registered nurse ranges from $37,700-$58,400. That equates to a cost ranging anywhere from $5-8 million per year for the average hospital.
A key contributor to high nursing turnover comes in the form excessive overtime hours. While some overtime work is necessary, Avantas research finds that the amount of overtime nurses can be forced to work before it begins to wear on their patience is somewhat low. The average amount of overtime the survey found at hospitals was around 4.6% of a nurse’s working hours. As it turns out, the point at which turnover begins to escalate is around 3.1%.
Getting this number down can be done through a metric known as “core as contingency,” according to an article from Becker’s Hospital Review. This is essentially a measurement of how much the “core” or full-time staff of the hospital is being asked to fill gaps in coverage and work over time hours as opposed to agency fill-ins being brought in to help out. If nurses are working too much overtime and end up quitting, it ends up being more expensive than the cost of agency nurses in the long run.
Dissatisfaction with workflows and scheduling are among the top reasons that nurses resign. By limiting the number of overtime shifts and not forcing nurses to perform duties outside of the nursing purview, organizations increase their nurse retention rates and lower costs at the same time.