16Oct

The cost of Nurse turnover is killing hospital financials and cripling bottom lines. Data continues to identify the costs associated with nurse turnover. In my post "Is there a Fix for the Nurse Crisis in America" (CLICK HERE TO READ). I share that RN turnover costs an organization 1.3 times the average salary. The costs are associated with the tasks of onboarding, orienting, and training the new nurse. In my post titled, "New York Boosts Starting Pay for State Employee Nurses" (CLICK HERE TO READ), the average RN in New York earns $93,320.

The theory I am testing is that a positive relationship exists between pay satisfaction and the intention to leave the job. A simple math equation shows that an RN leaving NY costs the hospital $121,316 ($93,320 X 1.3). The nurse turnover rate is 25.7% in the North East (According to NSI , 2022). This is why not paying more costs more!

A hospital employs 100 full-time Registered Nurses. I understand that rates vary due to years of experience, but for simplicity, we can divide this is half. 50% of the nurses earn the average NY RN salary of $93,320 and 50% earn the New graduate salary estimated around $61,260. **The annual RN salary costs (50x$93,320 + 50 x$61,260) = $7,729,000.**

A rate of 25.7% turnover at a cost of $121,316 for average salary and $79,638 costs the hospital $1,168,546.60. This accounts for 12.7% average salary turnover and 13% new graduate nurse turnover. This rate all depends on specific turnover ratios at each site. **The example hospital pays out $1.168 million for RN turnover.**

I am assuming that my theory is correct and that positive pay satisfaction results in a deacrease in the intention to leave. For this example, I use the baseline turnover rate of 15.9% per the NSI 2022 report. My theory is that the COVID-19 pandemic marginalized and highlighted the value of the essential working RN. Pay has boiled over the surface when when other factors are present that impact job satisfaction. For the sake of argument, we will hypothesize that it is logical to reduce turnover by 9.8% if we get baseline pay correct.

A reduction of 9.8% in turnover costs the hospital $490,253.40. I account for 5% of the reduction for new graduate nurses and 4.8% for average salary nurses.** The removal of 9.8% in turnover results in $678,293.20 fewer costs to the hospital.**

This next part is tricky and all hypothetical. I don't claim to know how much is enough to make a nurse stay, however, we can make assumptions. If the average salary in New York state hospitals is $121,316, we can add increments of 5% and identify the salry costs to compare to the turnover costs. We are bound to find a happy medium at some point**. **

A 5% increase means that average RN salaries are $127,381.80 for nurses and $83,619.90. The average annual salry costs the hospital $10,550,085. This is $2,821085 more in employee costs. Now deduct the turnover costs at the rate of 15.9%, which amounts to $409,737.51.

Hospital A pays out $7.729 million annually with a turnover cost of $1.168 million and hospital B pays out $10.55 million anually with a turnover cost of $678 K. It appears at the surface that raising RN salaries by 5% does not pay off for the hospital. Agency expenses are not factored in at this point. Reducing turnover by 9.8% accounts for fewer demand for agency nurses. For every 20 agency RNs eliminated, a hospital will save $4.3 million. A reduction of 9.8% in agency nurses for Hospital B equals $1,050,750 in savings. Year one for hospital B equals $9.5 million, while hospital A equals $7.729 million**. In 2023 hospital B factors in $409K for turnover and annual costs equal $9.01 million and hospital A costs equal $8.897 million.**

In 2024 hospital B spent $9.419 million, and hospital A spends $10.065 million. At year two the increase of 5% pays off as hospital B spends less than hospital A. I understand typical assumptions account for annual salary increases, but my math shows that if we reduce turnover, pay dedicated core staff more and rely less on agency nurses, hospitals will save over time.

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