Using Variable Labor Cost Accounting to Understand the True Costs of Care

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Variable labor cost accounting can help healthcare finance leaders drill down into the true reasons for budget variances.


David Janotha, our Industry VP of Healthcare, was recently featured in the Summer issue of HFMA’s Strategic Financial Planning. His article “Using Variable Labor Cost Accounting to Understand the True Costs of Care” is below.

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By David Janotha, Axiom EPM

As the healthcare industry moves to risk-based payment models and population management reimbursement, the need for cost information has reached a critical level. Below is some guidance on how to structure a variable labor cost accounting approach to fully understand the cost of care and quantify operational results. The following method—illustrated with a nursing unit example—can be applied to variable direct labor.

Budgeting Nursing Labor

Nursing units have defined staffing targets, typically for both nurses and aides. The staffing ratios are specific to the day of week and time of day. Costing should use that information to identify direct costs separately from other costs so that the analyses can be based on the true variable portion of the total labor expense.

The first step is to calculate the total direct patient care hours to establish the required number of nursing FTEs (see the exhibit below). The budget can then be set based on the necessary FTEs, tak-ing into account that differentials will be paid for nights, evenings, and weekends.

In this budget example, the average daily census (ADC) is consistent with the staffing ratios. Based on the activity forecast and the budget, the direct, variable RN cost per patient day is $465.90. That is the result of dividing total worked dollars ($39,136) by the number of patient days (84 patient days = ADC of 12x7 days per week).

Costing the Flexed Budget and Actual Results

A flexed budget is used to determine costing in this case. A flexed budget is simply the budgeted cost per unit times the actual volume. The variance that remains after flexing is due to changes in the price or amount of inputs, such as supplies or labor. For example, labor will be more expensive if a lot of overtime is used.

This approach provides the data needed to quantify the amount of variance that is due to the price and amount of labor.

Notice in the costing example that the flexed budget is based on the hours per patient day multiplied by the number of actual patients. Actual expenditures can now be quickly analyzed to determine the cost of efficiency and price variances.

Taking a Deeper Dive

The data is now in place to take a deep dive into an operational analysis (see the exhibit above). There are two types of variance: efficiency and price. The efficiency variance can be quickly calculated as: Flex Budget Hours – Actual Hours, or 485.33 - 576 = (90.67). The cost of inefficiency is calculated by multiplying the hours variance by the budget rate, or (90.67) x $87.36 = (7,920.93).

The presence of orientation hours indicates new staff coming on board. The average hourly rate is likely to be less when new hires are added to replace more experienced staff, which may explain the lower hourly rate. Alternatively, less overtime may have been used than was budgeted. Reviewing hours data with detail by pay type would easily confirm if that was the case. The rate variance can be calculated as (Budget Rate - Actual Rate) x Actual Hours, or ($87.36 - $80.23) x 576 = $4,106.88.

The variance analysis provides significantly more insight into operating results than the standard variance calculation, which would have been calculated as budget minus actual, or $39,136 - $46,210 = $(7,074). Using the revised approach, it becomes clear that the department is effectively managing the price of labor based on the actual cost per hour being less than the budgeted cost per hour. However, more analysis is necessary to determine why additional hours were being used. Fortunately, the approach has been designed to provide insight into that aspect as well.

The budget was based on a census of 12, which was clearly divisible by the targeted staffing ratio. The actual ADC was 13, which is why an additional nurse was needed. The additional nurse either cared for just one patient, or the full patient load was reallocated. Either way, this resulted in an inefficient matching of staff to census. The hour impact can be quickly calculated as (130.67) from the data already provided.

The key labor stats in the exhibit on page 4 provides a comparison of the current budget, the flexed budget, and actual results. The addition of one patient per day on average resulted in an increase in the number of RN hours required from 448 to 485.33. However, actual hours were 576, thus representing a variance that is not explained by the change in volume alone.

The variance data in the exhibit illustrates the true impact of adding one more patient. The staffing ratio discussed earlier dictates another RN being added when census reaches 13. The data show the difference between the additional patient care hours (which is equal to the adjustment from the fixed budget to the flexed budget) and the number of RN hours that will be added as a result of needing one more FTE. The net result is that the department added 168 RN hours to handle the increase of 37.33 patient care hours.

Understanding the Operational Impact

The first stage of the analysis identified an efficiency variance of (90.67) hours when measured against the flexed budget and without any adjustment for staffing ratios. When taking into account that an additional nurse became necessary to maintain a minimum RN-to-patient staffing ratio, the expected efficiency variance increases to (130.67) hours.

The difference in the actual efficiency variance (90.67) and the efficiency variance that is calculated when the need for an additional RN is considered (130.67) demonstrates a reduction of 40 hours, or 44 percent. In essence, the department was able to function at a lower staff-to-patient ratio than the target. While that is commendable, there should be a concern that not meeting the targeted hours per patient day could have an impact on the quality of care or patient satisfaction.

The real key to this analysis is identifying instances where census and staffing tar-gets are not aligned. In those cases, nurs-ing managers must work with the bed placement staff and finance to evaluate why the census exceeded the target and establish guidelines for floor assignments.

Making Cost Data Valuable

This approach to labor costing can be applied with some variation to all other patient care departments within the hos-pital. The variation in approach should be dependent on what the department produces and what data is available to establish costing. For example, the emergency department should quantify hours associated with minimum staffing levels that exceed the volume-based target and put them into a separate cost pool. Managers can then easily see when activity is such that it reduces the amount of unaccounted time associated with the minimum staffing levels.

This approach focuses on the direct cost of care and associated overhead, or inefficiency measures. It makes cost data more valuable to all users, whether they are conducting department analysis or putting together strategic plans.


David Janotha is industry vice president, healthcare, Axiom EPM, Portland, Oregon, and a member of HFMA’s Wisconsin Chapter (djanotha@axiomepm.com).

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