Medical Practice Human Resource Budgets

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Seeking Optimal FTE-to-Doctor Ratios


The full-time-equivalent (FTE) – to doctor (provider) – ratio of a medical practice is often more useful to know than the total amount of staff salary expense, according to industry experts like Dr. Jon Hultman MBA, of Los Angeles, CA.  

Why? Because comparable salaries have a wide geographic variance; and it is just more expensive to practice in New York City, than it is in Phenix City, Alabama.  


Payroll (human resources) typically is the largest singe expense and cost-driver of most medical practices. So, an optimal staffing ratio must be determined for every practice, considering quality, productivity and patient satisfaction at the lowest possible cost.

Reducing the FTE ratio, and hence overhead salary expenses, is desirable only when it does not lower productivity, quality or patient satisfaction. 

Most FTE ratios are significantly high, with no corresponding benefit to the typical medical practice (if there even is such an entity).

Moreover, this FTE excess establishes an environment for which “idle-time” for any given point is about 30%. And, corresponding redundant or unnecessary “task-time” is about 25%. 

In fact, it is often a management truism that smaller FTE ratios may be consistent with higher levels of productivity. On the other hand, lower FTE ratios may actually be consistent with lower levels of productivity, lower medical care quality and higher costs; all other things being equal. 

 The NAHC Review

The National Association of Healthcare Consultants (NAHC), Statistical Report 2000, is summarized below and was considered reliable at the time because the numbers were reported by accountants, not doctors. More current information is now available.

Nevertheless, these benchmarks may serve as a cogent starting-point for HR budget analysis and FTE evaluation:

Specialty                                FTE Ratio 

  • Ophthalmology                     5.19
  • OB/GYN                                 4.35
  • Dermatology                         4.30
  • Otolaryngology                     4.22
  • Hematology                          4.19
  • Oncology                               4.19
  • Family Practice                      4.18
  • Orthopedic Surgery               4.12
  • Pediatrics                              3.79
  • Gastroenterology                  3.75
  • Internal Medicine                  3.51
  • Dentistry                               3.00
  • Urology                                 2.94
  • Podiatry                                2.94
  • Neurology                             2.70
  • General Surgery                   2.50


Now, consider the specialty FTE-to-physician ratios listed above – index them over time for your medical specialty – and consider that famed investor Warren Buffett once said,

“There is a right size of staff for any business operation. For every dollar of sales (professional service income), there is an appropriate level of expense.”  

And so, how does your medical practice, clinic or healthcare organization stack-up to current NAHC benchmarks and their resulting HR budgets?


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Medical “BottleNeck” Accounting

Variance Analysis Re-Invented

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Any healthcare organization usually has several processes involved in the utilization of its patient services. Unfortunately, bottlenecks may arise which constrain the amount of services any given healthcare entity can deliver. 

Accounting Definition 

An accounting “bottleneck” is a process that has a low output and limits total healthcare entity revenues. If a medical business entity wants to increase sales or revenues, it has to solve its bottleneck [ie., access management] problems. 

Traditional Variance Analysis Dilemma 

With traditional variance analysis [VA], managers and administrators analyze the difference between budgeted patient revenues and actual revenues. Typically, differences between budgeted revenues and actual revenues are analyzed as seen in the example below.

Initially postulated by Horngren and Foster for manufacturing processes, VA can now be modified for medical business entity use. 


  Patient Service Units   Contract/UCR Fee    
Budgeted sales revenues 10,000,000 * 1,23 = $ 12,300,000
Actual sales revenues 9,000,000 * 1,21 = $ 10,890,000
          -/- —————-
Total variance         $ 1,410,000
Traditional Assessment
Actual patient revenues were lower than budgeted; and the unfavorable patient sales volume variance was (9,000,000 – 10,000,000) * $ 1,23 = – $ 1,230,000. 


The actual patient revenue price was lower than budgeted as the unfavorable price variance was: ($ 1,21 – $ 1,23) * 9,000,000 = – 180,000.

Traditional variance analysis however does not point out which of the processes were bottlenecks, which caused the negative volume variance.Thus, a normal variance analysis can’t be used to solve bottlenecks in a clinic, hospital or medical practice.

Enter B-N Accounting

In bottleneck accounting however, managers and healthcare administrators determine the bottlenecks in a medical organization.And, a bottleneck accounting report shows which process were bottlenecks occur and how much money is lost in each bottleneck.


Bottleneck Patient Sales Revenues $ 800,000
Bottleneck Dep. II $ 350,000
Other Bottlenecks $ 80,000
  + —————-
Total Volume Variance $ 1,230,000


The managerial accounting modification for “bottlenecks” not only points out the bottlenecks to solve, it also shows which bottleneck is to be handled first.

And so, what are your thoughts on this accounting machination? Please comment.

References: Horngren, C. T. and G. Foster, ‘Cost Accounting, A Managerial Emphasis’, Prentice-Hall, Inc. 1987. 

Related Information Sources:

Practice Management:

Physician Financial Planning:

Medical Risk Management:

Healthcare Organizations:

Health Administration Terms:

Physician Advisors:

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On David Ricardo and “Derived-Demand” Health Economics in Medicine?

On Ricardian Derived Demand – Does it Even Exist?


What it is – How it works

In economics, derived demand is demand for a factor of production or intermediate good that occurs as a result of the demand for another intermediate or final good. In essence, the demand for one is dependent on that whose demand its’ demand is derived from another:


For example, if the demand for a good such as cars increases, then this leads to an increase in the demand for iron ore.


For example, if the demand for a good such as wheat increases, then this leads to an increase in the demand for labor.


So, what about medicine? Saurabh Jha gives us some insight right here!


RELATED: big data

Your thoughts are appreciated.


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