Drexel University College of Medicine
Over the past several decades, there has been an increase in physician demand across all specialties1. This rise in demand coincides with increasing physician shortages, increasing demand for clinical services due to the rising number of insured, and increasing physician turnover within hospitals and multispecialty group practices1. One avenue that healthcare organizations have turned to in order to address this increased demand is the employment of locum tenens physicians.
�These substitute physicians cover established positions when full time physicians are on temporary leave for a maximum of 60 days2. In exchange for the on-demand coverage that they provide, locum tenens are paid significantly more per hour than their full-time counterparts. In recent years, this pay disparity has increased, creating a large incentive for full time physicians to instead leave for the greater pay and flexibility that locum tenens positions offer2.
�With their rising use and rising costs, there are some who question if using locum tenens continues to make financial sense. Despite their wide usage, there is almost no data regarding the finances of using locum tenens in practice due to the proprietary nature of the financial data needed to conduct such a project. Instead, this project created a simulation using national salary averages to find out at what point is using a locum tenens physician more expensive than hiring a full-time physician.
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Introduction
This project specifically focuses on anesthesiologist salary and locum data due to the high usage of anesthesiologist as locum tenens as well as the ease in finding posted financial information.
�Using python, a model was created that plotted the costs of both locum tenens and full-time physicians broken down by hour. For full-time anesthesiologists, the slope of this line is the combined value of the salary and benefits broken down into cost per hour and the y-intercept is the cost to recruit and onboard the anesthesiologist. For locum tenens, the slope is the hourly cost to hire the physician and the y-intercept is zero since there is no initial cost. The intersection of these two plots represents the point where the costs of hiring a locum tenens vs a full-time physician are equal. At any point greater than that intersection, the cost of a full-time physician is less than the locum tenens (Figure.1).
��The strict limitation on this model is that all initial conditions must be known. Due to the protected nature of healthcare finances, this is not feasible. To solve this, the model was run in a Monte Carlo simulation to find generalizable trends. Each of the three parameters (hourly cost of the full-time physician, initial cost of the full-time anesthesiologist, and hourly cost of the locum tenens) was given a mean and standard deviation based on best data. The mean hourly cost of a full-time anesthesiologist was set to $225/hr with a standard deviation of $32.6 and the initial cost was a mean of $90,000 with a std. dev of $50,0003. The locum tenens mean was determined to be $375/hr with a standard deviation of $252 (Figure. 2).
��Each time a simulation is run, these three parameters are given a different value from a normal distribution based on their mean and std. dev. This process was repeated 10,000 times and the data collected. This data set was analyzed to determine its average, standard deviation, and to find what percent of the intersection values were below 400 hours (Table 1).
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Methods
Simulation
Over the 10,000 simulations, it was determined that the mean breakeven point was at 665 hours, indicating that a locum tenens anesthesiologist is more cost effective for the first 665 hours of employment. After that, hiring and retaining a full-time anesthesiologist is a better financial decision.
�Of the trails run, 28% of them had their break even point below 60 workdays, which is the maximum consecutive days a locum tenens can work under another physician’s license. This means that in 72% of the simulations, the point where hiring a full-time anesthesiologist was cheaper fell outside of the maximum work window allowed to locum tenens.
�The majority of locum tenens positions are for short-term coverage that falls well short of these ranges. However, there are situations where a healthcare organization could use this information for hiring decisions and financial best practices.
�The first is for the 34% of healthcare organizations (HCOs) that use locum tenens to meet the rising population demand (citation). Rather than hiring locums for expected short-term coverage, there are a large number of practices that rotate through locum contracts to meet demand in their areas. These HCOs utilize loopholes in the locum tenens rules to allow locums to work in the capacity of a full-time physician. This makes recruitment and retention much easier at the expense of higher pay.
�The second is large HCOs or physician groups that employ many members of a specialty. Over the course of one year, some percentage of them will need to take a leave due to vacation, injury, maternity, paternity, etc. Locum tenens will be hired to cover for these short-term absences. Based on expected or historic absence data, an HCO could save money by hiring an additional full-time physician rather than rely on piecemeal locum tenens coverage.
Ultimately, it should be noted that the calculated standard deviation of 233 is high, which shows how much variability exists within the simulated data. This variability indicates how important it is to take individual needs and context into any hiring decisions involving locum tenens.
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Discussion
Due to the nature of using a simulation for analysis, there are inherent limitations worth discussion. Using a simulated model requires several assumptions to be made about the nature of the data being used. The distributions for full-time anesthesiologist pay, locum tenens pay, and on-boarding fees were assumed to be normal.
Full-time anesthesiologist mean salary and standard deviation was taken as a weighted average from 3 separate self-reported physician salary reports: The Bureau of Labor and Statistics, Doximity, and Medscape. Initial hiring fees for full-time physicians were determined using estimated breakdowns posted by physician recruitment agencies. Anesthesiologist locum tenens pay, and standard deviations was taken from postings on GasWork, an anesthesiologist job listing site. Publicly available data on physician and locum tenens pay had wide variance.
All financial data was taken for data listed in the Northeast region of the United States. Rural and urban data was not separated, which may contain significant differences.
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Limitations
1] ] R. G. Chiu, R. S. Nunna, N. Siddiqui, S. I. Khalid, M. Behbahani, and A. I. Mehta, “Locum Tenens Neurosurgery in the United States: A Medicare claims analysis of outcomes, complications, and cost of care,” World Neurosurgery, vol. 142, 2020.
[2] K. Redfield, “Locum Tenens Compensation Trends by specialty: 2022 report,” Locumstory.com, 29-Mar-2022. [Online]. Available: https://locumstory.com/spotlight/locum-tenens-compensation-trends/. [Accessed: 15-Oct-2022].
[3] “Physician Pay Reports,” U.S. Bureau of Labor Statistics, 31-Mar-2022. [Online]. Available: https://www.bls.gov/oes/current/oes291211.htm#(8). [Accessed: 15-Oct-2022].
References
Living La Vida Locum: A Simulation to Determine When You Are Paying Too Much for Your Locum Tenens Coverage
James Cross, MBA, Yeshwanth Lolla
Background: Since its inception in the 1970’s, the use of locum tenens as temporary physician medical coverage, specifically within anesthesiology, has gained in popularity with many organizations using locum tenens to meet rising patient demand1. As contract workers, locums forgo benefits in exchange for higher hourly pay. Over the past few years, these hourly rates have grown drastically compared to those of full-time physicians2. This project investigates at what point is it more economically feasible to hire a full-time anesthesiologist rather than a locum tenens.
�Methods: A python model was created that took inputs for the hourly rates of locum positions and plotted it against the hourly rate of full-time anesthesiologists along with hiring costs. The intersection of these two lines indicates the breakeven point in hours where there is no cost differential between the two. Any hours above this number would favor hiring a full-time anesthesiologist, numbers below favors the locum. This model was run through a Monte Carlo simulation where the parameters of the hourly rates and onboarding costs were varied and evaluated.
�Results: Across 10,000 simulations, the mean breakeven point was 665 hours with a standard deviation of 233 hours. Of the trials, 28% of them had a breakeven point below 60 working days.
�Conclusions: If a locum tenens is utilized for fewer than 665 hours, it is less expensive than hiring a full-time physician. However, there is great variability in this, and each hiring decision needs to be made with context.
Abstract
Results
Average Breakeven Time (hours) | 665.2 |
Standard Deviation (hours) | 233.4 |
% of time breakeven value below 60 days | 28% |
Breakeven Point Model
Monte Carlo Simulation of Breakeven Point Model
Distribution Plot of Breakeven Points
Results Table
Figure 1. Example of breakeven point model with example inputs shown for full-time employee hourly rate, employee hiring fee, and locum hourly rate.
Figure 2. 12 graph sample of the 10,000 trials run in the Monte Carlo simulation. Full-time employee hourly rate, employee hiring fee, and locum hourly rate parameters varied and ran to determine breakeven points.
Figure 3. Distribution of Monte Carlo simulation breakeven points. The average of 665.2 hours is represented by the red line.
Scan this QR code for a demonstration of the breakeven point program.
Steps
Table 1. A summary table with the average breakeven time, standard deviation, and percentage of time breakeven value was below 60 days, which is the maximum contract length for a locum tenens.
Future Prospects
The date used as the basis for this simulation was focused on anesthesiologists in the Northeastern United States. Comparing the breakeven points between specialties and regions could uncover interesting trends.
Working with a large healthcare organization that would allow access to financial information on full-time and locum tenens hiring finances would allow for much more accurate distributions of the data sets, creating a more accurate and usable simulation. The largest limitation of this project was working without much real-world data to base the simulation from.
Acknowledgements
I would like to thank Christopher Fichman for his help in Python coding and Yeshwanth Lolla for his help in creating the visual components of this project and streamlining my model.
I would also like to thank my advisor Michael Howley for his support in pushing me to learn an entirely new skillset for this project.