Introduction
In 2019, US pet owners spent approximately $96 billion in pet products and services, 30.6% of which represented pet health-care services.1 The market for pet health care and pet products is expected to grow because millennials outnumber baby boomers, representing the largest living adult generation in the US,2 and have higher rates of pet ownership than any other generation.3
Meeting the demands of pet owners while staying competitive in this fast-growing market requires that pet health-care providers increase productivity and technical efficiency. The number of employees per veterinary practice in the US has been increasing over the past 2 decades. Data from the AVMA Biennial Economic Survey indicate that the mean number of full-time–equivalent (FTE) veterinarians per practice grew from 2.1 in 1999 to 2.4 in 20094 and 2.5 in 2018.5 Furthermore, the percentage of veterinary establishments with > 20 employees more than doubled between 2010 and 2016.5
Although an increase in the number of employees per practice could lead to a greater number of patients seen and higher gross revenue from services, it could also introduce lower productivity and poorer efficiency if those employees are not used to their full capacity. The contributions of nonveterinarian staff to practice revenue and productivity in the US have not been fully investigated. According to a non–peer-reviewed report6 of 2007 US veterinary practice data, gross revenue increased by $93,311 for each additional credentialed veterinary technician employed by a private practice. However, the ratio of noncredentialed veterinary technicians to veterinarians was not significantly (P = 0.34) associated with gross practice revenue, and the analysis did not control for variables such as practice owner and other practice characteristics. In Ontario, Canada, survey-based research7 concerning the impact of nonveterinarian staff on practice revenue in 2018 (SM Roche, Acer Consulting, email, January 18, 2022) showed a significant (P < 0.001; n = 112) increase (mean ± SE, $79,118 ± 21,146) in gross annual revenue per veterinarian with each 1-person increase in the number of registered veterinary technicians employed per veterinarian. An earlier, brief report8 of data from a 2003 Ontario Veterinary Medical Association Economic Survey suggested that practices with more nonveterinarian staff than others earned more revenue, and practices with a higher nonveterinarian-to-veterinarian ratio were more efficient.
The main objectives of the study reported here were to quantify the contributions of veterinarians and nonveterinarian staff to revenue and veterinarian productivity (defined as number of patients seen/FTE veterinarian/wk) in private mixed animal (MA), companion animal exclusive (CAE), and companion animal predominant (CAP) practices in the US and determine the staff-to-veterinarian labor ratios (SVLRs) that maximized these 2 practice outputs.
Materials and Methods
Practice owner survey
Data used in this study were obtained from the AVMA Practice Owner Survey conducted in 2020 by the Veterinary Economics Division in collaboration with the University of Florida. This annual survey aims to collect data on veterinary practices and their owners with overall objectives of identifying drivers of practice productivity and generating business benchmarks.
Specifically, veterinary practice–owning AVMA members (whether sole owners or part owners) were asked to provide demographic information and practice-related data such as gross revenue, number of exam rooms, hours of operation, staff utilization, revenue sources, and expenditures. Electronically administered via an online survey tool (Qualtrics), the survey pertinent to the present study was first disseminated in August 2020 and officially closed in September 2020, allowing 4 weeks for survey completion. Reminder emails were sent once per week during this period.
Only owners who identified their practices as MA, CAE, or CAP practices were considered for inclusion in the study. Only owners of private practices (independently owned businesses) that had been in business for at least 2 years were included. Owners were excluded if they did not provide data for the main variables of interest. To select a sample generally representative of the target population, owners with extreme values, as determined on the basis of the normality assumption (ie, values > 3 SDs above or below the mean), for the variables of interest were excluded.
Data compilation
Data regarding the respondents (age, gender [male or female], and number of years of practice ownership) and the veterinary practices they owned (geographic location, number of years in business, approximate gross revenue in the previous tax year [2019], estimated share [%] of gross revenue for various types of services, total labor/category of staff [see subsequent definition], number of exam rooms, and number of patients seen/FTE veterinarian/wk) were compiled from the 2020 survey. Practice locations were grouped for analysis by US Census regions. Options for various types of services included dentistry (option 1), surgery and anesthesia (2), imaging (3), laboratory tests (4), and wellness exams and vaccinations (5), along with sales of drugs and pet products (6), food and feed sales (7), and other services that would not necessarily require a veterinarian’s involvement (8; eg, grooming, bathing, boarding, and behavior training). Options 1 to 5 were then grouped as service sales, and options 6 to 8 were grouped as nonservice sales.
Total labor, estimated as the number of staff multiplied by the mean total number of hours worked per week, was computed for 3 staff groups for each practice: veterinarians, technicians (credentialed and noncredentialed veterinary technicians or veterinary assistants), and nonmedical staff (eg, administrative personnel, reception staff, client-relations specialists, and kennel personnel). These values were subsequently used to compute SVLRs.
Statistical analysis
Descriptive statistics were computed to summarize general characteristics of the practices and their owners. Revenue and veterinarian productivity were hypothesized to respond to both veterinarian and nonveterinarian labor after controlling for practice owner and practice characteristics, and these hypotheses were tested by means of multivariable ordinary least-squares regression. The response variables in these models were gross revenue from services (revenue model) and number of patients seen per FTE veterinarian per week (productivity model).
In the revenue model, explanatory variables that were considered consisted of owner gender (male or female) and age and, for the practice, the number of years the practice had been in business, number of exam rooms, total number of hours worked by veterinarians per week, technician-to-veterinarian labor ratio (TVLR; with labor measured in hours), nonmedical staff-to-veterinarian labor ratio (NVLR), and percentage of gross revenue derived from dentistry, surgery and anesthesia, imaging, laboratory tests, and wellness exams and vaccinations (ie, service sales). In addition to the 2 SVLRs (TVLR and NVLR), a quadratic term was added for each ratio to allow a diminishing marginal return. The principle of diminishing marginal return posits that there exists a point in the response curve whereby a 1-unit increase in an input increases the response variable at a lower rate than the previously added unit.9
In the productivity model, owner gender, age, and number of years of ownership experience as well as the number of years the practice had been in business, number of exam rooms, mean total number of hours worked by veterinarians per week, TVLR, NVLR, primary focus of the practice (CAE [referent] vs CAP vs MAP), and geographic location (referent = northwest) were used to explain the variations observed in number of patients seen per FTE veterinarian per week. A squared term for age was added to account for the assumption of diminishing marginal productivity with respect to age.10
Each model was tested for heteroskedastic variance of the residual (error) terms with the Breusch-Pagan test. Results indicated that the variances in revenue and productivity were not constant across practices and generally increased with practice size (measured in veterinarian labor). Because constant variance is a critical assumption of the ordinary least-squares regression approach, the response variables in each model and the number of veterinarian hours were log transformed with natural logarithms.
All analyses were performed with the aid of statistical software (SAS version 9.4; SAS Institute Inc). The frequency procedure (PROC FREQ command) was used to summarize the data, and the regression procedure (PROC REG command) was used to estimate the model parameters. Values of P < 0.05 were considered significant.
Results
The survey was sent to 19,045 veterinarians who had identified themselves as practice owners in the AVMA membership database. Overall, 1,542 of these practice owners responded to the survey (8.1% response rate). Only responses from owners of MA, CAE, and CAP practices were considered for inclusion, constituting 86.3% (n = 1,331) of the survey respondents. After removal of private referral practices, corporate-owned practices, practices in business for < 2 years, and practices with outlying data, 409 (30.7%) practice owners and their practices remained and were included in the study.
Characteristics of practice owners and practices
Practice owners consisted of 232 (56.7%) men and 177 (43.3%) women with a mean ± SD age of 57.7 ± 9.1 years (range, 31 to 74 years). On average, female owners (mean ± SD age, 54.6 ± 9.4 years) were younger than male owners (60.0 ± 8.0 years). Overall, owners had a mean of 30.6 ± 9.5 years of total professional experience and 27.9 ± 9.0 years of ownership experience.
Owners represented CAE (n = 259 [63.3%]), CAP (92 [22.5%]), and MA (58 [14.2%]) practices. Practices had been in business for a mean of 33.4 years (range, 2 to 60 years) and had a mean of 3.1 ± 1.5 exam rooms. Practices were located in the southern (n = 150 [36.7%]), midwestern (119 [29.1%]), western (91 [22.2%]), and northeastern (49 [12.0%]) census regions of the US.
For included practices, the mean (range) total number of hours worked by veterinarians, technicians (credentialed and noncredentialed veterinary technicians and assistants), and nonmedical staff per week was 99.7 (20.0 to 396.0), 186.9 (0.0 to 1,440.0), and 179.0 (0.0 to 1,575.0), respectively. These hours were worked by a mean (range) of 2.7 (0.5 to 10.6) FTE veterinarians, 5.0 (0 to 38.4) FTE technicians, and 4.8 (0 to 42.0) FTE nonmedical staff. Mean ± SD values for TVLR and NVLR were 2.0 ± 1.8 and 1.9 ± 2.1, respectively.
Revenue and productivity
Practice revenue from service and nonservice sales in 2019 was summarized by source (Table 1). Total gross revenue ranged from $50,375 to $5,487,300. Revenue from service sales ranged from $10,328 to $2,720,000, and revenue from nonservice sales ranged from $0 to $5,355,000. On average, practices earned $593,025 from service sales and $405,476 from nonservice sales for an overall mean gross revenue of $998,502. The highest contributor to practice revenue was wellness exams and vaccinations, and the second highest contributor was sales of drugs and pet products.
Summary data for revenue ($) from various sources in the previous year (2019) reported by owners of 409 mixed animal, companion animal exclusive, and companion animal predominant veterinary practices in response to the 2020 AVMA Practice Owner Survey.
Revenue source | Mean | Median | Range |
---|---|---|---|
Service sales | |||
Dentistry | 61,853 | 43,875 | 0–420,000 |
Surgery and anesthesia | 130,989 | 92,064 | 0–1,080,000 |
Imaging | 62,894 | 38,250 | 0–510,000 |
Laboratory tests | 136,735 | 100,980 | 0–812,000 |
Wellness exam and vaccination | 262,408 | 192,824 | 0–1,360,000 |
Total service sales | 593,025 | 456,000 | 10,329–2,720,000 |
Nonservice sales | |||
Drugs and pet products | 238,499 | 160,000 | 0–4,851,000 |
Food and feed | 52,752 | 36,550 | 0–504,000 |
Other | 114,226 | — | 0–2,000,000 |
Total nonservice sales | 405,476 | 268,897 | 0–5,355,000 |
Total revenue | 998,502 | 767,125 | 50,375–5,487,300 |
— = Not calculated.
The mean number of patients seen per FTE veterinarian per week (used as a proxy for veterinarian productivity) was 78 patients/FTE veterinarian/wk (range, 11 to 200 patients/FTE veterinarian/wk). When broken down by primary focus of practice, this value was highest for CAE practices (mean ± SD, 84 ± 41 patients/FTE veterinarian/wk), followed by MA practices (83 ± 46 patients/FTE veterinarian/wk) and CAP practices (75 ± 34 patients/FTE veterinarian/wk).
Influence of owner and practice characteristics on revenue and productivity
Results of ordinary least-squares regression modeling of the influence of practice owner and practice characteristics on revenue and productivity were summarized (Table 2).
Results of multivariable ordinary least-squares regression to identify associations between various practice owner and practice (n = 403) characteristics and gross revenue from service sales (ie, revenue) and number of patients seen per FTE veterinarian per week (ie, productivity).
Revenue | Productivity | |||
---|---|---|---|---|
Variable | Estimate ± SE | Percentage changea | Estimate ± SE | Percentage changea |
Practice owner characteristic | ||||
Male (vs female) | 0.209 ± 0.042 | 23.2b | 0.056 ± 0.053 | 5.8 |
Age (a) | –0.003 ± 0.002 | –0.3 | 0.055 ± 0.027 | 5.6c |
Age2 (a2) | NA | NA | –0.001 ± 0.0002 | NRc |
No. of years of ownership experience | NA | NA | –0.001 ± 0.004 | –0.1 |
Practice characteristic | ||||
No. of years in business | 0.002 ± 0.001 | 0.2 | 0.001 ± 0.001 | 0.1 |
No. of exam rooms | 0.076 ± 0.017 | 7.9b | 0.031 ± 0.016 | 3.1 |
Mean total weekly hours of operation | NA | NA | –0.001 ± 0.002 | –0.1 |
ln (total No. of hours worked by veterinarians/wk) | 0.879 ± 0.044d | NRb | NA | NA |
ln (mean No. of hours worked/FTE veterinarian/wk) | NA | NA | 0.113 ± 0.081 | NR |
TVLR | 0.186 ± 0.025 | 20.5b | 0.058 ± 0.030 | 6.0 |
NVLR | 0.157 ± 0.021 | 17.0b | 0.135 ± 0.025 | 14.4b |
Percentage of revenue from specific source | ||||
Dentistry | 0.019 ± 0.004 | 1.9b | NA | NA |
Surgery and anesthesia | 0.010 ± 0.003 | 1.0b | NA | NA |
Imaging | 0.021 ± 0.004 | 2.1b | NA | NA |
Laboratory tests | 0.017 ± 0.003 | 1.7b | NA | NA |
Wellness exams and vaccinations | 0.012 ± 0.002 | 1.2b | NA | NA |
Practice type | ||||
CAP vs CAE | NA | NA | 0.136 ± 0.059 | 14.5c |
MA vs CAE | NA | NA | 0.055 ± 0.076 | 5.6 |
Census region | ||||
Northeast vs northwest | NA | NA | –0.176 ± 0.085 | –16.2c |
South vs northwest | NA | NA | –0.032 ± 0.060 | –3.2 |
West vs northwest | NA | NA | –0.122 ± 0.069 | –11.5 |
aValue represents the percentage change in revenue or productivity associated with a given category of categorical variable or with each 1-unit increase in a given continuous variable. b,cIndicated value is significantly (bP < 0.01; cP < 0.05) different from 0. dThis coefficient, from the simplified model lnY = lnX, is an elasticity, which can be interpreted as indicating that a 1% increase in the indicated variable (X) leads to a 0.879% increase in revenue (Y).
CAE = Companion animal exclusive. CAP = Companion animal predominant. ln = Natural logarithm. MA = Mixed animal. NA = Not applicable. NR = Not reported owing to the logarithmic or squared nature of the data. NVLR = Nonmedical staff-to-veterinarian labor ratio (determined from number of hours worked). TVLR = Technician-to-veterinarian labor ratio (determined from number of hours worked).
Veterinarian labor—For each 10% increase in total hours worked by veterinarians per week, revenue increased by a mean of approximately 9%. However, no significant association was found between mean number of hours worked per FTE veterinarian per week and productivity.
Technician and nonmedical staff contributions—Both revenue and productivity were found to be sensitive to variations in SVLRs. Specifically, a 1-unit increase in the TVLR was associated with a 20.5% increase in revenue (P < 0.001) but was not associated with any significant change in productivity. On the other hand, revenue and productivity increased by 17.0% and 14.4%, respectively, for each 1-unit increase in the NVLR (P < 0.001).
Impact of owner characteristics—Overall, revenue from practices owned by men was significantly (P < 0.001) higher than revenue from practices owned by women (ie, a difference of 23.2%). However, there was no evidence of a significant difference in productivity between men- and women-owned practices (P = 0.29). Although practice owner age was not significantly (P = 0.20) associated with revenue, each 1-year increase in age was associated with a 5.6% increase in productivity (P = 0.04). No association was found between number of years of ownership experience and productivity (P = 0.86).
Impact of other practice characteristics—Number of years the practice had been in business was not significantly associated with revenue or productivity. In terms of primary focus, CAP practices had 14.5% higher productivity than did CAE practices (P = 0.02). Practices located in the northeast had significantly (P = 0.04) lower productivity (16.2% less) than did practices located in the northwest.
Veterinary services such as dentistry, surgery and anesthesia, imaging, laboratory tests, and wellness exams and vaccinations had various significant (P < 0.001) influences on revenue (Table 2). The largest increase was found for imaging, for which a 1% increase in percentage of revenue from imaging was associated with a 2.1% increase in overall revenue. On the other hand, the smallest influence was found for surgery and anesthesia, for which a 1% increase in percentage of revenue from surgery and anesthesia was associated with a 1.0% increase in total revenue.
Optimal SVLRs
When controlling for other variables, a TVLR of 9:1 and an NVLR of 8:1 were associated with the highest revenue (Figure 1). In terms of productivity, the optimal NVLR was 10:1; the optimal TVLR was 14:1, but, again, this variable was not significant in the multivariable model.
Discussion
The present study revealed several factors associated with revenue and veterinarian productivity (defined as number of patients seen/FTE veterinarian/wk) in US private MA, CAE, and CAP practices, controlling for other variables. Of relevance to the study objectives and hypotheses, revenue from service sales increased significantly with increasing TVLR and NVLR (a 20.5% and 17.0% increase, respectively, for each 1-unit increase in these ratios), whereas productivity increased significantly with increasing NVLR (a 14.4% increase for each 1-unit increase in this ratio) but not with increasing TVLR. The optimal SVLRs from a revenue perspective were 9 and 8 hours of veterinary technician and nonmedical staff labor to every 1 hour of veterinarian labor. Respective values for productivity were higher, at ratios of 14:1 (not significant in the model) and 10:1, respectively. These findings suggested that a tradeoff in productivity could occur if a practice were staffed to maximize revenue and vice versa. Taken together, our results provided important evidence that nonveterinarian staff play an important role in both the revenue-generating and productive capabilities of veterinary practices.
Some of the purported benefits of veterinary technicians, particularly when those technicians are utilized optimally, include improved practice efficiency, client education, customer satisfaction, and patient care.11,12 We were therefore surprised to find no significant association between TVLR and the number of patients seen per FTE veterinarian per week, as many veterinary technicians—and certainly credentialed ones—are capable of history taking, surgical preparation and assistance, collecting biological samples and running diagnostic tests, educating clients, performing medical services such as dental scaling or imaging, and performing other tasks that can free veterinarians to see more patients in a given amount of time.13 Unfortunately, the survey was not designed to investigate whether these veterinary technicians were being used optimally, and further research in this area is recommended.
In the revenue model of the present study, a considerable and significant revenue advantage was identified for men- versus women-owned practices; this finding was not unexpected given the previously established greater incomes of male versus female veterinarians in the US14 and Australia15 and similar gender income disparities among people employed in science, technology, engineering, and math occupations.16 As far back as 1994 and 1995, female self-employed veterinarians, female veterinarians in partnerships, and female wage-earning or salaried veterinarians in the US earned, on average, 60%, 57%, and 85%, respectively, of what their male counterparts earned.14 In a more recent study of US veterinarians,17 female practice owners were found to earn significantly less than male practice owners, representing the largest source of intergender income inequality of all examined variables. The factors underlying these differences remain to be fully elucidated and would be important to understand to promote equity.
Revenue in the present study also increased significantly with increases in the natural logarithm of total hours worked by veterinarians per week and number of exam rooms. Both of these variables—considered practice inputs—could be presumed to translate into more patients seen overall and, hence, more revenue. Whether they would translate into improved profits as well was not determined and should be considered when interpreting our findings.
Data regarding the influence of veterinary services on revenue indicated that the service with the greatest influence (according to the percentage of revenue derived from the service) was imaging and the service with the lowest influence was surgery and anesthesia. This finding suggested that efforts to increase the percentage of revenue from imaging, particularly if performed and interpreted in-house, could yield substantial financial benefits for veterinary practices. Results also highlighted opportunities to improve revenue from surgery and anesthesia, such as by identifying and billing for missed opportunities18 (eg, premedications, surgical kits, anesthesia monitoring, diagnostic procedures, and IV fluids during the perianesthetic period).
Contrary to our expectations, neither practice owner age nor number of years the practice had been in business was associated with revenue. These findings suggested that experience is not an important factor in revenue generation among US private mixed and companion animal practices.
Turning to productivity, several variables in the productivity model, including gender of practice owner and number of years of ownership experience, number of years the practice had been in business, mean number of hours worked by veterinarians per week, number of exam rooms, and total weekly hours of operation, had no significant association with the number of patients seen per FTE veterinarian per week. These findings, supported by previous research,5,14 suggested that these characteristics of practices and their owners do not directly impact efficiency, although it is possible that some of these variables (eg, number of hours worked or number of exam rooms) could impact productivity with more efficient use. The observation that productivity increased with age of the practice owner, independent of years of practice ownership experience, was interesting and warrants further exploration.
Among significant variables in the productivity model, the northeastern US Census region was found to rank lowest in productivity, possibly because of the lower percentage of households owning pets in that region versus other regions.5 The reason that CAP, but not MAP, practices had significantly higher productivity than CAE practices remains unclear. The AVMA defines MAP as when at least 25% of patients seen are companion animals and 25% are food animal or equine; CAP as when the sum of cats, dogs, birds (nonpoultry), and exotic patients is ≥ 50% of all patients seen; and CAE as when the sum of these species is at least 90% of all patients seen.19 Therefore, if the observed difference was due to more large animal species seen, one would expect MAP practices to be more productive than CAE practices as well. It is already known that, on average, gross revenue is highest in CAE practices.5 Additional research is warranted to determine whether CAP, CAE, and MA practices differ in other ways that influence productivity.
An important limitation to the present study was our use of revenue as a practice output of interest. Although our findings indicated that increasing SVLRs and the number of hours worked by veterinarians can boost gross revenue, there is a physical limit to the number of people who can be hired or accommodated, after which additional fixed costs such as building expansion must be incurred. Whereas this report focused on revenue as a practice output, practice managers must also consider costs, including salaries and other inputs. Ideally, staffing decisions would maximize not just revenue but also profits.
Other limitations of the present study included its survey-based nature; responses to our research questions were limited by the questions that were asked. Some questions, such as practice type, had no provided examples or definitions and were, therefore, open to interpretation. Numeric responses such as estimated gross revenue from various sources were respondent estimates only. Because the respondents were unaware that this study would be conducted, we expected that any misclassification of responses would be nondifferential in nature and thus nonbiased toward a particular hypothesis. Indeed, we believe that the sample was sufficiently large and varied, with potential confounders analytically controlled, to provide robust findings generalizable to the broader target population: all US CAE, CAP, and MA practices.
In conclusion, understanding the drivers behind increasing veterinary practice revenue and improving veterinarian productivity are key to building a more successful and economically sustainable veterinary practice. The present study revealed the important role of nonveterinarian staff in increasing practice revenue and improving veterinarian productivity. Although the identified optimal SVLRs might be unachievable for small practices, lower ratios also yielded significant advantages. We believe that setting an optimal ratio of staff to veterinarians will ultimately improve productivity, lower production costs, increase technical efficiencies, improve service quality, and augment pet health-care delivery. Additional research is needed to investigate how veterinary practice staff, particularly veterinary technicians, could be utilized optimally to help practices and the patient care they deliver thrive in today’s hypercompetitive economic landscape. Research is also warranted into factors that influence the decision to own a veterinary practice, particularly among women.
Acknowledgments
The AVMA Practice Owner Survey was funded by the AVMA.
Drs. Ouedraogo, Lefebvre, and Salois are employees of the AVMA. The authors declare that there were no conflicts of interest.
Opinions expressed are not necessarily those of the AVMA.
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