Increased efficiency could lessen the need for more staff in companion animal practice

Frederic B. Ouedraogo Veterinary Economics Division, AVMA, Schaumburg, IL

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 PhD
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Peter Weinstein PAW Consulting, Irvine, CA

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 DVM, MBA
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Sandra L. Lefebvre Marketing and Communications Division, AVMA, Schaumburg, IL

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Abstract

OBJECTIVE

To evaluate technical efficiency of US companion animal practices.

SAMPLE

60 independently owned companion animal practices selected from the 2022 AVMA Veterinary Practice Owners Survey.

PROCEDURES

A ratio of the weighted sum of outputs to weighted sum of inputs was computed for each practice (ie, decision-making unit [DMU]). Inputs included labor (hours worked) and capital (fixed costs and number of exam rooms). Outputs (or production) included annual gross revenue, number of patients seen per year, and number of appointment slots per full-time–equivalent (FTE) veterinarian per year. Data envelopment analysis was used to optimize the ratio and estimate relative efficiency (RE) scores.

RESULTS

25 (42%) practices were classified as having high efficiency (RE = 1 or 100% efficient), 26 (43%) as having moderate efficiency (RE > 0.7 but < 1.0), and 9 (15%) as having low efficiency (RE ≤ 0.7). Mean RE scores for moderate- and low-efficiency practices were 0.83 and 0.66, meaning they could have reached their current production levels with 17% or 34% less resources. Per the model, if all 60 practices were 100% efficient on the RE scale, 22 fewer FTE veterinarians, 47 fewer FTE veterinary technicians and assistants, and 43 fewer FTE nonmedical staff would be needed overall.

CLINICAL RELEVANCE

These preliminary findings suggested that efforts to optimize efficiency could allow companion animal practices to meet demands for their services without necessarily needing to hire more staff. Such efforts might include engaging support staff to their full potential and implementing automated processes. Additional research is needed to identify routines or workflows that distinguish high-efficiency practices from others.

Abstract

OBJECTIVE

To evaluate technical efficiency of US companion animal practices.

SAMPLE

60 independently owned companion animal practices selected from the 2022 AVMA Veterinary Practice Owners Survey.

PROCEDURES

A ratio of the weighted sum of outputs to weighted sum of inputs was computed for each practice (ie, decision-making unit [DMU]). Inputs included labor (hours worked) and capital (fixed costs and number of exam rooms). Outputs (or production) included annual gross revenue, number of patients seen per year, and number of appointment slots per full-time–equivalent (FTE) veterinarian per year. Data envelopment analysis was used to optimize the ratio and estimate relative efficiency (RE) scores.

RESULTS

25 (42%) practices were classified as having high efficiency (RE = 1 or 100% efficient), 26 (43%) as having moderate efficiency (RE > 0.7 but < 1.0), and 9 (15%) as having low efficiency (RE ≤ 0.7). Mean RE scores for moderate- and low-efficiency practices were 0.83 and 0.66, meaning they could have reached their current production levels with 17% or 34% less resources. Per the model, if all 60 practices were 100% efficient on the RE scale, 22 fewer FTE veterinarians, 47 fewer FTE veterinary technicians and assistants, and 43 fewer FTE nonmedical staff would be needed overall.

CLINICAL RELEVANCE

These preliminary findings suggested that efforts to optimize efficiency could allow companion animal practices to meet demands for their services without necessarily needing to hire more staff. Such efforts might include engaging support staff to their full potential and implementing automated processes. Additional research is needed to identify routines or workflows that distinguish high-efficiency practices from others.

Introduction

Following the onset of the COVID-19 pandemic, the demand for veterinary services increased, as reflected by an increase in the number of appointments at companion animal practices in 2020 and 2021 versus 2019.1 Simultaneously, many practices were forced to operate with fewer team members and new workflows, while pet owners waited longer to secure appointments and to be seen for emergencies.1

On the employment front, hiring struggles are clear. The monthly number of veterinary job openings in the AVMA’s Veterinary Career Center approximately doubled between January 2021 and August 2022.2 Further, a survey of employers revealed that these job openings are taking longer to fill, with an average of only one-third (0.6 of 1.8) of veterinarian job openings filled per practice.2 These and other factors have helped fuel the perception that veterinary practices are experiencing a labor shortage and that one solution to the resulting “busyness” for existing staff would be to increase the supply of veterinarians and other veterinary professionals.

The tolls of this busyness on practice health are seen in results of recent surveys. Veterinarians worked a median of 5 more h/wk in 2020 through 2022 than they did in previous years.3 Burnout, as measured with the Professional Quality of Life scale, continues to increase among veterinarians, with the average burnout score in 2022 exceeding 30 (of 50) for the first time since measured for relief veterinarians, associate veterinarians, practice owners, and public practice veterinarians alike.3 Indeed, the cost of veterinarian burnout attributed to the US veterinary industry has been estimated to be between $1 and $2 billion annually.4 The percentage of veterinarians with serious psychological distress has also increased.5

A long-term solution to meeting an increased demand for veterinary services—and alleviating at least some of the observed tolls on practice health—would be to increase the supply of veterinarians. However, such a strategy would be effective only if the demand for veterinarians truly exceeds supply and only for as long as that situation continues. The potential long-term impacts must also be considered. For example, decisions to hire more staff can have long-term financial impacts on practices—impacts that might cause problems when markets change. In addition, more veterinarians could mean less job competition and thus lower or stunted salaries, as well as more graduates with educational debt as veterinary schools strive to increase class sizes to meet anticipated needs.

As we settle into the new, postpandemic “normal,” along with the rest of the world, it would be valuable to consider other ways of meeting demand that may provide more immediate—and more sustainable—gains, no matter how markets are behaving. One such strategy would be to improve practice efficiency. Efficiency essentially means doing things in the most economical way. Various types of efficiency are described in the economic and healthcare literature. We focus here on technical efficiency, which we consider synonymous with operational efficiency for discussion purposes. Technical efficiency captures how resources are used during the provision of services.6 In this regard, a highly efficient practice uses the least amount of resources (inputs) to achieve maximal production (outputs). An improvement in technical efficiency could therefore lessen the need for more veterinarians and other veterinary team members, allowing more patients to be seen by staff on hand each day and translating to higher revenues and profits and greater employee engagement and well-being.

Any strategy to improve technical efficiency presupposes that veterinary practices have room for growth in this area. Therefore, it becomes important to evaluate how practices are doing in this regard. Efficiency analysis can be used for this purpose and to provide evidence-based guidance on how efficiency might be improved. The purpose of the present study was to address these aims using data from US companion animal practices captured by the 2022 AVMA Veterinary Practice Owner Survey.

Materials and Methods

Data source

Data used for this study were obtained through the 2022 AVMA Veterinary Practice Owner Survey. This survey (Qualtrics XM; Qualtrics) was distributed to all veterinary practice owners in the AVMA membership database (n = 14,860) on June 13, 2022, and stayed open through July 13, 2022. A follow-up email was sent to survey recipients once a week for 4 weeks to encourage survey completion and submission. A total of 864 responses were received from practice owners, representing a 5.8% overall response rate.

These responses were then screened to include only independently owned companion animal practices (companion animal exclusive and companion animal predominant). Practices were subsequently excluded if owners failed to provide information on any question used to obtain the input and output variables used for modeling. The Grubbs test was used to screen remaining practices for outlying values that could disproportionately influence the outcome of the study, and those practices were excluded.

Variables

Data were extracted to capture 2 categories of variables to be used for modeling technical efficiency: inputs and outputs. Inputs represent all resources used to produce care services. For the study, these variables included labor (total number of hours worked per year) of 3 types of staff: veterinarians, veterinary technicians and assistants (ie, credentialed veterinary technicians [CVTs] and veterinary assistants, including noncredentialed technicians), and nonmedical staff (eg, kennel and reception staff, practice managers, and bookkeepers) as well as 2 variables representing capital. The first variable representing capital—fixed costs—captured total annual fixed costs for the practice, including employee salaries and bonuses, rent or mortgage payments, and all other expenditures insensitive to level of production, at least in the short term. The total values were then standardized to account for disparities in the cost of living across states using the US cost of living index.7 The second variable was the number of examination rooms in the practice. Although room space and layout can impact the efficiency with which patients are seen, as can the ability of staff to prepare each room for the next patient, these factors were assumed to be constant across practices to facilitate modeling.

Outputs represent possible outcomes resulting from the use of the inputs. These variables included gross annual revenue, total number of patients seen, and average number of appointment slots available per full-time–equivalent (FTE) veterinarian (working at least 37 h/wk)—all in 2021. Gross annual revenue was standardized using the same technique as for fixed costs. Data for total number of patients seen were derived from survey questions concerning the average number of patients an FTE veterinarian sees per week and the total number of FTE veterinarians in the practice. The average number of patients per week was multiplied by 50 (assuming a practice is open 52 weeks, but veterinarians may only work 50 weeks), to arrive at the total number of patients seen per FTE veterinarian per year. This value was then multiplied by the total number of FTE veterinarians to arrive at the total number of patients seen. For the average number of appointment slots, responses were used concerning the average number of appointment slots an FTE veterinarian had available each day of the week in 2021. These daily values were then used to determine a weekly average, and that number was multiplied by 50 to obtain a yearly average.

Statistical analysis

Background—

Two approaches are commonly used to estimate relative efficiency (RE) in healthcare industries: stochastic frontier analysis and data envelopment analysis (DEA).8 The former is a parametric model and requires specification of a functional form that reflects the relationship between the inputs and the output. In addition, and as is often the case with econometric models, stochastic frontier analysis handles only 1 response variable (output) at a time. DEA was designed to address these restrictions. With DEA, no a priori knowledge is needed of the data-generating process underlying the performance data under investigation.8 This means it is unnecessary to specify the functional form of the relationship between inputs and outputs. Second, DEA is effective and provides useful performance insights even in situations where data on the prices of inputs are unavailable or difficult to access.911 Third, DEA can appropriately handle multiple response variables (outputs) and multiple predictor variables (inputs) at a time.

DEA—

In the present study, DEA was used to measure the technical efficiency of included companion animal practices. In this model, the practices (or decision-making units [DMUs]) were assumed to use similar production technologies and produce similar goods and services.12

A general model to estimate efficiency, defined as the ratio of weighted sum of outputs to weighted sum of inputs (θp), can be expressed mathematically as follows12:

article image

such that θp ≤ 1, p = 1 through n, αr ≥ 0, and γi ≥ 0. In this equation, Qrp represents the rth output from the pth practice, Xip represents the ith input from the pth practice, and α and γ represent the weights placed on each input and output.

The main assumption for this analysis was that an increase in inputs would be met by a proportional increase in outputs (ie, constant returns to scale). Statistical software (R version 4.2.2; R Foundation for Statistical Computing) was used to determine the set of weights that maximized the ratio for each DMU and to identify high-performing DMUs. Each DMU was thus assigned an RE score between 0 and 1, whereby 1 indicated that the DMU had reached the highest level of output that could be achieved given the combination of inputs used. In the healthcare industry, these practices are considered to be exhibiting best practice in performance or benchmark studies. All other practices were assigned a score relative to this best practice level. On the basis of derived RE scores, practices were classified as having high efficiency (RE = 1 or 100% efficient), moderate efficiency (RE > 0.7 but < 1.0), or low efficiency (RE ≤ 0.7).

Descriptive statistics were also computed to summarize characteristics of the included practices, prior to DEA. Due to nonnormality of the data distribution for many values, these characteristics are reported as median, minimum, and maximum values.

Results

Descriptive statistics

In total, 280 independently owned companion animal practices were identified among the 864 respondents, representing 1.9% (280/14,860) of all survey invitees. After exclusion criteria were applied, 60 independently owner companion animal practices remained for inclusion in the study.

Descriptive statistics were tabulated for each of the evaluated input and output variables (Table 1). Median total labor for veterinarians was 3,600 h/y or 72 h/wk and for technicians (as defined) was 8,000 h/y or 160 h/wk. On an individual basis, this amounted to a median of 38 h/wk/FTE veterinarian and 36 h/wk/FTE technician.

Table 1

Descriptive statistics for selected inputs and outputs of US companion animal practices (n = 60).

Variable Median Minimum Maximum
Total labor (h/y)
 Veterinarians 3,600 1,450 14,350
 Veterinary technicians and assistants* 8,000 800 20,500
 Nonmedical staff 5,250 0 20,050
Fixed costs ($) 553,403 77,593 2,826,271
No. of exam rooms 3 1 7
Annual revenue ($) 1,054,950 109,287 4,387,000
Total patients seen in 2021 7,500 1,750 30,000
No. of appointment slots in 2021 3,950 1,750 9,750

*Represents both credentialed veterinary technicians and veterinary assistants (including noncredentialed technicians).

Median fixed costs were $553,403, and the median number of examination rooms was 3. As for outputs, practices generated a median of approximately $1 million in gross annual revenue in 2021 and saw a median of 7,500 patients/y or 150 patients/wk. In addition, practices had a median of 3,950 appointment slots available/FTE veterinarian/y or 79 slots/wk.

Data envelopment analysis

Practices were divided into 3 groups on the basis of their RE scores. Those with an RE score of 1 were classified as having high efficiency and represented 25 of the 60 (42%) companion animal practices. Practices with an RE score > 0.7 but < 1 were classified as having moderate efficiency and represented 26 (43%) of the sample. The remaining 9 (15%) with an RE score ≤ 0.7 were classified as having low efficiency.

The mean (SD) RE score of moderate-efficiency practices was 0.83 (0.08). This indicated that, on average, these practices theoretically could have reached their current production levels with 17% (1 – 0.83) less resources. Similarly, the mean RE score of low-efficiency practices was 0.66 (0.03), indicating that they could have reached their current production levels with 34% less resources. Per the model, if all 60 practices were 100% efficient on the RE scale, theoretically 22 fewer FTE veterinarians, 47 fewer FTE veterinary technicians and assistants, and 43 fewer FTE nonmedical staff would be needed overall. In terms of production instead, on an annual basis $237,495 more revenue could be produced, 53,937 more patients could be seen, and 44,350 more appointment slots could be filled if all 60 practices were 100% efficient.

Compared with moderate- and low-efficiency practices, high-efficiency practices consistently operated 9 h/d Monday through Friday, with veterinarians being available to see scheduled appointments 7 of those 9 business hours. This left 2 h/d for other staff to take care of matters not requiring a veterinarian’s supervision or expertise.

High-efficiency practices had more veterinarians on staff than did lower efficiency practices, on average. Specifically, high-efficiency practices had a mean of 3.2 FTE veterinarians, compared with 3.1 and 2.4 for moderate- and low-efficiency practices, respectively. For veterinary technicians and assistants, the pattern was less clear, with mean values of 5.7, 5.4, and 5.8 individuals, respectively. To further probe this finding, results for CVTs were separated from those for veterinary assistants. This separation showed that high-efficiency practices had more CVTs on staff than did moderate- or low-efficiency practices (means of 2.2, 1.9, and 2.1 individuals, respectively), although the difference among these categories was smaller than for veterinarians. On the other hand, high- and moderate-efficiency practices had fewer veterinary assistants, on average, than low-efficiency practices, with means of 3.5, 3.5, and 3.7 individuals, respectively.

High-efficiency practices also differed from their peers in the number of hours certain staff worked, with the direction of the difference—when present—varying by staff type. For instance, in high-efficiency practices, associate veterinarians (both full- and part-time) worked more hours than those in moderate- or low-efficiency practices, with values of 34.3, 30.1, and 21.5 h/wk, respectively. The pattern was similar for veterinary technicians and assistants as a group (34.9, 34.4, and 33.6 h/wk, respectively) but not for CVTs (37.2, 35.5, and 36.9 h/wk) or veterinary assistants (31.4, 32.6, and 29.9 h/wk) separately. On the other hand, owners of high-efficiency practices worked less hours than their peers, with values of 43.3, 47.0, and 50.9 h/wk, respectively.

When it came to practice management, owners (who were also veterinarians) in high-efficiency practices spent only 14.0% of their time on related activities, with the remaining 86.0% of their time seeing patients and clients, on average. In comparison, owners in moderate- and low-efficiency practices spent a mean of 25.6% and 18.3% on management tasks, respectively.

In terms of the number of available appointment slots per FTE veterinarian per day, high-efficiency practices had up to 24% more slots than moderate-efficiency practices and up to 38% more than low-efficiency practices, depending on the day of the week. The mean number of patients seen per FTE veterinarian per day was 21, 16, and 12 for high-, moderate-, and low-efficiency practices.

Discussion

Understanding the sources and types of inefficiencies in veterinary practices is important for guiding efforts to optimize operations and allocate resources. Optimal use of resources could help enhance quality of services, improve client satisfaction, boost practice profitability, and increase employee engagement and retention.

In the present study, over half (58% [35/60]) of included companion animal practices were found to be less efficient than their peers, suggesting opportunities for improvement. Three general strategies can be used to optimize efficiency: reduce resource levels (inputs) while maintaining production (outputs), increase production while maintaining resources as is, or reduce resource levels and increase production concurrently. Given the aforementioned busyness of veterinary staff, reducing available resources is likely an untenable solution to improving efficiency unless those resources are unnecessary or drain on efficiency. However, our data suggested that it would be possible to optimize efficiency by boosting productivity with existing staff. Indeed, if the DEA model results were extrapolated to 10,000 practices and productivity increased to achieve high efficiency in all as measured here, this could theoretically obviate the need for 3,633 FTE veterinarians, 7,838 FTE veterinary technicians and assistants, and 7,133 FTE nonmedical staff. Alternatively, with existing staff, those 10,000 practices could see a total of almost 9 million more patients/y. The potential impact of improved efficiency is backed by other research,13 in which a “practice productivity index” was estimated. According to that study, a companion animal practice could save up to 2,000 h/y (the equivalent of adding 1 FTE veterinarian), depending on its index value, by improving capacity.

Interestingly, high-efficiency practices in the present study employed more FTE veterinarians than other practices, which countered our expectation that the more efficient practices would be ones that used less resources, including less staff. However, this observation makes sense if one considers that those veterinarians also had more appointment slots available to see patients and saw more patients per day. For practices with staffing challenges or limited space, the question then becomes how to increase available appointment slots with the veterinarians and physical space (exam rooms) the practice already has. Underused areas represent expenses and can tax efficiency when not generating revenue. One answer would be to make maximal use of both available exam rooms and time through high-density scheduling.14 For example, a veterinarian and well-trained aide could cover 2 rooms together simultaneously, with the CVT performing the initial patient screen in one room, then moving on to the next room when the veterinarian comes in and takes over. Building on this, at the same time another veterinarian could handle surgeries and a third could manage hospitalized patients, treatment area procedures such as dental scaling, walk-ins, or emergencies. This example also speaks to the importance of assessing workflows, processes, and the practice’s physical layout for bottlenecks, dis- or misuse, and other detractors from efficiency. Among other potential outcomes, such an assessment could even lead to downsizing of physical space or services offered to promote maximal use of space and staff.

Another option for increasing production, without changing available resources, would be to use the best person for the task, based on their qualifications, competencies, and comfort level and as allowed per relevant state veterinary practice acts. Logic suggests that when a veterinarian performs tasks another team member could do, they see fewer patients, limiting their production and impacting efficiency. This appears to happen commonly, as 40% of veterinary technicians report not feeling fully utilized.15 And yet, in a previous study,16 higher staff-to-veterinarian labor ratios were found to be associated with higher gross revenue and productivity.16 Optimal engagement of staff could also improve job satisfaction and morale and reduce turnover15,17,18—all things that also can further tax efficiency and the practice’s bottom line.

A third option to boost production would be to adopt and fully utilize technologies designed to decrease the onus on people and make routine tasks or processes more efficient. In a 2022 survey3 of veterinary practice owners, only 58% of practices were reported to use electronic medical record software or practice information management system (PIMS), 45% communications software that integrates with PIMS, 33% online appointment scheduling systems, 34% digital inventory management systems, and 21% telehealth services. Among respondents in another study,13 78% reported not using PIMS to its fullest. The veterinarian owners of high-efficiency practices in the present study spent less time than those in less efficient practices on practice management–related activities, freeing the rest of their time for seeing patients and clients. Whether those owners embraced technology remains unknown but would be interesting to explore in future studies.

Finally, leadership can also influence efficiency. In a 2022 survey of veterinary technicians,15 22% chose “under-managed veterinary practice” as one of the most challenging aspects of their job. Leadership efforts that support efficiency include developing a mission statement for the practice and making efficiency a strategic pillar, hiring a practice manager or training an interested teammate to fill the role, clearly defining staff roles and tying them into the mission, promoting teamwork and culture of respect and support, and providing or encouraging training or continuing education opportunities for interested staff. It is also useful to measure productivity before and after any efficiency-related changes are made to see if those changes are working.

The present study had some limitations that need to be considered when interpreting the results. These include the small sample size and limitations inherent to DEA. The DEA approach allowed estimation of the efficiency of companion animal practices relative to those displaying best practice but not the absolute efficiency of each. High-efficiency practices were assumed to be 100% efficient, yet in reality even high-efficiency practices have room for improvement. Further, the fact that 42% of included practices were classified as having high efficiency suggested the developed DEA model may have lacked some discriminatory power.

Another limitation was that participating practices differed somewhat from the sample from which they were drawn,3 including more veterinarians and CVTs on staff, suggesting a certain amount of selection bias. Consequently, the generalizability of the DEA results to the general US companion animal practice population is unclear. However, it should be noted that these practices were comparable to the sample in other aspects (eg, number of examination rooms, gross annual revenue, and hours of operation),3 so some of the general patterns observed may extend to other practices. Additional research into routines or workflows associated with high or low efficiency could yield more actionable insights.

In conclusion, the present study revealed opportunities to improve efficiency in companion animal practices and the potential benefits of doing so. These findings could be considered hopeful, as they highlight a possible solution to current staffing challenges through improving productivity with available staff, rather than through hiring and onboarding new staff. Employers may do well to ride out the short-term pain of an uncertain labor supply and take the opportunity now to ensure their practice is running at optimal efficiency before making financial decisions that could have long-term impacts. Improved efficiency could put the practice in a better position and ease some of the frustrations arising from the challenge of finding new team members, without compromising the quality of, access to, or cost of care. Solutions could be as simple as engaging existing staff to their full potential, identifying and addressing workflow issues, leveraging technology, and providing strong leadership.

Acknowledgments

The Veterinary Practice Owners Survey was funded by the AVMA.

Drs. Ouedraogo and Lefebvre are AVMA employees. The authors declare that there were no conflicts of interest.

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