ADA 2014 Practice Trends Survey

Author: Amyn M. Amlani, Ph.D., University of North Texas, Denton, Texas
Joel Lahrman, MBA, Clockwork Solutions, Austin, Texas
Jason Aird, Au.D., Iowa Audiology & Hearing Aid Centers, Coralville, Iowa
Barry A. Freeman, Ph.D., ZPower, Camarillo, California
Brian Taylor, Au.D., Hypersound Hearing Solutions, Turtle Beach Corporation, San Diego, CA
Jerry Yanz, Ph.D., Hearing Industry Consultant, Minneapolis, Minnesota

Audiology is being commoditized. Disruptive innovations—such as online hearing tests, Internet-available personal sound amplification products (PSAPs), and Big-Box retailers—have influenced the delivery of audiological services and technology. To understand the influence of these factors on the work settings of ADA members, an Innovation Working Group was formed shortly after the 2013 ADA annual conference. A Web-based survey was distributed to ADA members between September 2014 and November 2014. Respondents were asked to provide information about their practice as it related to various internal (e.g., patient demographics, services, technologies) and external (i.e., competition, marketing, referrals) factors. These factors were then compared against categories of Total Gross Revenue, the study’s dependent variable, provided by the respondent. Key findings from this undertaking are provided in this article.
Survey Demographics
Three hundred sixteen respondents completed the survey, with only 187 respondents providing a response for Total Gross Revenue. Thus, we discarded 129 responses from further statistical evaluation. Table 1 displays the categories of Total Gross Revenue, along with the percentage of responses for the 187 respondents. Because of the reduced number of usable responses, the findings reported are limited to private practices that are independently owned. The dataset was analyzed by Clockwork, a third-party data analysis firm based in Austin, Texas.

Table 1. Categories of Total Gross Revenue and percentage of responses per category.
Total Gross Revenue Percentage of Respondents
Less than $100,000 5%
Between $100,001 and $250,000 7%
Between $250,001 and $500,000 21%
Between $500,001 and $750,000 14%
Between $750,001 and $1 million 14%
Between $1.001 million and $2 million 22%
Greater than $2 million 15%
Statistical Approaches
Our primary objective was to determine which internal and external factors influenced Total Gross Revenue. We determined these predictor variables using two types of statistical approaches: Chi-squared tests and Spearman rank correlations.

Chi-squared tests
Chi-squared is a statistical test commonly used to compare differences between observed (i.e., respondent-provided) data with data one would expect to find based on a hypothesis. As an example, 15% of respondents reported that their practices grossed greater than $2 million in revenue. As such, the Chi-square test expects 15% of all sub-categories of internal (e.g., services provided, technology provided, patient demographics) and external (e.g., competition, marketing) data to be associated with practices that gross greater than $2 million in revenue. Further, the Chi-square test is restricted to responses provided for nominal (i.e., yes/no, male/female) and ordinal (i.e., scale having ordered structure but unknown intervals between categories [Likely, Neutral, Not Likely]). Statistical significance was applied at a minimum alpha value of 0.1, meaning that there is a 90% chance that observed differences are true, and not due to chance.

Spearman rank correlations
Spearman rank correlation (rs) is a statistical measure of the strength of a monotonic relationship between paired data responses to ratio (i.e., continuous) data (e.g., cost of goods sold, time spent working), as shown in Figure 1. A Spearman rank correlation is expressed as a number between -1 and +1. A Spearman rank correlation having a positive value close to +1 indicates that as the variable (e.g., time spent working) increases, Total Gross Revenue is also increasing (see top panel of Figure 1). Conversely, a Spearman rank correlation having a negative value close to -1 indicates that as the variable (e.g., time spent working) decreased, Total Gross Revenue is also decreasing (see middle panel of Figure 1). Spearman rank correlations displaying a value close to 0 indicates no relationship between the variable (e.g., time spent working) and Total Gross Revenue (see bottom panel of Figure 1).

Figure 1. Spearman rank correlation patterns.

A Spearman rank correlation with an absolute value of .6 or greater is considered to demonstrate a strong association (i.e., relationship) between data responses. A Spearman rank correlation with an absolute value between .3 and .59 is considered to exhibit a moderate association between data responses. A Spearman rank correlation with an absolute value of .29 or less is considered to highlight a weak association between data responses.

A total of 162 Chi-Squares and 75 Spearman rank correlations were calculated against Total Gross Revenue. Thirty-six Chi-square outcomes yielded statistically significant outcomes at a minimum significance level of 0.1. In addition, 61 Spearman rank correlations showed an association towards Total Gross Revenue. We provide the salient outcomes from the analysis using the outline shown in Figure 2.
Figure 2. Categories of results from the ADA 2014 survey.

Internal Factors
In this section, results related to the core facets of the practice are provided.

Practice Management – With respect to the owner of the practice, the Chi-square procedure yielded statistical differences between respondent-provided data and expected data in the: (a) level of education in business/finance (p < .05), (b) annually established financial goals (p < .001), (c) office management systems (p < .05), (d) weekly/bi-weekly staff meetings (p < .001), and (e) affiliation with a third-party (i.e., manufacturer, network, buying group) (p <.1). Results also revealed statistical significant findings for staff members when they were offered: (a) paid vacation (p < .01), (b) retirement plan (p < .01), (c) health insurance (p < .01), and (d) profit sharing (p < .01). For both the owner and staff member, a secondary analysis revealed that these trends were seen primarily in practices that grossed $1 million or more in total revenue.

Spearman rank correlations further indicated that Total Gross Revenue is strongly associated with the number of hearing aids dispensed annually per location (rs = .56), which increases in association with multiple locations (rs = .76). On the other side, practices that reduce their gross expenses (rs = .69) and cost of goods sold (rs = .43) also increased their Total Gross Revenue. Interestingly, Total Gross Revenue had the strongest association when 2-3 audiologists were employed in a single location (rs = .68), which decreased in association with a single audiologist per location (rs = .22) and when three or more audiologists were employed at a single location (rs = .33). Employee training was also found to be a primary contributor to Total Gross Revenue (rs = .62).

Patient Demographics – A Chi-square analysis revealed statistical differences between respondent-provided and expected data as a function of the patient’s age. Specifically, significant differences were found for children 11 to 17 years (p < .01), and for adults 18 to 40 years (p < .1), 41 to 60 years (p < .05), and 61 to 80 years (p < .05). In addition, results revealed a significant association between Total Gross Revenue and serving returning patients, not new patients (p < .01). A follow-up analysis indicated that these trends occurred in practices grossing greater than $1 million annually.

Services Provided – Practices that grossed $1 million or more were also found to provide their patients with a statistically significantly wider range of services compared to practices having less Total Gross Revenue. Practitioner-provided services included: (a) billing new patients for audiologic assessment (p < .05), (b) central auditory processing assessment for new (p < .05) and returning (p < .05) patients, (c) tinnitus evaluations for new patients (p < .1), and (d) vestibular testing for returning patients (p < .1). With respect to hearing aid service delivery, practitioner-provided services that were significant included: (a) counseling fee for returning patients (p < .05), (b) fitting fee (p < .1), and (c) fee for 6-month follow-up appointment (p = .1). Non-audiology staff services that contributed to Total Gross Revenue included the provision of standard audiological assessment (p < .05).

Technology Provided – As indicated earlier, Total Gross Revenue was highly correlated with the number of hearing aids dispensed annually, but varied depending on whether the practice had a single location (rs = .56) or multiple locations (rs = .76). Additional analyses also revealed that binaural fittings (rs = .76) and fewer hearing aid returns (rs = .57) contributed to increased Total Gross Revenue. As expected, results revealed a negative Spearman rank correlation (rs = -.36) for practices that were brand-specific (i.e., offering only one or two manufacturers' products).

With respect to whom the technology was provided, Spearman rank correlations yielded a strong association for adult patients previously seen in a practice (rs = .64). Moderate associations were revealed for previous adult patients seen in a competitor’s practice (rs = .52) and new patients seeking amplification technology for the first time (rs = .39). For children, the associations for previously seen in a practice (rs = .29), previously seen in a competitor’s practice (rs = .29), and new users of amplification technology (rs = .29) were all weakly associated.

For services provided for amplification technology, use of probe-microphone measurements in adult patients was strongly correlated with Total Gross Revenue (rs = .64), while use of soundfield testing measures weakened this association (rs = .28). For children, probe-microphone measurements were weakly correlated with Total Gross revenue (rs = .29), and had essentially no bearing when soundfield testing measurements were employed (rs = .05).

External Factors
In this section, results peripherally related to the practice are reported.

Practice Value – The patient-provider relationship begins with the patient assessing the practice’s value (Amlani, 2013b), often with the first step being an Internet search. Chi-square results revealed a statistically significant difference between respondent-provided data and expected data as it related to Internet presence (p < .1). For practices with an Internet presence, three variables were found to be statistically critical to increasing the practice’s value: (a) information about hearing loss and treatment (p < .01), (b) information about audiologic disorders and treatment (p < .01), and (c) information about the practice (e.g., payment options, hours, location) (p < .05). A trend analysis revealed that practices that generated less than $1 million annually in Total Gross Revenue had essentially no Internet presence, or lacked the information to increase the practice’s value.

Competition – Responses were also obtained to determine the degree to which competition within each respondent’s market disrupted their Total Gross Revenue. While the obvious answer is expected to be the Big-Box retailer, results for this practice category were not significantly significant (p > .05). Instead, Chi-square results found three disruptive competitors: (a) an ENT practice within 10 miles (p < .05), (b) a Veteran’s Administration Hospital within 10 miles (p < .1), and (c) the Internet (p < .1). A secondary analysis revealed that practices that generated less than $0.75 million annually in Total Gross Revenue were most affected by these competitors.

Marketing Strategy – Respondents were also asked to provide data as it related to various aspects of their marketing strategy. Spearman rank correlations revealed that Total Gross Income was moderately associated with Web/social media (rs = .58), newspaper ads (rs = .50), and direct mail (rs = .47). Factors such as television ads (rs = .12) and radio spots (rs = .04) had essentially no bearing on improving Total Gross Revenue.
The primary goal of this survey was to understand how today’s disruptive innovations are influencing the audiologic landscape of ADA members. To remain competitive within the market and increase revenue stream, results from this study shed light on how practices can enhance their Total Gross Revenue. Below is a summary of the findings:

For practice owners, a business education is necessary, along with established annual financial goals and regularly scheduled meetings with staff members. This finding also supports the need for university AuD training programs to teach future practitioners business-oriented topics as part of their mandatory curriculum.

Total gross revenue was strongly correlated with multiple locations, as long as these multiple locations employed no less than 2 audiologists and no more than 3 audiologists. While hearing aid dispensing is a primary revenue source for most practices, the inclusion of tinnitus evaluations, central auditory processing assessment, and vestibular services helped differentiate practices from their competition, while positively augmenting the practice’s revenue stream.

With respect to hearing aid dispensing, offering more than two manufacturer brands enhanced Total Gross Revenue. In addition, practices that provided probe-microphone measurements—and not soundfield measurements—and billed for audiological evaluations, hearing aid fittings, counseling, and follow-up visits yielded higher Total Gross Revenues.

Increasing patient demographics beyond older adults differentiated practices within the market. Survey findings revealed that age populations 11 to 17 years, 18 to 40 years, and 41 to 60 years improved Total Gross Revenue, and provided opportunities to grow a long-term patient-provider relationship (Amlani, 2013b).

Correlations for growing one’s practice were strongest with patient’s previously seen in the practice, followed by new patients seen previously at a competitor’s practice, and lastly by first-time patients. This finding is a common thread in the industry (Amlani, 2013a), and one that requires additional investigation to understand the causes for this behavior (Amlani, in press).

Having a Web presence is essential. However, this Web-presence also required that practices provide patient-sought information, such as information about hearing loss and treatment information about audiologic disorders and treatment, and information about the practice (e.g., payment options, hours, location). Lack of this data increased the likelihood that the patient seeks another practitioner to provide services (Amlani, 2013b).

The primary competition for a practice was not the Big-Box Retailer, but the local ENT, the local VA Hospital, and the Internet. Strategies to compete in the marketplace, such as those suggested in this article, are opportunities to increased success. A practice’s marketing strategies were highly correlated with Web/social media, newspaper ads, and direct mail. Within these avenues, messages highlighting hearing health, not price, will increase the reputation of the practice (Amlani, 2013b).
Amlani AM. (in press). Application of the Consumer Decision-Making Model in assessing hearing aid adoption intent in first-time users. Seminars in Hearing.

Amlani AM. (2013a). Influence of perceived value on hearing aid adoption and re-adoption intent. Hearing Review Products, 20(3), 8-12.

Amlani AM. (2013b, Winter), Growth in hearing aid adoption depends on the patient-provider relationship. Invited paper, Audiology Practices.