20Q: Consumer Reviews Offer Hearing Care Insights

From the Desk of Gus Mueller

Okay, be honest. When was the last time you read an audiology article that talked about consumer health infomatics, linguistic inquiry word count, natural language processing and automatic topic modeling? I thought so. But I bet you’re considerably more familiar with these terms:  Facebook, Twitter, Reddit and YouTube. One component of consumer health informatics is to model and integrate consumers' preferences into clinical practice and information systems. And where do we often find consumers’ preferences? You're right, on social media sites.

What our patients say about our services, and even the exact words they use to describe them, can be used to gain helpful insights. These comments can be analyzed and categorized. The notion is that the review of these data will then lead to better clinical decisions and services. In the world of hearing aid fitting, for example, based on a large number of online reviews, we might be able to develop profiles for patients who are best suited for various form factors, features, assistive technology, or even different processing strategies. We might see what best drives hearing aid benefit and satisfaction, and for whom? This could take us a step beyond what we learn from other sources such as the MarkeTrak surveys.

So, are there really audiologists looking into all this? We know of at least one, and he is our guest author this month. Vinaya Manchaiah, AuD, MBA, PhD, is a Jo Mayo Endowed Professor of Speech and Hearing Sciences at the Department of Speech and Hearing Sciences at Lamar University, Beaumont, TX. His appointment at Lamar centers predominantly on research that focuses on improving the accessibility, affordability, and outcomes of hearing loss and tinnitus by promoting self-management and use of digital technologies. His research has been funded by various organizations including the National Institutes of Health.

You probably are familiar with Dr. Manchaiah’s name from his many publications, including the five textbooks he has edited or authored. He has received numerous awards including the Bharat Samman Award from the NRI Institute in India and the Erskine fellowship from the University of Canterbury in New Zealand. He also was named a Jerger Future Leader of Audiology by the American Academy of Audiology.

I think you’ll enjoy learning about this new audiology research area in Vinay’s 20Q. He also has an interesting story to tell regarding his professional journey, which we’ve included at the end of the article.

Gus Mueller, PhD
Contributing Editor

Browse the complete collection of 20Q with Gus Mueller CEU articles at www.audiologyonline.com/20Q

20Q: Consumer Reviews Offer Hearing Care Insights

Learning Outcomes 

After reading this article, professionals will be able to:

• Discuss how online reviews of hearing aids and hearing care services may benefit consumers and professionals.
• Explain how consumers' experiences as described in online reviews relate to satisfaction ratings.
• Discuss three main methods for analyzing online consumer reviews.

  Vinay Manchaiah 

1. Your title intrigues me. We really can learn something that is clinically useful from consumer reviews?

Most certainly, and it’s something I’ve been researching. The main goal of my research is to work towards improving accessibility, affordability, and outcomes of hearing care services by promoting self-management and the use of digital technologies, such as the Internet, smartphones, wearables, etc. Within this broader aim, I have a few different but complementary areas of research. We are focusing on developing and evaluating Internet-based rehabilitation (also referred to as telerehabilitation or digital therapeutics) for hearing loss and tinnitus. In addition, we are conducting some studies for developing an evidence-base for direct-to-consumer service delivery models, and also a relatively new line of research on consumer health informatics (CHI).

Our initial efforts within the CHI line of research were to examine Internet-health data including information on websites and social media. Our goal was to understand health communities as well as patient-generated data and its uses within and outside healthcare systems. We’ve also examined its relationship to promoting self-assessment and self-management of hearing-related disorders. More recently, however, we are doing a series of studies looking at online consumer reviews about “hearing aids” and “hearing healthcare services.”

2. What prompted you to study this particular area?

My interest in this area started several years ago when I collaborated with some sociologists and a science educator with expertise in automated text analysis to understand the social representation of hearing loss and hearing aids. This led to several studies and other collaborations, as well as overall interest in online health information. At first, I wasn't sure where this research would lead, but I have since become convinced that online health information is a timely and futuristic area of research and extremely important for hearing healthcare. 

I have summarized part of my journey in a footnote at the end of this article.

3. Why should practicing audiologists care about online reviews?

I think that it is extremely important for audiologists to consider online health information including online reviews, and it is important for the profession as well. There are many compelling reasons for this. First, consumer surveys suggest that more than 80% of people trust online reviews as much as personal recommendations and often they use this information when making decisions about products and services. It is not that they don’t trust healthcare professionals or are hesitant to consult us. Online reviews are much easier to access. Using online reviews when buying products and services for just about anything has become commonplace for most of us today. We need to know what our patients are saying (or reading) about the products and services we provide. Second, online reviews may serve as an early indication of the quality of our products and services as well as how consumers relate to them. There is some research showing that the consumer experience shared as online reviews may relate to health outcomes (e.g., Glover et al., 2015). So, if you want to get insight into possible outcomes as a result of the products and services provided, then online reviews will give you an early glimpse. Lastly, but more importantly, the trend of online reviews about healthcare products and services is in its early stages and is expected to rise exponentially in the coming years. We need to start learning now how to make sense of online reviews to improve on what we do and to leverage them to our advantage. If we don't, we may miss the opportunity and we risk being defined by them. 

I think we do have to be cautious and open-minded when we consider these unsolicited reviews online. It is human nature to react defensively to public criticism (if the reviews are bad). Alternately, if we take the approach that listening to the patients' (or consumers) views and perceptions is an opportunity to develop what we do, and how we do things, these reviews are likely to benefit both patients, ourselves, and our profession.

4. Are online reviews a reliable source of information?

This is an important question to consider. There is a lot of concern in the online world about “fake reviews,” which are created by some freelance marketers trying to create a specific image (either positive or negative) about products or services. There are even fake-review generator tools that can leave a fake review just like the freelancers. Despite this, it still is important to pay attention to online reviews. There are ways in which we can overcome this problem in research by identifying fake reviews using some tools (e.g., www.fakespot.com). The answer to this problem also lies in large numbers. For example, let’s say we are choosing a restaurant in Yelp.com based on online reviews. If you have a review about a restaurant with a 5-star rating with only 5 reviews, you will have less faith in the validity of the review. On the other hand, if the rating is 4 stars with over 1,000 reviews then you are pretty confident that the restaurant you are choosing is of good quality. In the same way, when we have large data, reviews that are fake are likely to have much less impact on the overall results.

For a typical audiologist, it is still important to pay attention to what patients are saying about their services online. For example, audiologists can closely monitor reviews about them or their practice using Google or websites such as www.hearingtracker.com. If they have a positive review, it’s like a pat on the back. It can reinforce what is working in terms of patient satisfaction. If they have a negative review, it’s important to consider the reliability. The best way to handle a negative review is to invite the reviewer to have a dialogue so that they can learn more. A lot of businesses are doing this to ensure that they are responsive to consumer needs. A recent Harvard Business Review article provides some helpful guidelines for responding to reviews (Manis, Wang, & Chaudhry, 2020).

5. What are the advantages and disadvantages of looking at online consumer reviews in audiology?

The main advantage is that we get patients’ views in their own words. As most online reviews are unsolicited, they are probably more ecologically valid data than clinical interviews or questionnaires. This is also a free source of market research. In other words, instead of conducting detailed interviews about our services to selected patients, we can obtain the views of a large number of consumers by means of online reviews.

I don’t see any major disadvantages of looking at online reviews, although there may be some challenges. First, as I mentioned before, is the possibility of some fake reviews. Second, some audiologists may not know how to respond to a bad review. Third, larger practices may not have any good methods to quickly and reliably analyze hundreds or even thousands of reviews. I believe, however, that we can easily overcome these challenges.

6. Is there a quick and easy method of monitoring online reviews?

Broadly, there are two approaches to analyzing and making sense of text data in online reviews. We can use traditional qualitative methods (e.g., content analysis, thematic analysis) to read each review, code them or classify them into meaningful units, and then identify the key themes within the reviews. This method, however, is tedious and time-consuming. It may be practical for smaller practices, but not for larger practices, buying groups, and the profession as a whole to monitor what consumers are saying about products, services, and audiology care. There are many different automated text analysis software available that can quickly and reliably analyze the text responses. These are becoming increasingly used in analyzing Internet health information including online reviews.

There are several methods within automated analysis. The most common approach is to perform what is called “topic modeling,” using statistical techniques such as cluster analysis. Cluster analysis helps to identify key themes just like a traditional qualitative analysis does. Another common approach is to look at linguistic dimensions (e.g., Linguistic Inquiry Word Count or LIWC) which can help to identify aspects such as positive or negative emotions, analytic thinking, social processes or cognitive processes just by looking at the text data. Using automated methods to analyze text data is referred to as Natural Language Processing (NLP), which can be applied to almost any text data. What you write online (e.g., email, online reviews, social media posts) may already be analyzed using these techniques.

The automated methods help analyze large amounts of data very quickly and result in a broad understanding, like a bird’s eye view. On the other hand, qualitative methods provide a more in-depth understanding, although we may not be able to apply them to large data sets. In our research, we use both methods as they complement each other.

7. Why can’t we just use standardized questionnaires as a way to gather consumers' opinions and experiences?

Good question. In the field of psychology, the Likert scale was developed as a way to measure attitude. Although this was not the best method, it became very popular in all areas including psychology, healthcare, marketing, etc., as using this method provided an easy way to gather and analyze data. You’re probably familiar with the many MarkeTrak surveys. However, the standardized questionnaires we typically use have some issues with the rating scales. First, not all questions within the standardized questions may be of interest (or even relevant) to all those who are completing the questionnaire. Second, when measuring things like satisfaction, the unidimensional Likert scale can be problematic as elements of satisfaction and dissatisfaction can coexist about something, With a Likert scale, users are asked to choose degrees of being either satisfied or dissatisfied. This may result in more neutral responses. Using open-ended questions has been proposed as a way to overcome this limitation. In the past, it was not easy to analyze responses (text data) to open-ended questions. Using modern NLP techniques, however, we can understand and predict how people think, feel, and behave just by examining how they speak and write. In other words, the use of language seems to say a lot about people as demonstrated from recent research in psychology and social psychology. So, these methods are likely to have big implications on medical research methodology. If we apply them to consumer reviews of hearing care, the information we get could have a big impact on our clinical practices.

8. What exactly is natural language processing (NLP)?

Natural language processing is a branch of linguistics, artificial intelligence, and computer science that deals with the interaction between computers and our use of natural language. The main application of NLP is to process and understand large amounts of textual data. The study of natural language has existed for more than 50 years, although it has grown exponentially during the last decade, with the rise of faster computing and the use of the Internet to communicate. You are probably already using NLP.  For instance, if you are using smartphone applications such as Apple’s Siri or Amazon Alexa, then you are using NLP in your daily life. These applications convert the speech to text, process them to make sense of what you are saying, and then act on the instructions you provide. Various artificial intelligence techniques including machine learning are used during NLP. This means, the NLP is not just to analyze the past information, but it can be used in predictive analysis as well. In addition, there are modern NLP techniques in which researchers can look for less frequent hidden messages rather than looking for key themes. So, the future applications of NLP techniques in healthcare would be very interesting and potentially useful to improve the outcomes of our patients.

9. Can someone without a lot of technical expertise use these automated techniques to analyze textual information?

Fortunately for us, the answer is yes. The technical details of how it is done may not be needed for most people. For example, we use various electrophysiological techniques although we have limited knowledge of electronics, as our focus is on the application and not on the technology itself. In the same way, we have many open-source (e.g., https://www.iramuteq.org/) as well as commercial (e.g., https://info.leximancer.com/) text-analysis software that can be used to analyze large amounts of textual data. Some recent ones provide user assistance including YouTube videos with tutorials on how to use the software.

10. What can researchers learn using these automated techniques to analyze consumer reviews? 

The type of software or analysis we choose depends on what we are trying to learn. For example, there is specific software for topic-modeling which can help us examine the main themes within consumer reviews. Alternatively, if we are interested in sentiments or language dimensions, then there is specific software that can help understand these. There are some good review articles and book chapters for those interested in learning more about these methods (e.g., Boyd, 2017; Demner-Fushman et al., 2009 ). In addition, we can use traditional statistical methods to examine the main themes or language dimensions in relation to some meta-data (e.g., age, gender, user experience ratings).

I mentioned NLP earlier.  Looking at the natural language in consumer reviews can provide an understanding of the reviewer’s thoughts, emotions and behaviors. Research has shown that there is a good correlation between what we can learn from open-text and some standardized measures. In addition, some research suggests that we can even predict some things about a reviewer (such as personality) just by analyzing their use of language. I think these automated techniques can be used to examine answers to open-ended questions (for example, on surveys and questionnaires), as well as to analyze large numbers of online consumer reviews.

These methods have many advantages as they are quick, easy, and less expensive, but also some limitations (e.g., they provide general information rather than in-depth understanding). However, if we understand their purpose and how we are using them, I believe the advantages outweigh the limitations.

11. It seems like this area of research could be applied to user benefit and satisfaction with hearing aids?

You are correct. This is one of the ways in which I see the profession as a whole can benefit from looking at online consumer reviews. 

In a recent study currently under review, we examined 1,378 consumer reviews on hearing aids extracted from the HearingTracker.com website. These reviews were unsolicited and were left by self-selected hearing aid owners in the general public. The data included text responses to the open-ended question, “How are things going with your hearing aid(s)?” In addition, the users also completed a 10-item structured questionnaire about benefit and satisfaction that used a 5-point response scale. The responses to the structured questionnaire were averaged to get a global performance/benefit rating. We also were able to extract some meta-data (e.g., hearing aid brand, technology level). 

The open-text data were analyzed using three different methodological approaches to text analysis: 1.) Automated Topic Modeling to identify the main themes in the reviews, 2.) Qualitative Content Analysis (a traditional researcher-led qualitative content analysis, where a researcher reads every comment and attempts to categorize/analyze them to identify main themes), and 3.) automated Linguistic Inquiry Word Count (LIWC) to identify linguistic aspects of social, emotional, health and personal dimensions.

As we would expect, consumer reviews on hearing aids were both positive and negative in nature, although the vast majority of them were positive (i.e., mean rating of 4.04 on a 5-point scale). 

The Automatic Topic Modeling identified six clusters within two domains. 

Domain One (Device Acquisition) included three clusters: 

• finding the right provider, device, and price-point
• selecting a hearing aid to suit the hearing loss 
• attaining physical fit and device management skills

Domain Two (Device Use) included three clusters: 

• smartphone streaming to hearing aids
• hearing aid adjustment using a smartphone
• hearing in noise

The Qualitative Content Analysis resulted in the identification of three domains, containing eleven themes and 136 sub-themes. 

Domain One (Clinical Processes) contained two themes: 

• Hearing Assessment 
• Hearing Aid Acquisition 

Domain Two (The Device) contained five themes: 

• Function
• Performance
• Physical
• Device Management 
• Maintenance 

Domain Three (The Person) contained four themes: 

• Satisfaction
• Quality of Life
• Personal Adjustment 
• Knowledge 

The Qualitative Content Analysis results were very similar to the Topic Modeling results. However, the Qualitative Content Analysis was able to identify some new things that were not identified via the Topic Modeling method. For example, when consumers talked about sound quality, the automated analysis (Topic Modeling) identified these comments as a sound quality theme (or cluster) but did not know if the comments were positive, neutral, or negative. The Qualitative Content Analysis was used to further delve into those comments.  

The automated LIWC helped identify various psychological, social, and clinic visit-related language dimensions. Examining the association between key linguistic variables and the overall rating we learned two things: First, the more that people were personally, socially, and emotionally engaged with the hearing device experience, the higher they rated their hearing device(s). Second, a minimal occurrence of clinic-visit language dimensions was related to benefit and satisfaction ratings. For example, if people mention paying too much money, their overall ratings were generally lower. Conversely, if people wrote about their health or home, the ratings were higher. There was no significant difference in linguistic analysis across different hearing aid brands and technology levels. 

The main takeaway from this study is that consumer reviews about hearing aids are very insightful and provide information about consumers' experience and satisfaction. They may even help predict hearing aid outcome. 

12. You also have findings from analyzing consumer reviews that give us information about hearing healthcare services?

We do. In another study (also under review), we extracted 9,622 consumer reviews of hearing healthcare services from Google.com. The reviews were samples from clinics based in 40 different cities across the U.S. The data extraction included consumer reviews that were responses to the open-ended question, “Share details of your own experience at this place” and an overall rating of the global experience on a 5-point scale ranging from “very good” to “very poor”. In addition, some meta-data about the cities (i.e., region, population size, median age, percentage of older adults) were also noted. Similar to the hearing aid study, the text data were analyzed via two different methodological approaches: 1.) automated Topic Modeling to identify the main themes, and 2.) automated LIWC analysis to identify linguistic aspects.

The majority of consumers appeared satisfied with their hearing care services, with nearly 95% of consumers reporting “very good” and “good” (mean rating of 4.78 on a 5-point scale) on the global experience scale.

The analysis of text responses using automated Topic Modeling resulted in seven clusters within two domains:

Domain One (Clinical Processes), clusters include:

• Administration 
• Benefits 
• Acquisition 

Domain Two (Staff and Service Interactions), clusters include: 

• Communication 
• Professionalism 
• Service 
• Satisfaction 

We found that when reviews had content about the Administration process, there was a relationship to the overall rating. That is, when the reviews mentioned the Administration process, they mostly described negative experiences, and therefore these reviewers were more inclined to provide poorer overall ratings.

Automated LIWC analysis was used to examine the association between the overall experience rating and the key linguistic variables. Two broad findings were revealed: First, when reviews had more mentions (or engagement) in terms of social processes, positive emotions, hearing, and work, they were associated with higher overall ratings. Second, higher engagement (e.g. more mentions) of negative emotions, time awareness, and money were associated with lower overall ratings.

13. What are the takeaways from these findings?

There are several implications. From the research perspective, it was interesting to see that three different methodological approaches each provided unique contributions to understanding the dataset. From a clinical perspective, it is interesting to see that although consumers indicate an overall positive rating for hearing aid performance and benefit, they also describe a myriad of barriers limiting their success (negative comments) when responding to an open-ended question. 

Also, these studies provide an important insight into hearing benefit and satisfaction. The content of online reviews about hearing aids and hearing care services show that both social/emotional dimensions, as well as clinic-visit related aspects of the rehabilitation process, affect hearing aid benefit and satisfaction. These results suggest that hearing care professionals should employ a patient-centered approach to rehabilitation to ensure individual patient’s needs are met. Hearing aid benefit and satisfaction extends beyond simply fitting the devices in the clinic; a whole person approach is needed. 

14. Are these consumer reviews more helpful for large companies or organizations rather than a small private practice?

Online consumer reviews are important for everyone irrespective of the size of the practice or organization. However, resources available to examine these as well as what they do with this data may depend on the type of organization and scope of their work. For example, hearing aid companies may use the reviews on specific hearing aids as early evidence of their products, and may consider making some design or processing changes if users are complaining about something (e.g., acoustic feedback, battery compartment, rechargeable unit, smartphone connectivity). Practice management companies or private independent clinics may look at the hearing aid reviews as well as reviews of hearing care services to make necessary changes to their service delivery model (e.g., administration process) to ensure they meet the needs of their patients. Smaller individual practices may find qualitative analysis of reviews manageable.

Consumer reviews may be first categorized based on a number of factors (e.g., different products, different services, different people who provided services, or demographic and health-related variables of consumers) so that we can make a more detailed analysis of how different consumers may rate their experiences. In addition, healthcare professionals' incentivization can also be linked to their consumer ratings (e.g., some additional pay if they have very high consumer satisfaction ratings). These kinds of proactive approaches to analyzing, understanding as well as managing the consumer experiences are likely to create more trust and loyalty, as well as result in better health outcomes for our patients. 

15. Has this type of data analysis been used in other areas of healthcare?

Healthcare researchers and clinicians from several disciplines are now starting to pay attention to Internet health information as well as online consumer reviews. There are a few studies in other health areas that have examined online reviews regarding health products and/or services, in an attempt to draw meaningful inferences. For example, studies have looked at how online reviews affect physician outpatient visits (Lu & Wu, 2019); the relationship between online reviews and rehospitalization in skilled nursing facilities (Ryskina et al., 2020); and the adverse effects or performances of some medications (Adusumalli et al., 2015; Borchert et al., 2019). The Consumer Health Informatics area is fairly new, and we are likely to see rapid growth in scientific work in this area in the coming years. We can draw inspiration for studies as well as changes to clinical practice by looking at what is happening in other areas of healthcare.

16. And these reviews would seem to provide more immediate information about products and services?

That indeed is one of the benefits. Within audiology, we have a lag between technological development and the development of an evidence-base. For instance, we have new hearing devices (or new features) that are released almost every year just like smartphones. To develop research evidence-bases on these it might take 2-3 years, or at least 12 to 18 months. By the time the research evidence is out, the technology may be outdated and we may have moved on to a new product. In some other areas of health, consumer reviews have been used as early evidence about products or services, as well as to study side effects on large populations. For example, although drug development has a long and rigorous process the drugs may not have been used by hundreds and thousands of people. So, drug companies are starting to watch consumer reviews carefully as early evidence of outcomes, as well as to document any side effects that they were not aware of. In the same way, I believe that consumer reviews about hearing aids can serve as early evidence. We have to be cautious, of course, as there may be a placebo effect or unreliable reviews. However, we do have the opportunity to learn something if we simply treat this as early evidence, but not as a replacement of clinical trials (or controlled experiments).

17. Have the direct-to-consumer model and/or teleaudiology models had any bearing on online consumer reviews?

The new service delivery models including direct-to-consumer, as well as teleradiology, are likely to make online consumer reviews more important. People who use these models are likely to be active online and more likely to leave online reviews than traditional patients. For audiologists and researchers, this provides easy access to gather patients’ views. Those who pay attention, and are responsive to adjust their service delivery are more likely to succeed in the coming years.

18. How do you see consumer reviews being gathered and analyzed in the future?

I think that consumer reviews will become an even more important variable in all areas. Currently, people are likely to provide one review of a service or practice by typing responses online. In the future, the reviews can be provided more regularly (e.g., longitudinal data with reviews at several stages like a few days, weeks, months, or years after receiving services). This way of collecting data is called Ecological Momentary Assessment (EMA). EMA is becoming more common in healthcare including within audiology. Consumers may be able to record their views and experience and share their reviews in real-time. These speech data can be converted into text for applying NLP. Healthcare practices may even make this a routine process for gathering patient experiences as a way to encourage consumer reviews. I also anticipate the development of Chatbots in which some computer algorithms can respond to user’s concerns or questions by analyzing the reviews, similar to how Siri or Alexa work today. 

19. Does this area of research fit within audiology education and training?

Elements of consumer reviews about hearing aids can be useful for hearing aid courses when discussing the hearing aid experience, as well as for looking at the measurement of benefit and satisfaction. Online reviews about hearing healthcare services can be good reading for practice management as well as audiologic rehabilitation courses. The idea behind the online consumer reviews is to listen to patients’ views and be more responsive to adapt the service delivery models to be more patient-centric.

20. What are the next steps for your research?

Our recent studies of online reviews have limited user data (e.g., age, gender) as they were extracted from online sources. We are planning to do some clinical studies with detailed meta-data as well as clinical outcomes. This way we can see how well the consumer reviews relate to standardized measures. More importantly, we can see if reviews indeed predict outcomes.

We are also conducting studies based on postings from social media. For example, we have built a large corpus of social media posts (n=130,000) on conversations about tinnitus on Reddit. We are trying to determine the key issues that users are discussing about tinnitus. Is there anything new in these discussions that are not covered in the academic literature? Do patients discuss some issues online which they typically don’t discuss in the clinic?

In collaboration with Dr. Abram Bailey at Hearing Tracker and Dr. Erin Picou at Vanderbilt University, we also have some large-scale survey data (n=2,000 and n=15,000) on hearing aid users about hearing aid benefit and satisfaction, as well as preferences for different hearing aid features. We are applying some advanced statistics to identify key factors that may influence hearing aid benefit and satisfaction, as well as different hearing aid user profiles based on their preferences. I think these studies will supplement what we have learned from MarkeTrak and EuroTrak surveys. Perhaps we’ll be able to talk about some of these findings here at 20Q in the future.

Author’s Footnote  

A little background. Several years ago, I started collaborating with some sociologists (Prof. Berth Danermark and Dr. Per Germundson from Sweden) and science educator with expertise in automated text-analysis (Dr. Pierre Ratinaud from France) to understand the social representation of hearing loss and hearing aids. This led to a few multi-country studies (Manchaiah et al., 2015a; 2015b). These studies provided some new insights about the general public’s view towards hearing loss and hearing aids that were not evident from studies using attitude or stigma theories, which are most commonly used within audiology. I also started collaborating with Dr. Rebecca Kelly-Campbell in New Zealand on some health literacy studies looking at internet hearing health information (Manchaiah et al., 2019a; Kelly-Campbell & Manchaiah, 2012). These studies helped me develop interest in online health information.

I moved to the United States in 2015 to take up a position at Lamar University. My main role was to provide research leadership to the department and the university. At the time, I wanted to establish a hearing aid lab. We did a series of studies on hearing aids and personal sound amplification systems (PSAPs) (Manchaiah et al., 2017, 2019; Manchaiah, 2018; Tran & Manchaiah, 2018). However, it was evident that we were really lagging behind other reputed labs within the US. Often, we would work on a project and by the time we publish a similar data would come out from other labs. I then started thinking how we can make unique contribution to hearing aid research without competing with other labs. I realized that focusing on the patient’s view would be unique and timely due to the move towards person-centered care. Moreover, during some conference interactions with a few colleagues such as Prof. De Wet Swanepoel (South Africa) and Dr. Rebecca Bennet (Australia) showed interest in this work. We had to develop interdisciplinary collaborations with experts from different disciplines (e.g., social psychology, computer science, marketing) who had the right knowledge and skills to help us learn new methodologies. Together, we performed studies on online consumer reviews and currently are doing a series of studies looking at internet health information, especially on social media (e.g., Facebook, Twitter, Reddit, YouTube) on hearing loss and tinnitus (Manchaiah et al., 2018, 2020a, 2020b; Ni et al., 2020). The first two years, I was not comfortable talking about this work as I was not sure what we’re studying and how useful the findings would be. However, I am now convinced that this area of research is extremely important for hearing healthcare.

Acknowledgments

I would like to acknowledge that the work discussed in this article was done in collaboration with audiology colleagues: Prof. De Wet Swanepoel (South Africa), Dr. Rebecca Bennet (Australia), Dr. Abram Bailey (United States), as well a few world-renowned social psychologists including Prof. James Pennebaker (United States) and Dr. Ryan Boyd (United Kingdom).

References

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Manchaiah, V., Bellon-Harn, M.L., Michelles, M., Vinay., & Beukes, E.W. (2020a). A cross-sectional analysis of portrayal of hearing aids in YouTube videos. Journal of the American Academy of Audiology, In Press.

Manchaiah, V., Bellon-Harn, M.L., Mercado, I., Beukes, E.W. & Vinay. (2020b). Portrayal of hearing loss in YouTube videos: An exploratory cross-sectional analysis. American Journal of Audiology, 29(3), 450-459. https://doi.org/10.1044/2020_AJA-19-00121

Manchaiah, V., Danermark, B., Ahmadi, T., Tomé, D., Rajalakshmi, K., Zhao, F., Li, Q. & Germundsson, P. (2015a). Social representation of ‘hearing loss’: Cross-cultural exploratory study in India, Iran, Portugal and United Kingdom. Clinical Interventions in Aging, 10, 1857-1872. DOI https://dx.doi.org/10.2147/CIA.S91076.

Manchaiah, V., Danermark, B., Vinay., Ahmadi, T., Tomé, D., Rajalakshmi, K., & Germundsson, P. (2015b). Social representation of ‘hearing aids’: Cross-cultural study in India, Iran, Portugal and United Kingdom. Clinical Interventions in Aging, 10, 1601-1615. DOI https://dx.doi.org/10.2147/CIA.S86108.

Manchaiah, V., Dockens, A.L., Flagge, A., Bellon-Harn, M., Hartwell Azios, J., Kelly-Campbell, R. & Andersson, G. (2019). Quality and readability of English-language Internet information for tinnitus. Journal of the American Academy of Audiology, 30(1), 31-40. DOI: 10.3766/jaaa.17070

Manchaiah, V., Ratinaud, P. & Andersson, G. (2018). Representation of Tinnitus in the US newspaper media and in Facebook pages: Cross-sectional analysis of secondary data. Interactive Journal of Medical Research, 7(1), e9. doi: 10.2196/ijmr.9065.

Manchaiah, V., Taylor, B., Dockens, A.L., Tran, N.R., Lane, N., Castle, M. & Grover, V. (2017). Applications of direct-to-consumer hearing devices for adults with hearing loss: A review. Clinical Interventions in Aging, 12, 859-871. doi: 10.2147/CIA.S135390.

Manis, K.T., Wang, Y., Chaudhry, A. (2020, May). 5 principles for responding to customer reviews. Harvard Business Review. Available at www.hbr.org

Ni, J., Bellon-Harn, M.L., Zhang, J. & Manchaiah, V. (2020). Twitter usage using common reference to tinnitus. American Journal of Audiology, 29(2), 206-217. https://doi.org/10.1044/2020_AJA-19-00055

Ryskina, K.L., Andy, A.U., Manges, K.A., Foley, K.A., Werner, R.M., & Merchant, R.M. (2020). Association of online consumer reviews of skilled nursing facilities with patient rehospitalization rates. JAMA Network Open, 3(5), e204682. https://doi.org/10.1001/jamanetworkopen.2020.4682

Tran, N. & Manchaiah, V. (2018). Outcomes of direct-to-consumer hearing devices for people with hearing loss: A review. Journal of Audiology and Otology, 22(4), 178-188. doi: 10.7874/jao.2018.00248.