Market research successfully accomplishes its goals when insights translate to action. Nowhere is that arguably more important than in the life sciences, where gaining a better understanding of patient and professional needs and expectations can make the difference between life and death. As head of research and design at inVibe Labs, I deem it my personal responsibility to ensure we give a voice to health stakeholders, and empower our clients with what we learn.

Inspired by the candid opinions and feelings of thousands of patients we’ve recorded, analyzed, and shared, I flew to Boston this mid-October to present at the DTC National Conference. Thrilled to keynote “Transforming the Patient Experience Through Innovative Market Research,” I presented an overview of the area of opportunity that inVibe fills, and demonstrated how our unique voice response technology brings out the best features of qualitative and quantitative research.

I was also excited to attend many of the other keynote presentations, panel discussions, and parallel track experiences throughout the two-day conference. Given my expertise in linguistic analysis and platform design, I viewed the industry perspectives through the lens of the patient experience and tech, and am eager to share my thoughts on the subject matter. After some reflection, I’ve broken down the takeaways into 5 essential DTC trends for 2021 and beyond:

Trend #1: COVID and the Growing Shift to Digital Health

The global pandemic has impacted all our lives, so assessing its seismic impact on DTC engagement and especially the patient experience was an apropos way to start the conference. Principals from ZS Associates presented on the latest shifts in health behavior due to COVID and their implications, revealing an eye-opening acceleration of legacy trends, including an exponential increase in telehealth, virtual care, remote monitoring, and connected health.

As the pandemic stubbornly continues to threaten and inconvenience life, the return to normalcy has been delayed. The realization is also taking hold that many of the transformative trends triggered by the lockdowns and physical distancing may indefinitely persist, and even become permanent. Of these, none are more striking to me than the rethinking of the point of care experience, and how patients have been offered ever-greater flexibility and empowerment.

Patients, their physicians, and all healthcare system stakeholders have been given convenient and scalable options with the expanded and increasingly immersive utilization of digital video, biosensing, and other virtual technologies. At inVibe Labs, we’ve already witnessed success from implementing voice-response tech, human and machine learning analytics, and dynamic reporting for market research. Patient voices are finally being heard.

Trend #2: Expansion of Digital Touchpoints Throughout the Health System

Another compelling trend reshaping DTC engagement is expansion of digital beyond the point of care. Ritesh Patel of Finn Partners led a vibrant panel discussion focused on digital acceleration of the pharmacy and pharmacist. The inexorable evolution of brick and mortar pharmacies into virtual laboratories finally capable of taking advantage of years of progress in mobile health and eCommerce is yet another benefit of the otherwise devastating pandemic.

The panel revealed exciting innovations from the integration of health trackers and other compliance-boosting tech to Amazon-like ordering convenience and follow up. With patient needs at long last front and center, the utility and personalization offered by now routine app-driven digital services is poised to redefine the pharmacy experience, and augment the role of the pharmacist as extended support between patients, professionals, and pharma brands.

Lisa Brookwalter of Twitter led a discussion on another expanding digital touchpoint, the growth of social media as a primary screen for patients. Joined by Patty Ryan of Publicis and Steven Xie of Biohaven Pharma, the group shared how channels such as YouTube and Twitter often supplant broadcast media in effectiveness for DTC, becoming go-to resources for health information and peer-to-peer support to millions of increasingly empowered patients.

Trend #3: Fixing Health Disparities through Improved Digital Access

Another captivating group of experts featuring Dr. Jessica Shepherd of Verywell, Roslyn Daniels of Black Health Matters and others discussed the impactful creation of intentional, authentic DTC communications for multicultural communities. Keys to success are understanding preferred channels, developing a relevant and emotionally impactful lexicon, and educating internal reviewers on the nuances of properly connecting with often disenfranchised segments.

This discussion alongside the social media panel particularly resonated with me, since a big component of my work with inVibe is helping life sciences companies better engage with patients of diverse backgrounds and nuanced expectations. Historically, market research has endured some implicit biases due to sample selection criteria and methodology; by empowering subjects to collect voice data from their own phones, we have strived for universal access.

Sheila Thorne of the Multicultural Healthcare Marketing Group also pointed out the singular importance of appropriate creative and content, another area where inVibe has shed light. Too often, pharma companies lack the patient sensitivities and insights to fully understand and appreciate cultural references, distinctive community needs, and lingering societal prejudices. By analyzing and extracting best practices from the patients themselves, progress is made.

Trend #4: Big Data 2.0 -- Next Generation Analytics to Heighten Patient Engagement

The future of digital marketing was brought to life with the help of a keynote from Hart Mechlin of Veeva, who demonstrated the latest advances in precision targeting, dynamic engagement, and ROI substantiation using sophisticated analytics. I was fascinated by the extent to which marketers gain understanding of our healthcare audiences throughout their treatment journey, and how measurement across multiple platforms and channels can create dimensional profiles.

We take data processing equally seriously at inVibe, adapting the latest in AI and machine learning tech to augment human linguistic analysis of direct voice responses from patients. Many of the same tools used by tech giants are incredibly useful for extracting patient insights from their candid and often chaotic feedback; also similar is our use of data visualization and dynamic reporting to convey actionable recommendations designed to heighten engagement.

As another illustrative example of big data power, media fragmentation and the need for balancing budgets were elucidated by Ryan Wilson and Carens of Samsung. Their essential takeaway: the transforming market, driven by changing patient user behaviors, demands a re-evaluation of spend between linear TV and ad-supported streaming services, among other channels. I loved hearing how major players are listening to patients, too, here in aggregate.

Trend #5: Better DTC Advertising through Better Understanding of Patient Needs

John Kenny of InTouch Solutions highlighted the vital importance of authenticity when it comes to DTC engagement, hitting home the need for establishing trust and genuine connection. Pharma tends to communicate with patients through the lens of their disease burden, rather than taking a few steps back and building a relationship based on their holistic needs and expectations as complex, multidimensional people seeking guidance and emotional support.

Copresenters Bonnie Jean Perkins of Alexion and Julie Cosgrove of Alnylam Pharmaceuticals demonstrated how these humanistic strategies are even more vital for rare diseases, where the patient communities are small and identification remains a main goal. I strongly related to both keynotes, my work at inVibe similarly focused on encouraging patients to transparently share their thoughts and feelings about the best way to create and sustain a meaningful connection.

I’m hopeful and confident that by listening and responding to the patient voice across multiple disease states, the life sciences industry can improve patient engagement and empowerment. The ultimate result for DTC marketers is a better and more lasting connection between brands and their beneficiaries, realized through improved creative, content, and downstream media activation that makes a difference. I’m privileged to help our industry listen and learn.

If you saw my presentation at DTC National in Boston on October 13th, then thank you for attending, and I can’t wait to hear your feedback. And if you couldn’t make it, the fine folks at DTC Perspectives are making it available online for the 

 on November 3 at 12:15pm. Continuing the spirit of open communication and listening, I’m eager to tell you more about our exciting work at inVibe, offer you a demonstration, and learn more about your market research needs.

inVibe Labs' Director of Research shares her 5 Key Patient Experience Trends for 2021

Identifying 5 Key Patient Experience Trends for 2021

Big Tech companies have mastered the consumer experience, enabling the likes of Apple, Google, and Amazon to evolve within a few short years from tiny Silicon Valley startups to trillion-dollar market cap behemoths. Through a robust combination of digital profiling, precision targeting, and dynamic engagement, they provide an unprecedented level of personalization, two-way communication, and real-time responsiveness that has transformed all of our lives.

Understanding the end-user is a prerequisite for success in most industries, arguably none more important than healthcare. But unlike consumers of media, commodities, or luxury goods—whose buyers consider shopping a flattering reflection of themselves and their values—patients are instead burdened by their disease, viewing treatment options as the only way to resume their everyday lives. The stakes couldn’t be any higher.

That makes the relatively slow incorporation of the patient voice into the clinical development and commercialization of drugs puzzling. Part of the challenge has been the paternalistic nature of the healthcare system itself, where physicians have historically been anointed sole decision-makers. But thanks to the rise of patient advocacy—largely driven by digital data sharing and social media—patients have an increasingly powerful voice in their own care.

Healthcare companies are now behooved to listen to that voice, and have already made great strides throughout the downstream marketing of post-launch treatments. Many of the same tools used by technology and media firms are now being applied by pharma and life sciences to better understand and empower patients to become stewards of their own healthcare journey, resulting in a progressively improving patient-brand relationship. While strides have been made on the commercial side, opportunities abound to bring the patient voice to the center of clinical drug development.

Key decisions made in the early stages of drug development cascade throughout the lifecycle of pharma brands, so gaining insights into patient needs is vital. The implications are consequential, both in terms of potentially costing pharma companies millions, and compromising the patient experience itself.

For example, lacking sufficient understanding of patient attitudes toward disease burden, a pharma company may design a clinical study that sufficiently meets its end-points, yet leads to approval of a drug with low adoption. Conversely, companies may fail to move forward with otherwise promising drug candidates due to questionable tolerability—yet if asked, patients would be willing to handle side effects in exchange for an improved overall quality of life.

Since the pharmaceutical industry has repeatedly developed products that fail to meet these critical patient needs, Congress and the FDA recently issued the Cares Act and extensive new guidance to ensure patient voices are heard at key touch points throughout clinical development. With a renewed focus on collecting comprehensive and representative patient input, fresh methods to identify relevance, and incorporating clinical outcomes assessments into endpoints for regulatory decision-making, patient insights are becoming increasingly influential.

Taken together, these governmental and industry-based initiatives have spawned a new era in patient-focused drug development (PFDD), uniquely positioning patients to inform the therapeutic context and make actionable recommendations. Although just beginning, PFDD has already systematized approaches to data collection, identified best practices, captured data on tradeoffs between benefits and risks, and analyzed input on communications preferences.

Hurdles nonetheless abound, many of them endemic to the pharmaceutical and healthcare industry. These include a patchwork of regulations and policies across a chaotic and evolving landscape; varied market needs from region to region, and country to country; failing to build an internal company culture conducive to patient-focused research; limited cross-functional collaboration; and hesitation on the part of executive leadership, often stuck in old habits.

Fortunately, the clinical process naturally lends itself to direct patient involvement—pharma simply needs to listen and more proactively participate. After all, patients are already recruited into clinical trials, share their feelings about the disease burden and their treatment journey with experts, and actively assist with the generation of evidence and actionable data. Qualitative interviews and the managing of patient thought leaders further enhance their burgeoning role.

Effective inclusion begins with study design, where technologically facilitated qualitative research can extract the candid thoughts and feelings of patients with the goal of increasing their participation and retention. By better understanding end-users through patient-reported outcomes measures, researchers are able to create more patient-centric trials that produce increasingly meaningful results, ultimately approving safer and more effective drugs.

Past the clinical trial phase, improved market research methods can also further optimize every forthcoming step of drug development. From nomenclature to naming, branding to messaging, reimbursement to support and relationship management programs, tapping into the patient voice informs and empowers researchers, educators, marketers, and the vested healthcare professionals who discover, evaluate, and prescribe the approved drugs.

A compelling example of infusing the patient voice at the earliest stages of the development process was for 

, the triple-combination treatment of cystic fibrosis from Vertex. A collaboration between the CF Foundation (that helped raise millions for research) and scientists, patients, and their families brought the patient voice into many aspects of the clinical process. This resulted in a breakthrough drug that launched months ahead of schedule.

But challenges remain for clinical teams struggling for patient input, often employing legacy quantitative and qualitative market research methods that remain resource-intensive, inefficient, time consuming, expensive, and inflexible. Traditional approaches, including speaking with advocacy groups, convening patient ad boards, conducting phone interviews, and relying on the collective experience and intuition of brand teams can only go so far. Innovation is needed.

Embracing Patient-Focused Drug Development

Exploring novel creative and agile methods are therefore necessary to ensure patient insights are captured and resonate. The good news is that many of the same tools used by Big Tech for commercialization—alongside proven patient outreach techniques—can be applied to market research conducted at earlier clinical development phases. Let’s consider each step of the data collection, processing, and reporting cycle, and see how next-generation researchers are gaining transparency into the hearts and minds of patients across numerous disease states.

Data collection tools are now able to capture every nuance of a patient’s cognitive response and emotional intent through voice and other modalities. By recording frank patient reactions to creative and messaging stimuli, along with detailed, often stream of consciousness answers to open-ended questions, researchers can then process gigabytes of data with more depth and sophistication than through limiting legacy surveys and questionnaires.

The data processing demands of such layered input is met through a combination of AI/machine learning technology and human-powered analysis. Cutting edge semantic analysis software known as GPT-3 is also being used to rapidly search through and extract relevant content, laden with meaning and emotional context. The result is a dynamic database capable of answering secondary and tertiary research queries, diving deep into the patient’s world.

Without the reporting of actionable market research intelligence, the entire effort would be for naught. Here the dynamic, data-driven dashboards common to digital platforms are helping clinical researchers better understand and contextualize patient feedback and sentiment. Actionable patient-driven recommendations for the optimization of trial designs, disease awareness, and messaging considerations help drive an improved patient experience.

Even with the most sophisticated market research techniques and technologies, the patient voice will remain unheard throughout clinical development without significant company investment in patient listening. Companies must now commit time, money, and resources as early and as enthusiastically as possible. By building trust and nurturing patient relationships, pharma benefits downstream when critical patient insights translate into successful brand decisions.

From patient journey studies to detailed feedback about unmet needs, efficacy and safety concerns, adherence considerations, reimbursement support, and advocacy, candid feedback from patients early on in the development cycle leads to improved post-launch engagement. Only by creating a culture where these insights are valued and where their acquisition is assured can pharma brands keep pace with the increasing demands of their stakeholders.

Until recently, gaining transparency into the thoughts and sentiments of patients fell exclusively into the realm of quantitative and qualitative research. Novel approaches fill the gap between the broad but shallow results of quant, and the deep but narrower revelations of qual. This gives clinical researchers agile and scalable ways to incorporate the patient voice throughout the drug development process. The result is empowered patients, improved drugs, and better healthcare.

By combining their dedication to patients with an embrace of the next generation in market research capabilities, pharma is poised to catch up to the tech firms paving the way for the ideal consumer experience. Given how healthcare is often a matter of life and death, no initiative is more significant than improving public health through the same tools we use to shop, commute, and entertain ourselves. Patients are speaking, and pharma must listen every step of the way.

While strides have been made on the commercial side, opportunities abound to bring the patient voice to the center of clinical drug development.

Why the Patient Voice Should Influence Drug Development — and How It Can

A core principle of inVibe has been the relentless pursuit of improving the market research process. We started with the goal of conducting cost-effective, high-speed qualitative research that didn’t sacrifice depth and quality. To this end, we continue to build software for all market research stakeholders — from research participants to our primary research customers to our internal teams.

A key area of opportunity for inVibe has been developing tools that aid the analysis of the data we collect. One of the most challenging aspects of what we do, and qualitative research in general, is organizing and structuring qualitative data. Thanks to our unique voice-response methodology, we start with much more structured data than traditional qualitative research. For each question in an inVibe study, we have an array of transcribed audio files — one for each participant in the study. Clients and analysts alike have long been able to use the inVibe platform to filter, sort, or group answers in our reporting dashboard by a variety of factors such as demographics, speech-derived emotions, and other linguist-coded signals. 

But what if we could organize those answers by the content of the answers themselves? Wouldn’t it be easier to listen through all of the respondents who took a negative stance on a creative concept at once? 

Even seeing the distribution of positive/negative/neutral helps frame the data in a meaningful way. Any pre-work the technology can do to help organize content ahead of time translates to faster and simpler analysis. In other words, our team of insight explorers can spend less time creating a map of the content landscape, and more time consuming its lush and revealing landmarks.

Recent technological advancements in machine learning and natural language processing (NLP) have pushed us to expand our thinking about what processing tasks can be offloaded to a machine. inVibe is excited to share our progress in this area through utilizing GPT-3, an advanced language model created by 

GPT-3 stands for "Generative Pre-trained Transformer 3." Consisting of over 175 billion parameters, it is one of the largest language models ever created. GPT-3 represents a drastic leap in the capabilities of NLP models. Because of the massive size of the model and its corpus, GPT-3 excels at what are called "zero-shot," "one-shot," or "few-shot" machine learning tasks. That means tasks that involve either zero, one, or a few examples. Examples in this context are the sample data that teach or train the machine learning model. Much of qualitative market research involves a smaller number of respondents, and therefore fewer examples to leverage. This naturally leads to diminishing returns for small sample sizes as the number of examples required increases.

, inVibe has built several algorithms on top of GPT-3. These algorithms are used to perform novel NLP tasks, some of which we will explore in more detail below. If you are curious about some of the technical challenges around working with GPT-3, see Edge Analytics' recent post on 

getting the most out of GPT-3 based text classifiers

It is worth noting that GPT-3 is better at some tasks than others, and it does have limitations. However, combining GPT-3 with expert human analysts (e.g., our extensive team of language experts) and other proprietary automated tools gives us the best of both worlds. It allows us to produce reports that are rich with information and insights efficiently and with a high degree of accuracy.

Sentiment analysis is far from a new idea in the world of NLP. It is perhaps the most widespread task used in NLP. Because of this, many are quite familiar with its traditional limitations. While simple examples perform reasonably well, things start to break down when phrases include rambling, double negatives (e.g., "I wouldn't say it was my least favorite."), or references to other topics (e.g., "I like this a lot more than the other one, which was confusing and uninformative."). Recently, state-of-the-art sentiment analysis engines have been built using deep machine learning, but these models require hundreds if not thousands of domain-specific examples in order to deliver relatively meaningful results.

However, with the GPT-3 algorithms we developed, inVibe was able to achieve better performance on "subject-aware" sentiment analysis than any of the existing solutions we tested against. "Subject-aware" means that our technique considers the subject of the phrase, not just whether the words are positive or negative in general. This is a marked improvement over traditional techniques and even exceeds the performance of advanced models like Google Cloud's semantic analysis API.

Let's look at some examples. The following responses were collected from patients participating in an inVibe voice-response survey where they were asked to provide their opinion about three different proposed advertising concepts for a heart condition called atrial fibrillation: Concept J, Concept F, and Concept K.

Google Cloud

inVibe GPT-3

The algorithm output in this example illustrates the power of "subject-awareness." The quote is in response to a question about "Concept F." If we break it down sentence-by-sentence, Google Cloud's API assigned a negative score (-0.8) to the first sentence: "

" It assigned a positive score to the remaining sentences, which results in the response being labeled as "neutral" as a whole.

In contrast, inVibe's GPT-3 algorithm 'understood' that the response was about "Concept F" and correctly labeled the response as "positive" overall. We saw this consistently across a majority of the responses captured with an accuracy of over 90%.  This is a case where incorporating the subject into the design of the algorithm is able to significantly improve performance in an area where most machine learning models struggle to accurately derive sentiment.

One advantage of operating our proprietary voice-response platform at inVibe is that we can invest in optimizing seemingly rare or niche tasks. For example, let's consider a common type of study we conduct — 

. These studies are typically conducted with physicians shortly following the release or presentation of new data about a therapeutic intervention. Oftentimes a client leverages our platform to measure a physician's enthusiasm or hesitation around adopting a potential new treatment option — or to determine whether some aspect of the study design may be causing concern.

To aid in this analysis, we developed an algorithm to identify and extract 

 (e.g., trial design, side effects, etc.) from any response our system captures. Unlike sentiment analysis, this task results in an open-ended output that isn't restricted to choosing from a few predefined labels. Instead, inVibe's algorithm is able to accurately extract 

 concerns, including ones it has never seen before, from the collective body of transcribed content. Furthermore, the algorithm is also capable of paraphrasing these concerns in remarkably succinct phrases.

This example demonstrates how our algorithm can accurately identify and extract any number of concerns and is undeterred by stop words like "uh" and "um."

In this example, our algorithm correctly identifies one key concern and paraphrases by shortening "a very small sample of patients, only seven patients as part of the trial" to just "small sample size." Compared to extracting entire phrases or sentences, paraphrasing makes it easier for analysts or clients to quickly read through the list of concerns and identify any commonalities.

Here, the response is full of medical jargon and run-ons. Even the part of the response that includes some concerns is expressed through a collection of semi-coherent and wandering phrases, like "

Uh, I guess there was some concern, obviously, about the.

" This kind of language is part of how people naturally speak but can easily cause problems for even some of the more sophisticated algorithms available to researchers and analysts. Despite this, inVibe's algorithm was able to correctly identify "febrile neutropenia" as a specific concern in this physician's response.

A common trope when discussing AI advancements is to project into a future where the AI system is more knowledgeable than humans. While results like this would not have been possible even a few years ago, it is essential to reframe where AI fits with the future of knowledge work and insights gathering. Our use of NLP is not to replace human analysis but rather to allow language experts and customers alike the ability to focus on higher-level thinking.

These tasks are tools to add context and provide meaningful organization to our data. Much like a scientist behind the microscope — the instruments used to measure and distill may change, but the need for human intellect and contextually relevant interpretation remains constant.

The ability for an analyst to view the distribution of sentiment in reaction to a concept enables them to immediately ask better questions of the data — and helps them understand where to dive in. Likewise, seeing a range of concerns about a study has a similar effect.

For insights to be valuable, organizations must have the time to take action. However, the longer the data collection and analysis process takes, the less relevant the findings will be. This same heuristic can apply to a more general truism — the longer it takes to answer a question, the less valuable that answer will be. This is why we as an industry have so often focused on speed at the expense of quality. With the steps we’ve taken, inVibe hopes to increase the clarity, organization, and timeliness of our insights for clients.

In part 2 of this series, we’ll expand on how modern, AI-assisted workflows can bring about new and exciting ways of gathering insights and listening to customers.

How GPT-3 Unlocks Deeper Listening at inVibe

inVibe's breakthrough NLP algorithm accelerates healthcare insights

Apple, Microsoft, Amazon, and Google/Alphabet are the first companies to reach market capitalizations of over a trillion dollars, with no end in sight for continued exponential growth across the technology sector. At the core of their jaw-dropping success is a revolution in the customer experience, triggered by digital and empowered through data-driven personalization.

The result has been a stunning transformation in the everyday lives of most people on the planet, raising expectations for the benefits brands must now provide their customers. Entire industries from entertainment to hospitality to commerce are conveniently activated in real-time from anywhere with a click, tap, or swipe, the user experience improving with every interaction.

Although great strides have been made to similarly improve the patient experience, healthcare remains a sluggish sector along this road to customer-centricity. Numerous obstacles abound, including the legacy of a paternalistic approach that marginalizes the patient voice; privacy, compliance, and interoperability restrictions that limit the sharing of data; and other factors. The disappointing and often tragic irony of such healthcare digital inertia is the obvious truth that nothing is more important than our health and wellness.

Compounding the frustration is the tangible progress that has nonetheless been made across digital health, from diet and fitness apps for consumers to significant advances in diagnostics and telemedicine for professionals.

One of the silver linings of the global pandemic of 2020 is the acceleration of these digital health trends, encouraging a greater appreciation of how digital technology could further improve the patient experience. A central but often overlooked element of this hopeful paradigm shift is the increasing digitization of market research, and its striking implications for healthcare brands.

The incredible potential for a digital revolution in market research begs the question of the specific gaps digital can immediately fill, and how fresh insights from innovative technology can improve the overall patient experience. So let’s take a look at how healthcare brands usually use market research, and show recent advances changing the way brands learn and grow.

The Role of Market Research in the Patient Experience

Broadly described, the “patient experience” is not only an individual’s reaction to illness and treatment, but an aspirational goal healthcare companies strive to maximize. Metrics are often used to define what a good or even an exceptional patient experience is like, with various indicators of satisfaction often going beyond direct engagement with the healthcare system.

An optimal patient experience is therefore somewhat analogous to a personalized customer experience, where buyers feel their needs are met and expectations exceeded at each touchpoint of the customer – or within the healthcare context, treatment – journey. In both cases, the end-user demands convenience, responsiveness, support, and ongoing guidance.

Patients differ from consumers, however, in that being treated for an illness is nothing like purchasing a product or service. Unlike consumers, who embrace a brand choice as a flattering reflection of themselves, patients consider their treatment as a means to an end, namely getting back to their healthy lives. If nothing else, that makes a good experience even more important.

Healthcare brand teams understand this, and like their counterparts in consumer packaged goods, entertainment, and other industries know the power of market research to try and get that experience right. At its best, market research provides actionable insights into what patients need and expect, and how they react to information from and interactions with their treatment.

With origins that go back to the Golden Age of Radio in the 1920s, market research is ultimately designed to gain transparency into marketplace competitors and trends, audience behaviors and triggers, and the effectiveness of marketing and communications efforts. Tools have ranged from landscape to ATU and SWOT analyses, and fall into qualitative and quantitative buckets.

Healthcare marketers typically rely on a broad arsenal of these research capabilities, choosing a combination that best reveals the hearts and minds of patients to improve their experience throughout the continuum of care. Compromises have had to be made between the strengths and weaknesses of these methods, though, a challenge digital tech is finally beginning to meet.

Either Quant or Qual: An Unnecessary Compromise

Optimizing the patient experience starts with understanding it, no easy task given the uniqueness of each patient, and the complexity of their journey through the healthcare system. From symptom awareness to point-of-care diagnosis, treatment decision to reimbursement, adherence to advocacy, the holistic patient experience continuously morphs and evolves.

Depending on the specific need, brand teams tend to choose between market research methods that have a reputation for being most effective: quantitative research is typically associated with larger sample sizes and the need for speed; qualitative research is used, in contrast, for more focused, hands-on studies of fewer subjects to extract more nuance.

Given the inherent dichotomy between quant and qual, brands often feel forced into a tradeoff: go broad and shallow, or deep and narrow. For example, a team might release thousands of surveys to statistically assess the diabetic patient experience surrounding a new type of insulin, or conduct a far more limited but intensive focus group to ask a tiny N more detailed questions.

Strategic brands with sufficient resources often opt for both methods: they may use quant to acquire a general understanding of the larger patient population, then follow the initial research with better informed one-on-one interviews across a select cross-section of subjects. But even such a hybrid approach frequently falls short of providing the actionable insights brands crave.

Part of the challenge is the methodology of market research itself, which is only as good as its data 1) input, 2) processing, and 3) output. Regardless of technique, the thoughts and feelings of patients must be translated into information researchers analyze and populate into a report the brand team can understand and act upon. Any weak link destroys the entire data chain.

When viewed through this three-step lens, opportunities abound for applying similar technologies the trillion-dollar big tech giants use to improve the customer experience. Although surveys and statistical analytics continue to serve quant researchers well, and first-hand participant observation remains the bedrock of qual, Next Gen market research is worth exploring.

The average human body contains over 37 trillion cells, each a treasure trove of complex health information. Our technological ability to capture biometric data of ever-increasing sophistication and volume is the foundation of the “Quantified Self,” and gateway to digital health ultimately enabling the optimal diagnosis, treatment, management, and even prevention of disease.

A legacy challenge of traditional market research is the need to translate biometrically rich behavioral data into fill-in-the-blanks survey and questionnaire bubbles, unnatural interview and focus group responses, and other limiting formats. Rigorous observational studies can be subject to bias, and force responses from uncomfortable subjects within artificial settings.

In contrast, the tech giants have mastered the science of understanding the customer experience through passive digital surveillance. Arguably controversial and often violative, the ability to monitor every action in real time provides an unprecedented and astonishingly accurate window into the attitudes, intentions, and behaviors of billions of active consumers.

Although these “Big Brother” tactics remain problematic, they reveal the incredible potential for similar data being utilized to better understand the patient experience. Under consensual research circumstances, for example, consider the power of a patient voice recording compared to a written response from a survey or questionnaire. Skeptical of the inputs? Go ask Alexa.

Imagine patients sharing their thoughts and feelings about their experience with a disease state or treatment from their own smartphones. Not only does a voice recording contain 100x or more data than the written word, but no data is lost or filtered during the input process. The data is also acquired conveniently and efficiently, and in a manner that can seamlessly scale to need.

Unlike eye-tracking widgets, heat mapping, and other emerging yet gimmicky technologies, asynchronous telephony-based voice recognition enables low friction, high speed data collection on any device. The digitized input becomes a window to the heart and soul of your patients, potentially revealing the essence of their experience with the right data processing.

Step 2: The Digitization of Data Processing

Aftermarket research data is acquired, whether in alphanumeric form from a survey or as a multivariate input from an observational study, the information must be processed. For quantitative research, where by nature the amount of data is relatively large and the analysis is statistical, the sophisticated application of mathematical algorithms is usually involved.

For qualitative research, data processing can be more subjective and ad hoc. One of the many perceived benefits and possible weaknesses of qual is this “human touch,” where experienced researchers collect the data and correlate it with similar inputs from analogous scenarios. In either case, the research is only as good as the processing remains objective and accurate.

Given these differences between quant and qual data processing, brands again feel that certain trade-offs are necessary: mountains of information from quant surveys are anonymized and statistically processed for generalized insights about the patient experience, while more dimensional, deeper data sets from smaller samples in qual might deviate from broader norms.

Innovative digital technologies can again come to the rescue: Imagine a platform that combines human (linguistic) analysis with an AI-powered processing interface that utilizes machine learning tools. Such a hybrid approach can reveal vital patient attitudinal insights from deep biometric voice data otherwise lost or never inputted in the first place through mere surveys.

By layering advanced sentiment analysis on top of transcribed textual responses, dimensions of additional emotional detail, subtle nuance, and behavioral indicators become transparent. While survey responses or observational feedback might communicate the what and how of the patient experience, emotional insights reveal the resonant why behind patient behaviors.

All too often, the data processing step of market research is ignored or considered a “black box” sandwiched between traditional inputs and outputs. A combination of sophisticated software and human-powered analytics can bridge the gap between signal and noise to extract patient experience insights precisely tailored to reveal underlying needs and fulfill unmet expectations.

Market research can input the best data and process it with accuracy and finesse, but if the insights are incomprehensible or confusing then the entire effort is for naught. The whole point of such research is to better understand and hopefully improve the patient experience; to do so, brands need not only receive intelligible insights, but simple and prescriptive recommendations.

Big tech again leads the way, having developed dynamic dashboards for their customers. From Google Adwords and Analytics to Tableau to Microsoft Azure, the presentation of data results in a visual, intuitive, and compelling way is the hallmark of digital supremacy. Gone are the days of generic PowerPoint slides of bar graphs and pie charts, as digital storytelling takes the prize.

Unfortunately, most quant and qual outputs demand significant attention and effort from the marketing team. Not only should the results be intuitive and straightforward, but key findings and recommendations must resonate and be impactful. Insights, however broad or detailed, must ultimately lead to specific action. Brands want the why, but they really need the what next?

Yet another advantage of biometric data as input is layered, dimensional output. Otherwise, it’s garbage in, garbage out. Processed voice data, to continue our tech example, lends itself well to a dynamic dashboard with insights that drive prescriptive action: from study design to endpoints to a lexicon, emotional connection is arguably one of the best indicators of future behaviors.

Megabytes of actionable intelligence across multiple modalities also lends itself to sophisticated digital features, including advanced search, audiogram creation, and listening studios. Being able to play source files instantly connects brands back to the raw input; hearing the subjects speaking for themselves infuses the output with transparency, credibility, and patient passion.

“Without data,” W. Edwards Deming once notoriously said, “you’re just another person with an opinion.” And without a visual, intuitive, and KPI-driven dynamic interface, your brand is lost at sea regarding how patients feel about their condition and its treatment – and the steps you need to take to ensure their voices are heard, their needs met, and their expectations exceeded.

Getting to the core of the patient experience through custom-designed market research is a winning strategy. Adept brand stewards identify research opportunities across functional teams and the product life cycle, from clinical trials and R&D to primary and health economics and outcomes research, et al. Tailored combinations of quant and qual are the industry staples.

But what if your quant study suggests insights that need a further evaluation? Or your qual observational project reveals anomalies that need to be tested? The current gap in market research capability is a service that scales as fluidly and rapidly as quant, while offering the depth and dimensionality of a thorough qual. You need agility, but with the extra firepower.

Although privacy and other ethical concerns continue to plague big tech, many of their proven innovations for understanding the customer experience are capable of responsibly transforming market research. These include voice recognition, AI and machine learning, and dynamic dashboards that can take both quant and qual to a whole new level with a bold third option.

Ideal use for such capabilities to better understand the patient experience include situations where patient recruitment is time-consuming or tricky; additional research depth is required with minimal ramp-up time; campaign stimuli need testing during the creative development cycle, and any other research scenarios where quant may fall short in-depth, and qual in breadth.

Innovation springs from creativity thrown at an unmet need. Harnessing the innate power of the patient’s own voice, a fresh approach to market research gives additional flexibility and strength to the arsenal of tools already at a brand’s disposal. Taken together, all healthcare stakeholders can be heard and brands more responsive by embracing the potential of biometrics and data.

“The patient needs an experience,” said Dr. Frieda Fromm-Reichmann, “not an explanation.” When done right, market research helps explain what that optimal patient experience should be like, and recommends how brands can assist in creating it. By extracting learnings from big tech and the customer experience, the digitization of market research can bring out the best in each.

Transforming the Patient Experience Through the Digitization of Market Research

While marketing research has evolved over time, we don’t often see new technologies that turn the industry on its ear. The notion of truly listening to your research introduces a way to connect with it on a level as profound as face-to-face discussions. The method is a conversational analysis platform we’ve developed at inVibe, and the results are actionable insights from voice-based customer feedback and the spoken word. Employing an automated interview process that is then analyzed by advanced machine learning and a team of linguistic experts, inVibe adds a unique layer that can supplement, and even replace traditional research methods.

Judi Profant, Director of Medical Affairs for 

, engaged inVibe to recruit 25 physicians for a research project designed to understand the type of guidance physicians prescribed during a particular course of treatment with their patients. One of the key aspects of the methodology that stood out for Profant was the participation level in the research study.

“We didn't want to ask physicians to come to a research facility. We knew that wouldn't happen,” noted Profant. The reduced burden on respondents – enabling them to participate at a convenient time and in a less cumbersome manner equated to significantly improved participation levels. “All 25 physicians responded to every question they were asked,” she added. “Not a single physician participant hung up before the end of the electronic interview. That was really impressive.”

Ultimately, Profant and her team “were able to capture the information of the patient-physician voice and how they spoke to the patient. It was representative of a physician guidance experience in clinical practice.”

a standardized questionnaire wouldn’t capture whether a physician took a collaborative or a directive approach. But in a voice recording it's abundantly clear whether the physician is being directive and giving clear guidance.

Working with inVibe has given us valuable information that we wouldn't have been able to capture otherwise

Listening: The Newest Competitive Research Advantage

Despite the many human-centered models and social listening capabilities, the healthcare industry still struggles to include the voice of the patient. Organizations need methodologies that reduce key barriers to listening – enabling them to create better products and services that deliver greater human value.

inVibe partnered with Rare Patient Voice to solve this challenge. In 2013, inVibe introduced the first-ever Automated Voice Response (AVR) platform to empower patients, caregivers, and physicians to tell their stories anytime, anywhere, and extract insights through linguistic analysis.

The same year, Rare Patient Voice was established to help patients share their experiences with organizations developing products and services.

These two organizations partnered to provide a way for patients to share their voices while delivering sharper insights, streamlining processes, and reducing costs. Working with the Rare Patient Voice patient panel, inVibe deployed their AVR solution to automatically conduct interviews with over 250 patients and caregivers across 15 diverse therapeutic conditions. The questions were designed to inspire people to share deeply personal stories. Here are a few examples:

What are some perceptions about your condition that make you cringe?

What do you wish people wouldn’t say about your condition?

If you could travel back in time and meet the person you were when you were first diagnosed, what advice would you give?

                                       Download the PDF study 

In less than two weeks, inVibe was able to automatically conduct hundreds of interviews and collect thousands of minutes of voice responses to a series of eight unique questions which evoked interesting and useful emotional content. For example:

Speech emotion recognition (SER) algorithms detected acoustic differences across therapeutic areas such as Cystic Fibrosis, Type I Diabetes, and Sickle Cell Anemia, indicating that even in chronic conditions, patients experience varying levels of engagement (

inVibe’s linguistic experts noted meaningful differences in how patients described “How I Feel,” “What I’ve Learned,” and ultimately, “Who I Am.” These subtle patterns in word choices and intonation can help deepen our understanding of the patient experience as well as explain, and even predict behaviors.

In addition to capturing patient’s responses to thought-provoking questions, inVibe's linguistic experts leverage AI to extract powerful insights from the voice data. You can access all the patient stories that were captured at 

inVibe is a full-service voice research company that helps healthcare organizations generate meaningful evidence and gain actionable insights by unlocking the signals in the human voice. 

Rare Patient Voice, LLC provides patients and caregivers an opportunity to voice their opinions through surveys and interviews to improve medical products and services. RPV has over 100,000 patients across 485 different conditions. 

Reducing Barriers to Amplify the Patient Voice

Hearing is Believing

Here at inVibe, we tend to obsess about the importance of storytelling, and we’re particularly passionate about making sure patient voices are heard.

As inVibe’s Director of Research Kathryn Ticknor noted in 

PharmaVOICE’s 2nd annual Patient Perspectives edition

, “patient stories may be among the most powerful tools for fostering patient-centricity — as well as the most easily misunderstood. In a world where big data is king, we can be quick to dismiss storytelling under the label of ‘anecdotal evidence’ and ‘small sample sizes.’ Yet, as human beings, we are hardwired to respond to narratives at a deeper emotional level than we ever could to a series of numbers.”

And what exactly is behind that hardwiring? What makes stories so sticky?

The power of storytelling comes from its inherently 

 nature. In healthcare, this is in stark contrast to the 

 dynamics of the intake form, exam room, or satisfaction survey. Storytelling is rooted in a uniquely human format that encourages patients and caregivers who may not be fluent in “medicalese” to participate and share more fully and authentically than they otherwise might.

Stories also stick because our brain is built to learn by 

. Humans process emotional stimuli first and foremost through the auditory system. As a result, we react to the 

 faster and more empathetically than we do to written text. Without these cues, the challenges of decoding and correctly understanding natural language multiply — as do the potential traps for brand decision-making. As researchers and marketers strive to be more patient-centric, it is vitally important to move beyond just hearing 

 it is said. In many ways, listening becomes the “System 1” of emotional analytics — helping us to quickly understand a speaker’s intention, mood, and attitude at a deeply human level. By taking time to listen to patient stories, we move from “anecdotal evidence” to a framework for empathy and understanding.

 to find out how inVibe can help you listen better.

The Power of Patient Storytelling

inVibe fielded a mixed-methods survey asking physicians to answer the same set of three questions.

Two sets of responses were recorded from each physician:

First, open-ended, written responses followed by voice recorded, qualitative responses – allowing for fluid, authentic, and more comprehensive captures of respondents’ attitudes, sentiment, and experiences.

When comparing the written responses to the audio transcripts of the voice responses, we found that Gastroenterologists have a lot more to say about etrolizumab and the treatment of ulcerative colitis (UC), using on average over four times more words per response when speaking than they are when writing.

While Gastroenterologists’ written responses express positivity toward etrolizumab and the general prospect of another biologic option for UC – especially with a novel MOA – their spoken responses reveal a more complex and richly detailed view of their awareness, attitudes toward etrolizumab’s potential role and use, and their own treatment decision- making behaviors.

Insights from the voice recordings include Gastroenterologists’ verbal focus on the benefits of etrolizumab’s “gut action,” some hope for first line biologic use, the attractive convenience of subcutaneous administration, andthat vedolizumab adoption may already influence the perception and future use of etrolizumab.

Q: Have you heard about a treatment in development for UC called etrolizumab? It’s a humanized IgG1 MAb targeting the beta 7 integrin subunit that’s currently in phase 3 trials. What have you heard and what are your overall thoughts on this potential new treatment?

Q: If etrolizumab were available on the market today (with FDA approval), would you prescribe this medication to your patients? Why or why not? And if so, which patient types would be ideal candidates?

Q: What are your overall thoughts on the current treatments available for ulcerative colitis? Which medications do you use and why?

We have shared a teaser of our findings, but what inVibe delivers to its clients is even more informative, interactive, and actionable. Along with a comprehensive strategic report of findings and actionable recommendations, clients also have access to an interactive dashboard, which allows them to listen to the audio responses from each participant, along with AI-powered 

Speech Emotion Recognition (SER) analytics.

 Just as the research method we choose dictates the type of results we receive, the type of data we collect dictates the quality and depth of insights.

Physician's Voices vs Written Responses

The Difference a Voice Makes

 into your 2021 business strategy can have several immediate and long-term benefits for your organization. In addition to providing a more efficient, effective, and nuanced primary market research solution, voice research also delivers some unique advantages in a pandemic-plagued world where conferences, relationships, and partnerships have all gone virtual.


So, for 2021, we offer you three unique ways that voice research can help your organization stay connected and get ahead by listening better.

What if you could hear exactly what your customers are thinking about your clinical data, product efficacy, and support services?

 In today's remote environment, where companies' face-to-face interactions have been severed with the people who matter most to their business, it's become increasingly difficult to understand what's going on inside your customers' heads.

inVibe’s automated voice research platform can help you collect and analyze meaningful customer feedback about your organization’s data and its competitors— while also providing an authentic barometer of how customers think and feel.

In the absence of in-person meetings and conferences, voice can also be the bridge you need to make the most out of your remote collaborations with buyers, physicians, and others within the healthcare ecosystem that fuels your business. Analyzing voice responses from your customers can provide you with actionable and meaningful insights that other data collection methods lack. So if you are looking to gather precise and nuanced responses from the people that matter the most, tap into the voice of health.

The best way to predict the future of your business is to take a proactive tracking approach by listening early and often. inVibe's voice research platform can give you unique insights into 

indicators that affect your products’ development, distribution, and consumption and track changes in emotional cues from customers over time.


Standardized, recurrent voice-tracking can provide your company with multidimensional data insights that explain, predict, and analyze your customers' thoughts, feelings, and behaviors. Early tracking of emotional trends through voice will give your company decision-making confidence while the competition will be second-guessing themselves.

Whether your company is working with internal or external stakeholders, one of the primary benefits of voice research is that it is 

. Voice breaks through the barriers of complex healthcare ecosystems and helps companies extract the most value out of their relationships with customers 

Advocates, patients, physicians, caregivers, sales teams, advisors, employees, and anyone with crucial insights about your company and products can help you supplement your data with their voice feedback, either on-demand or throughout the year. Voice is new, fresh, and human — and it opens the pathways for companies to rapidly and efficiently tap into their internal and external brain trust. When you listen to what makes those close to your company excited or concerned, you position yourself to be a part of their story and learn what needs your organization can meet before anyone else does.

inVibe's voice-powered market research platform helps life science companies capture deep, emotional insights by leveraging linguistic experts and artificial intelligence to measure message resonance, test creative campaign concepts, and map customers' lexicon landscape — helping inform communication at all stages of the product life cycle.

It's time to kick-off 2021. It's time to rethink how we connect with all those that matter to us. 

Meta description test. Enter description here.

Identifying 5 Key Patient Experience Trends for 2021

Why the Patient Voice Should Influence Drug Development — and How It Can

How GPT-3 Unlocks Deeper Listening at inVibe

Transforming the Patient Experience Through the Digitization of Market Research

Listening: The Newest Competitive Research Advantage

Hearing is Believing

The Power of Patient Storytelling

The Difference a Voice Makes

Voice Research

The Year of Voice

Using Speech Emotion Recognition in Market Research (Case Study)

Case Study (Recorded Webcast): “Assessing the Market Opportunity for a New Rare Cancer Treatment by Uncovering Linguistic and Emotional Reactions from Hematologists and Nurses"

Reimagining the In-Person Workshop

Virtual Conferences. Real Insights.

The Value of Longitudinal Voice Tracking Research

The Changing Voice of a Virus: 10-Week Tracking Study (Ongoing)

Voice Analysis is Redefining Market Research

The Voice of Love

Healthcare Customer Research 2020: Getting All Emotional

Introducing: reCon

What makes stories stick?

Meet the inVibe team at Intellus 2019

Using AI to Drive Empathy

Talk vs. Text: The Real Voice

Mental Wrestling: The Power of Talking it Out

Hellooo?!

Listening with Purpose in 2019

When the truth is hard to hear

inVibe Labs: Origin Story

Physicians Say Utilization Management Has Gotten Out of Hand

Using Voice Analytics to Measure Emotion in Ad Concept Testing [Case Study]

Healthcare High Class of 2016 Machine Learning Superlatives

Agile, inVibe & The FDA

Intro to InVibe

/voice

/product

/resources

/use-cases

/company

@social

.css-1tgiq9g{color:#F5F5F4;}.css-1tgiq9g:hover{color:#FFFFFF;cursor:pointer;}Identifying 5 Key Patient Experience Trends for 2021

/voice

/product

/resources

/use-cases

/company

@social

Identifying 5 Key Patient Experience Trends for 2021