The role of AI in the biopharmaceutical sector to date has focused mostly on optimising and accelerating R&D processes and on innovating clinical trials. Those activities belong to the pre-commercialisation stage of bringing a new therapy to market, and have historically absorbed the lion’s share of investment and attention in the industry. But with companies increasingly looking to AI to also support their commercialisation efforts, a new horizon is sliding into view: realising the technology’s potential to revolutionise biopharma’s marketing and sales processes.
Taking an asset to market is the culmination of what is usually a multi-year and multi-billion dollar/euro investment. The granting of a marketing authorisation, however, is not an assurance of success and often many challenges must be addressed to achieve a successful launch. One key factor that is critical to the success of a new medicine is having robust data and insights on which to base your launch strategy and make investment decisions. This could be information about unmet patient needs, treatment paradigms, prescription trends, influence maps in companies’ top accounts, share of scientific communication by segment, KOLs, digital opinion leaders (DOLs), and healthcare providers. In short, access to data is an opportunity for companies to gain competitive advantage before, during, and after product launch, and if there is one characteristic that neatly sums up AI capabilities, it is “expresswaying” access to and interpretation of large volumes of data.
The implication for the industry? Thanks to AI-enabled data analytics, medical affairs, sales, and marketing teams can gain deep commercial insights that not only measure the impact of existing market access and adoption strategies, but also serve as a guide on how to improve them by collecting, interlinking, and pinpointing the most relevant data and messaging approaches for each target stakeholder group. In particular, by gaining a deeper understanding of their KOLs and relationship networks in real time, medical affairs teams can craft highly tailored scientific communications, and by harnessing the power and speed of AI in the pre-launch phase, improve their understanding of a new therapy area at scale very quickly.
One of the most intuitive applications of AI in drug commercialisation is enabling high-precision profiling of HCPs, so that marketers can design customised outreach strategies instead of relying on a traditional one-size-fits-almost-all approach. AI technologies make this possible by micro-segmenting key stakeholder groups not only across therapy area, publishing output, or visibility – the traditional approach taken in audience segmentation. They also do so according to size of individual physicians’ professional networks and the degree centrality they occupy in them, communications preferences, availability, and location. Further, AI can search beyond companies’ existing customer data sets and unearth new cohorts of practitioners who may be open to considering novel treatments for some of their patients. The results can be applied to optimise engagement strategies, connect with would-be partners via their preferred communication channels, and ultimately increase market adoption rates.
In the same way that AI technologies can tap into public online databases and extract KOL and HCP-level data at a fraction of the time it would take a person to do it, they can garner patient-level data. For example, an AI algorithm trained to process natural language in the medical domain can harness data found in public forums or social media channels where patients share personal experiences of using a particular therapy or a drug. Those comments can then be cross-linked with physician-level observations shared in a similar social context, yielding multi-perspective insights from openly shared real-world evidence (RWE). Having such data enriches pharma companies’ understanding and monitoring of product performance across indication, region, physician specialty, type of patient (e.g. GP practice, hospital, specialty clinic), or type of payer (public vs. private). Lastly, AI-enabled social listening can also be leveraged to detect patterns in prescribing behaviour and to assess prevailing public sentiment towards new or existing medications, which are essential tools for running brand diagnostics.
On the regulatory and compliance front, AI can accelerate drug commercialisation by reducing the time and human resources that are invested into compliance review, allowing the latter to be shifted towards more demanding cognitive tasks that cannot be automated. AI-powered analyses can also help leaders decide how to deploy sales teams across a geographic region or therapy area for greatest return on investment, using insights from the micro-segmentation and social listening applications described above to determine what type of content and communications channel would be most impactful for each.
Last but not least, data-driven AI technologies can support drug commercialisation in the post-marketing or pharmacovigilance stage by compiling publicly shared data on undesirable side effects in real time. This can help companies conduct causality assessment on an ongoing basis and react faster to minimise damage to both patients’ health and the health of their own brand image.
Adoption challenges in Europe
Despite the competitive edge promised by embedding AI and machine learning-powered technologies into pharma´s commercial operations, the EU biopharma industry has been slow to adopt such technologies , according to a report by IQVIA published in 2018. Although the survey cited dates back to 2018 and company readiness to embrace AI has likely improved since then, it is an “open secret” in the industry that in comparison to the U.S. and some Asian countries, Europe is not moving as fast as it should and could.
How PeakData can help
PeakData’s Healthscape software can support organisations that are ready to deploy AI for drug commercialisation. It comprises a set of tools that perform real-time web wide market intelligence based on publicly available, decision-shaping conversations about drug uses and indications, real-world outcomes, and patient and provider-generated insights across social media, peer-reviewed publications, preprint repositories, press releases, conference agendas, and other formal and informal sources. This comprehensive search is complemented by rigorous and nuanced analysis of the findings, according to client-defined variables and characteristics, to obtain a panoramic view of all the open data and social signals generated around a drug that is about to go to market or is already being commercialised. As an added value, Healthscape’s capabilities are governed by a clear strategy for sourcing the data such complex AI searches require.
If you are interested in learning more about Healthscape and how it can make AI a reality in your company´s marketing and sales strategies, we invite you to schedule a demo.
Influencer ecosystems are continually evolving in response to changing consumer preferences and the pharmaceutical sector is no exception. On one hand, patients and their doctors are looking beyond traditional Key Opinion Leaders (KOLs) for guidance on therapies and treatments and want to hear from alternative sources, such as Patient Opinion Leaders (POLs), whose feedback resonates on both personal and real-world evidence level. On the other hand, as a growing number of people seek information online, they discover a parallel universe of Digital Opinion Leaders (DOLs), who tend to have a large followership and are more approachable. We wrote about this expanding universe of Opinion Leaders (OLs) in a previous blog.
Despite these shifts, many pharma companies cling to the idea that KOLs represent the best, safest, most authentic marketing partners. Yet as audiences are increasingly more diverse, better informed, and often skeptical of authority figures who speak only to their “filter bubble” of similarly credentialed physicians, KOLs are really just one of several types of sources healthcare professionals look to for advice and information.
Not only that, but the term “KOL” itself has begun to lose its luster. As far back as 2015, 62% of medical professionals and 56% of pharmaceutical executives believed that it should be replaced, according to a survey of nearly 400 respondents from both Europe and the U.S. On the physician side, many respondents felt that it is often used too loosely or attached to people who do not warrant that title or the trustworthiness implied by it. On the pharma side, many others signaled that their companies were phasing out use of the term because of its unflattering connotations and it being seen as “marketing speak.” It is reasonable to assume that in the five years since that survey was conducted, the “KOL” terminology has hardly gained back much support.
In the context of this less-than-enthusiastic adoption of the “KOL” concept, it is obvious why alternatives such as DOLs and POLs have been gaining currency. But what makes the most novel among them – Connected Opinion Leaders (COLs) – stand apart from the rest and what is their value to pharma companies?
Although COLs´ profiles seem similar to others, their unique feature is that they bring together the best of the KOL, DOL, and POL worlds. They are not only digitally savvy, relatable, and with an active presence across multiple content channels, but are also sought-after experts, published authors, and conference speakers. And it is precisely their panoramic view of the industry from both a professional and a communications perspective make them a strategic partner to pharma marketers who want to deliver sleek messaging to an engaged professional network.
To build a relationship with COLs, the industry must expand the prism through which it searches for them. Instead of relying only on traditional metrics, such as impact factor of journals they publish in or conferences they present at, it should seek to glean insights on factors such as who else is in their networks or what publicly communicated research projects they participate in. Pharma marketers should then triage those traditional metrics with social stats such as the frequency of their online activity, number of followers, geographic reach, likes, and reposts/retweets, to name a few. Once identified, to then engage them in a sustainable, authentic partnership, pharma marketers should consider offering them membership on advisory boards, consultancies, or content creation on blogs or proprietary websites, where their expertise, communication and social skills can truly shine. At PeakData, our software platform HealthScape™ can help your company develop an effective strategy for identifying COLs with the highest potential and relevancy for your business. For example, our algorithms and comparison tools let you sift through data on healthcare professionals labeled according to multiple features, select the ones that meet your essential criteria in terms of therapeutic area or publications, and then triage the results against relevant social metrics to understand each person´s reach and digital footprint. Give us a chance to show you a new, smarter way of choosing who you entrust with your marketing message – we are sure our solution will surprise, inspire, and delight you.
The Covid-19 pandemic has accentuated and accelerated important changes to how healthcare providers (HCPs) keep abreast of new product information relevant to their specialties and the patients they treat. Most notable among those is the HCPs’ embrace of digital technologies and digital-first channels of delivering content. This trend is accompanied by a growing preference for engaging with Medical Science Liaisons (MSLs), who bring technical expertise and rigour to their sales pitches, over traditional medical representatives. Those shifts have a direct impact on how pharmaceutical marketing and sales teams must reorganize their work and the tools they use to communicate with HCPs, such that Sales may convert leads to prescribers.
Underpinning the drive toward digital is a set of challenges that providers have been facing for years. Namely, high administrative burden, paucity of time for learning and training, a need to ”squeeze in” activities not related to direct patient care during off-hours and weekends, and a quest for immediate trustworthy responses to product-related questions.
In the context of these challenges, traditional sales rep strategies have begun to lose steam as a growing number of physicians have discovered the convenience of digital platforms. For example, HCPs are able to get many of the benefits of face-to-face detailing by attending training webinars and accessing scientific publications online – that is, without the need to schedule a meeting with a sales rep. Indeed, a survey of over 1500 physicians in Europe carried out by global research company Bryter showed that only 44% of HCPs were rep-accessible in 2016 compared to 80% in 2008.
So how is the industry evolving to cater to this transformation? The sobering reality for pharma companies´ European operations is that uptake of digital marketing on the continent is lagging, with only 11% of players reorienting their strategy compared to 21% in the U.S. and 47% in Japan, according to the Bryter survey. Similarly, deploying a hybrid approach that combines face-to-face meetings with online channels accounts for only 6% of contacts between HCPs and sales reps in Europe (although, to be fair, at 8% the U.S. is not doing much better). Altogether, 48% of surveyed physicians reported a “monochannel” experience.
To stay relevant, the pharma industry should take decisive steps to improve its digital footprint in dealing with HCPs. To do so, it only has to follow the guideposts helpfully left by market researchers: almost a third of survey respondents (32%) indicated that digital platforms make information easily shareable with colleagues, while 37% said digitally delivered promotional material fits in better with their schedule. If companies succeed in delivering their marketing pitches in a way that meets those preferences, they will have gone a long way toward repositioning their brands as trustworthy partners to physicians.
With Covid-19 having put the brakes on off-line marketing, the current moment presents a perfect opportunity for the industry to pivot to a more modern and flexible approach to HCP engagement. The type of pharma-proprietary content that physicians seem increasingly drawn to includes online training and education on drug safety, efficacy, and real-world data, so this is one sure path companies can take right away to get started on their digital marketing transformation journey.
In order to be successful – even comfortable – in their new marketing skin, however, it is essential for organizations to gain a 360° view of their customers. This is where PeakData’s Healthscape suite of services can help by partnering with your marketing or business development teams to understand their pain points in engaging HCPs and suggest data-driven improvements. Our deep expertise in untapping real-time insights from publicly available data on physician affiliations and appearances, social listening analytics, and publishing choices and collaborations can be your ticket to aligning your available resources with the demands of the digital communication era. So send us a message or give us a call and let´s explore how far you want your company to go – here at PeakData we´ll be sure to have just the right mix of research capabilities to help it get there.
Finding the right number of patients with the right characteristics is one of the biggest challenges for clinical trial sponsors. In large part this is due to the fact that randomised clinical trials – the gold standard for conducting clinical research – come with rigid inclusion and exclusion criteria, which render many potential participants ineligible while placing undue burden on those who do qualify. Luckily, clinical trial methodology has evolved since the 20th century and even since a few years ago. Today trials are much more granular, which makes them particularly well-suited for looking into rare diseases and for engaging with hard-to-find, hard-to-enroll, and hard-to-retain patients, such as those living with Alzheimer´s disease, rare cancers, and other rare diseases.
The main paradigm change that enables such granularity is the shift from traditional RCT approaches, which require patients to physically check in at clinical sites, to virtual designs that are better suited to the era of telemedicine, Big Data, and patient-centred healthcare. Two examples of siteless, decentralised trials – which still have to be deployed under the supervision of principal investigators – are home-based RCTs and in silico simulated RCTs.
Home-based RCTs enable patients to report their clinical evolution telematically or by relying on a home health nurse who administers tests and treatments. This type of RCTs are ideal for improving the patient experience, which is key to enrolling and retaining participants. They minimize inconvenience for people with mobility issues by reducing commuting time and effort, and often provide real-time insights that can serve as a behavioural nudge to improve treatment adherence. They also benefit sponsors by increasing the probability of recruiting the needed number of participants with the needed characteristics and of obtaining a more representative sample, since the remote design of the trial reduces geographic and administrative barriers across countries.
In silico simulated RCTs, on the other hand, have no human participants involved. To run them, researchers use historical data from real-life patients to model different scenarios and “health personas” and infer levels of drug efficacy. The use of retrospective medical and clinical data enables the identification of inclusion and exclusion criteria and the enrolment of virtual patients, modelled after real patients´ data footprint. This type of trial design is particularly relevant for drug development in rare diseases, where potential study cohorts and target markets are naturally very small. Thus, in silico RCTs benefit pharmaceutical and biotech companies mainly by reducing the time and costs of R&D.
The key advantage of these emerging trial approaches is that since they are managed remotely or through the cloud, the protocols can be tweaked as more data come in and shed light on the behaviour of the tested drug or intervention in patients. This flexibility, which translates into smarter patient enrolment and lower costs, makes them increasingly attractive to the industry; however, implementing them in practice is still a challenge.
PeakData´s strategic focus on helping companies identify KOLs through both traditional and novel approaches makes us uniquely equipped to help pharma and biotech innovators pivot to decentralised clinical trials. One way we can do so is by leveraging our data collection and analysis tools, used to untap KOL insights that escape the “eye” of conventional searches, to help develop criteria for successful trial design and protocol from the perspective of opinion leaders. We can also use our existing resources and approaches to beta-test the effectiveness of trial design prototypes by measuring the opinions of key stakeholder groups. To learn how a partnership with PeakData can add value to your company´s clinical trial operations, contact us and let´s brainstorm together.
Tried-and-true methods of identifying and managing KOLs do not always yield optimal results. They tend to lean toward high-profile experts who are “top of mind” for the professionals entrusted by pharma and biotech companies with spotting and engaging them, and the hidden risk in this is that some of those prominent KOLs are in fact LOLs – “loud” but unimpactful opinion leaders in the clinical research ecosystem who produce more noise than signal.
The traditional approaches to KOL identification, which rely primarily on observation, surveys methods, and literature searches based on standard publishing metrics, suffer from observer and response biases and are far from unearthing the most effective potential collaborators, while at the same time overlooking social information that could turn up powerful insights. Because they tend to prize quantity – in the form of peer-reviewed journal articles, conference appearances, clinical trials management, advisory board participation, or other quantifiable scientific activity – over quality, they risk failing to distinguish between purely prolific producers of output and truly influential advocates whose work has meaningful repercussions beyond publications and the echo chamber of conference halls.
Indeed, research has shown that relying on bibliographic searches or considering only the number of publications or conferences attended as indicators of thought leadership fails to spot more than 50% of influential actors compared to social network analysis (SNA) – and this is research dating back to 2010, when SNA did not have the capabilities it has today. (The study, which questioned the dominant KOL identification methodology even then, can be consulted here.)
The “secret” to identifying KOLs using SNA is that this approach, which makes explicit the webs of relationships between thought leaders and derives insights from their centrality and connectedness to others in the network, allows for selection of criteria that are not easily mappable using traditional methods. Such criteria may include identifying KOLs with more recent publications rather than with a greater number of publications, with recency being assigned higher value than sheer volume, thus potentially reducing the impact of age or career length on KOL selection; the frequency of collaboration within a network of experts, which can be interpreted as evidence of authors´ preferred working relationships; or the social reverberation of publications, keynotes or other appearances related to a given therapeutic area, measured as the sum of substantive comments and engagements on social media.
An example of a KOL identified through SNA who may not necessarily have been captured using a literature search would be an author of a publication that is well received on social media and commented by Twitter or LinkedIn members as having an impact on clinical practice or real-world outcomes research. By contrast, an example of a LOL identified through conventional bibliographic search would be a highly visible, prolifically published expert whose work falls short of meaningfully impacting clinical practice, physicians´ prescribing practices, or community conversations.
Of course, using SNA for KOL identification should be done discerningly because it has its own dark side: the presence of hyperactive social media commentators and self-proclaimed experts who “talk the talk” and have all the bearings of – and perhaps even the networks typically associated with – KOLs, but whose engagement with and impact on others remains at the level of metaphorical back-patting without translating into concrete actions (e.g. evangelizing new research ideas, driving market adoption of a new drug or treatment).
In the era of AI and Big Data, it is paramount for pharmaceutical and biotech companies of all sizes to get smarter about their KOL strategies and upgrade their tactics. If you have already leveraged SNA in your practice or are considering doing so after reading this blog, we invite you to share your experience in the comments or contact us directly with feedback. Here at PeakData we are always interested in learning how we can best help our clients and are constantly tweaking, fine-tuning, and adjusting our methodologies so we can be of service to you.
Artificial Intelligence and machine learning are making ever more sophisticated forays into healthcare and pharmaceutical R&D through applications ranging from processing medical images, unstructured EHR notes, and biosensor-equipped patient wearables to pattern-finding in synthetic control arms of clinical trials. However, beyond their usefulness in such controlled environments, little is known about how AI-powered data analytics can support pharmaceutical companies and biotech startups in their market access and commercialization efforts.
One scenario where AI´s role in pharmaceutical innovation is still poorly understood and under-utilized is in identifying global key opinion leaders (KOLs) such as physicians, hospital pharmacists, community healthcare providers, and others on the frontlines of patient care. These professionals are crucial in accelerating adoption of new drugs and treatments in local markets, as they can impact the presentation of research in professional forums, serve as phase III clinical trial investigators, or participate in the collection of real-world data through post-marketing surveillance.
Traditionally, addressing this need has been challenging for medical affairs managers, because identifying KOLs in an ocean of unstructured data floating in different formats and languages on the internet is a Herculean task. The conventional way to approach it is through consultancies that manually reach out and conduct interviews with potential leads – a high-touch, time-consuming process whose output is often limited when compared with the resources expended and the client´s outreach ambitions. The conventional way to approach this is by having medical science liaisons or other members of the medical affairs team identify, reach out to, and network with KOLs in an effort to understand how a drug in development compares with current treatments they are using with patients or how it may be improved to make a difference and create value.
AI offers a more efficient and less personal relationship-dependent approach to identifying KOL’s: by leveraging advanced capabilities, AI professionals can develop algorithms that comb through structured and unstructured data across the web and extract relevant information from millions of websites. Mapping potential KOLs in this way has less human bias because it is agnostic to consultants’ top-of-mind roster of contacts, casts a wider net than website rankings in search results can ever do, and is scalable to a degree that is simply not feasible when using human labour.
The “magic” making this novel approach possible is recent advancements in natural language processing (NLP). NLP is a well-established field of machine learning, which has experienced vast development of late. Its functionalities allow data technologists to acquire high-resolution, per-client data by creating scripts that match predefined criteria against publicly available information. In the context of identifying KOLs for , these criteria may include target country, practice area, specialty, services offered, level of care, and even academic affiliation. Whenever those criteria are reflected on the websites or other online materials of potential partners, the algorithm will detect it, extract the relevant details, and compile them into a client-specific database sorted by degree of relevance to the expressed criteria.
Another advantage of using AI to get a comprehensive list of KOLs who can provide guidance in the late stages of pharma and biotech companies’ R&D journey is the capability to capture up-to-date contact data and replace previous versions with it. This is important when considering that KOLs who are often most worth engaging with are the ones who ascend quickly in their careers and frequently move between institutions, change affiliations, participate in multiple projects, and give keynote addresses in locations around the world. As such, their contact details may overlap or become quickly outdated, which makes them harder to identify through conventional country- or institution-based search criteria.
NLP-powered algorithms can make sense of the labyrinth of publicly available KOL details on the web and convert them into an organized, updated resource thanks to their capability to monitor petabytes of messy web data in real time. These “super powers” allow software engineers to design scripts that send out alerts as information corresponding to clients´ KOL search criteria changes, assuring that medical affairs professionals are kept abreast of key details important to their outreach efforts.
The combined challenge of manually identifying a critical mass of KOLs and accounting for unpredictable changes in their contact details is compounded further by the different languages of the web domains where these data often reside. For marketers wanting to reach local KOLs and prescribers this can be yet another hindrance since deciding whether to engage with a particular person can depend on the context in which they identify themselves online. Multilingual NLP coupled with real-time data capture and updating that can dynamically convert to English (or another preferred language) the relevant information means the AI toolbox has a solution for this too.
Sustaining market adoption once a new drug or treatment has been approved by regulators means companies remain invested in maintaining relationships with KOLs who can speak to the effectiveness and value of their products throughout their life cycle. This is especially relevant in cases where the comparative effectiveness of therapeutic innovations – which takes into account subgroups of patients who benefit differentially from treatment with the same drug – surpasses that of established products, since physicians and other patient-facing professionals are in the best position to attest to the impact of novel drugs to heterogeneous patient populations.
For this reason, just as important as identifying currently recognized professionals is creating channels to map and connect with up-and-coming KOLs or “rising stars.” One way AI can help in this area is by automating client searches whose criteria may include progressively higher publishing frequency in scientific journals compared to the length of a young KOL´s career, the type of journals in which such publications appear, grants received, simultaneous or evolving expertise in more than one field, speaking engagements, and consulting gigs.
Regardless of the stage at which companies find themselves in their medical affairs endeavors, the opportunities afforded by proprietary AI and machine learning algorithms to dive into the ocean of publicly available big data on the web and come out with pearls of information tailored to the most demanding search criteria will increasingly mark the difference in outcomes of KOL identification and retention efforts. The sooner pharmaceutical and biotech organizations incorporate this approach into their existing strategies, the more rewarding their global and local network-building will be.