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.
Two converging and consolidating trends – the amplification of the digital space (social media, messaging apps, microblogs, short video apps, and Q&A destinations such as Reddit and Quora) and patient-centred transformation – are driving largescale shifts in value creation in the pharmaceutical and biotech industry. As such, going digital and listening to the patient voice have become a part of the overall strategy of many large pharmaceuticals, but the industry can mobilize trust further by sustainably embedding these trends throughout its KOL engagement strategy in particular.
Why does KOL engagement need a rethink or any change of strategy at all? The conventional definition of a KOL envisions a healthcare professional, academic, or corporate leader whose influence stems from the fact that he or she speaks from a raised stand (e.g. through publications in high impact factor journals). But just as marketing departments across the board are adopting more participative approaches, with end user-led involvement and innovation front and center, pharma and biotech companies ought to broaden their vision and bring more patients into their KOL mix. This means reaching out to patients not only at the clinical trial stage but also during the marketing and post-launch phases of product development, and managing the new relationships sustainably rather than as one-off, “pop-up” campaigns.
Engaging patient opinion leaders (POLs) has immense value for reaching targeted therapy markets organically. It helps the universe of patients for whom a new treatment is developed feel spoken for and is both educational and empowering to them, since POLs are seen as experts in the management of a health condition. It is also good for business as it allows the industry´s end users to engage in authentic conversations with peers, exchange disease management tips and emotional support, and discuss the benefits of new medicinal products. This in turn adds a patient perspective to the clinical results-driven conversations propelled by traditional KOLs and helps rally engagement around the quality-of-life impacts of pharma innovation, treatments, and care regimens.
On the other hand, the shift towards digital spaces means that many outreach channels for the patient´s mind have moved online. Thus, beyond recruiting patients as opinion leaders, it is key for pharma and biotech companies to also map those among them who are digitally savvy and have a significant followership and influence on digital platforms. One marker of digital opinion leaders (DOLs) is sharing original content frequently and driving high engagement and response rates around it. Like KOLs, DOLs can be segmented by therapeutic area or country of interest and their online activities can be complemented by in-person events organized by the sponsoring company.
So what are effective strategies to strengthen engagement with both POLs and DOLs? It starts with having a clear view of desired metrics to assess the impact of these new categories of influencers, determining a small group of first contacts, and defining a pathway and milestones to implement the outreach in a manner that can be replicated. The last point is important since many promising POL and DOL initiatives have failed to scale due to their being implemented sporadically or as part of product launch campaigns rather than as part of an enterprise-wide engagement roadmap.
If your company is considering expanding its KOL identification efforts to include POLs and DOLs but is not sure where to start, PeakData can help. Building upon our established methodology for mapping KOLs using dispersed public data and automating bibliographic searches, we are expanding our own arsenal of tools to assist pharma and biotech companies with gaining new opinion leader allies. One such set of tools is our proprietary algorithms, which track activities generated by DOLs across social media and detect spikes of activity around topics of interest such as a health condition or a drug. The information is aggregated across multiple data sources and monitored in a longitudinal manner, such that a complete picture of emerging topics and trends can be observed in real time through a specially designed BI-like web platform.
If you would like to know more about how this methodology can give a competitive edge to your company, do not hesitate to get in touch with us – we are always happy to brainstorm creative solutions with and for our clients.
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.