Insights Management: the Next Phase of Clinical R&D Gains
Adopting solutions that provide human-centric knowledge will be the key to increasing the value generated from the pharmaceutical industry’s tech investment
Tony Page | | 5 min read | Technology
The clinical research and development space is no stranger to large-scale tech investment – in 2018, the clinical R&D technology market was worth US$17.5 billion. But by 2022, that figure had more than doubled, with continued growth expected in the years to come. What’s less clear, however, is whether or not this tech investment is having the desired impact. After all, many of the top challenges facing clinical R&D today are similar to those of six years ago.
When issues, such as trial design, site selection, staffing, budgets, and regulatory approval, persist after more than half a decade and billions of dollars in tech investment, it’s a sign that something needs to change. Evidently, current approaches to R&D tech investment are not helping life science companies generate the insights that they need to make informed decisions.
But where and how can that investment improve? And what can organizations do to ensure that clinical teams get the insights they need?
Global R&D Technology Market Size and Forecast ($ Billions)
Workflows and data versus people
The issue is not the amount of money, but how and where it’s being spent. According to Clinical Trials Arena, the most common themes among clinical trial operations industry deals made in 2021 included robotics, data analytics, and digital therapeutics.
Emerging themes in clinical trial operations industry deals. This table highlights the themes within the top 30 in 2021 to have climbed furthest up the ranks compared with 2020
These are all promising areas with significant potential for enhancing the clinical trial and drug development processes. But what’s missing is the human element: the voice of the patient, the voice of the healthcare provider (HCP), and the voice of the customer. The challenges that continue to frustrate clinical trial professionals – lack of personnel, site identification/suitability, ethics approval – are people-centric. To maximize the value of tech investment, to address some of these longstanding issues, and to unlock actionable insights, the clinical R&D space must pivot to a similarly people-centric approach.
Let’s consider some human-centric insights management technologies poised to deliver true return on investment for clinical teams.
There is often a disconnect between the people who life science organizations consider to be experts, customers, and patients, and the real-world makeup of those communities. Through detailed analysis of integrated science and medical data sources, network analytics technology provides a clearer picture of disease communities – helping teams understand the relationships within these complex ecosystems.
By its nature, network analytics is people-centric. It analyzes connections between individuals, how they interact, and how they behave, thus providing clinical teams with the insights they need to make better-informed decisions and improve patient outcomes. Typical applications of network analytics include assessing the size, composition, and disposition of a particular market, creating a heatmap of patient populations or HCPs, and making site selection decisions that lower or eliminate enrollment barriers for underserved communities.
If network analytics helps organizations to understand the composition of a disease community, then social listening brings the voices of patients and HCPs to light – in their own words – enabling clinical teams to better understand their needs, challenges, and frustrations.
Social listening effectively provides a window into the patient experience. There remain numerous barriers to both care and trial participation, including health and literacy challenges, logistical and financial hurdles, historical mistrust, and marginalization. Understanding which challenges are at play among a particular disease community allows clinical teams to address those barriers, and improve access among underserved populations.
By exploring and understanding the challenges that patients are facing, life science teams are better placed to solve them – and make positive changes to remove barriers to care and trial participation.
Asynchronous virtual engagement
Asynchronous virtual engagement is the cornerstone of insights management and represents nothing short of a revolution in terms of patient centricity, removing many of the longstanding barriers to care and trial participation.
Asynchronous virtual engagement enables decentralized clinical trials, where patients can participate from home on their own schedule. According to the FDA, 75 percent of participants in US clinical trials are white, while the National Library of Medicine reports that 80 percent of trials fail to enroll on time. With site selection, access, and logistics proving significant barriers to diversity and inclusion in clinical trials, asynchronous platforms look set to make the trial process more equitable and more representative of the population.
Artificial intelligence and natural language processing
We’ve considered how network analytics can help identify a patient population, and how social listening can bring their voices to light. Technologies such as artificial intelligence (AI) and natural language processing (NLP) can listen to those voices, and help extract the meaning behind what a disease community is talking about.
AI and NLP can parse huge volumes of text to pick out trends and themes. Clinical teams can use these programs to analyze conversations – from 1:1 discussions, asynchronous meetings, on social media platforms, and other interactions between HCPs, patients, or experts – and extract recurring themes in near real-time.These findings are often displayed in a word cloud to provide an instantly-digestible overview of disease community interactions.
Sentiment analysis is another AI application capable of providing powerful human-centric insights. This technology can determine whether individuals and groups feel positive, negative, or neutral about the topics identified through NLP. Such insight helps organizations gain a deeper, richer understanding of their disease communities.
Though some of these concepts may feel new or unfamiliar to some pharma companies, I hope it’s clear that AI can help clinical teams identify new patient populations more quickly, efficiently, and cost-effectively, while improving trial participation, diversity, and inclusion.
If companies choose to invest in human-centric technologies, they should begin to see significant gains in clinical trial efficiency. By investing in a human-centric insights management platform, clinical teams can gather actionable information from their disease communities, make better-informed business decisions, unlock new commercial opportunities, and establish a culture of patient-centricity in everything they do.