Bridging the Gap: How AI is Leveling the Playing Field Between Large and Small Pharma

In our last blog, we discussed how a recent report by PPD showed growing optimism from within the pharmaceutical industry around our ability to solve core clinical research challenges related to patient recruitment, spurred by the advance of technologies like artificial intelligence (AI). In this blog, we will discuss how these challenges can look very different depending on the size of the research organization, and how technology might help to level the playing field.

The 2024 Sites and Patients Trends Report by PPD highlights the differences between how large, mid-size, and smaller pharma companies address patient recruitment challenges, shedding light on the distinct obstacles faced by sponsors of various sizes. While large pharma grapples with keeping up with technological advancements and addressing patient diversity, smaller companies are often constrained by limited funding and talent shortages. However, emerging AI solutions, such as BEKhealth’s BEKplatform, are poised to change the game with their ability to level the playing field and streamline patient recruitment processes across the board.

Unique Challenges Faced by Large and Small Pharma

According to the PPD report, one of the primary hurdles for large pharmaceutical companies is staying abreast of rapid technological advancements and integrating them effectively into their operations. The complexity and scale of their clinical trials necessitate sophisticated data management and patient recruitment strategies. Additionally, large pharma companies are under increasing pressure to enhance patient diversity in clinical trials, ensuring that their research is representative of broader populations.

In contrast, small and mid-sized pharmaceutical companies often struggle with financial constraints and a lack of skilled personnel. These companies, inherently, have smaller teams. Member of those teams, therefore, typically need to wear many hats. Smaller companies, in general, usually means smaller budgets as well. Funding limitations can keep smaller pharma organizations from investing in new technologies or wide-reaching, multi-strategy recruitment campaigns, all of which can hinder their ability to conduct large-scale clinical trials effectively.

Divergent Strategies for Overcoming Recruitment Challenges

Given these differing challenges, large and small pharma companies employ varied strategies to enhance patient recruitment and trial efficiency. Large pharmaceutical companies, for instance, prioritize building robust relationships with patient advocacy organizations and leveraging remote monitoring technologies. These strategies help them to engage with diverse patient populations and streamline trial logistics. By incorporating patient-centric platforms and applications, they can also improve patient education and retention, making trial participation more convenient and accessible.

On the other hand, small and mid-sized companies focus on adopting less resource-intensive strategies, such as refining their inclusion criteria to broaden the pool of eligible participants and providing logistical support to ease some of the burdens associated with trial participation. This approach enables smaller companies to optimize their limited resources while still striving to meet recruitment goals and maintain high standards of trial integrity.

Can AI be the Equalizer?

AI solutions like the BEKplatform can be instrumental in bridging the gap between large and small pharma by streamlining and enhancing patient recruitment processes. One of the key advantages of AI is its ability to analyze both structured and unstructured patient data rapidly and accurately. By processing data from electronic health records, insurance claims, and even physician notes, AI can identify relevant patient information and create comprehensive profiles that inform recruitment strategies.

For large pharmaceutical companies, AI helps to manage the complexity involved with vast datasets, widely dispersed global patient pools, and large portfolios of concurrent trials. Advanced machine learning algorithms can sift through massive amounts of data, identifying patterns and correlations that might be missed by traditional methods. This capability not only improves the accuracy of patient matching but also ensures that recruitment efforts are more targeted and efficient. AI-driven tools can automate many of the labor-intensive tasks associated with data analysis, freeing up researchers to focus on higher-level strategic planning and patient engagement.

AI and Specific Eligibility Criteria

As noted, smaller sponsors are striving to get more specific with eligibility criteria. They are seeking to create more precise patient profiles that can enhance the relevance and reliability of their research outcomes. AI solutions play an important role in making this strategy work, enabling researchers to significantly reduce the time it takes to qualify patients.

Further, small pharma can use the same tools as their larger competitors to streamline data analysis and patient matching, enhancing their recruitment capabilities without adding unnecessary cost. By analyzing historical recruitment data, along with data from deidentified medical records, and real-world data, AI tools enable these companies to anticipate potential issues and develop proactive measures that mitigate risks and enhance recruitment outcomes.

Conclusion

The patient recruitment challenges faced by large and small pharmaceutical companies, clearly stated in the 2024 Sites and Patients Trend Report from PPD, are significant and varied. However, AI solutions like BEKplatform can help level the playing field, enabling companies of all sizes to enhance their recruitment processes and achieve their research goals. By harnessing the power of AI to analyze complex data, streamline patient matching, and accelerate the qualification process, both large and small pharma can work to get their studies enrolled and on the road to delivering life-changing new therapies.