Stakeholder roles: Most leaders agree that life sciences organizations are best positioned to lead this initiative. However, they should partner closely with technology companies with purposebuilt algorithms as well as connectors (e.g. NGOs) to help with patient recruiting based on their connections and credibility in target communities. Governments and regulators can also play a role by enacting policies that increase the pace of innovation by enforcing transparency and targets for meeting regulatory approval criteria and timing, while providers (and payers to some extent) will need to integrate optimization approaches into existing care pathways and research processes. Public-private matrix for clinical trial optimization F I GUR E 6 Why it matters: Clinical trial participation is a key sectoral challenge in life sciences and medtech. For instance, in the US only about 5% of the population has participated in clinical research.16 Meanwhile, about 80% of clinical trials fail to meet their recruitment targets (and therefore stall or fail).17 At the same time, nearly one in three clinical trial participants drops out of their trials, often due to the hassle and inconvenience associated with their participation. Making progress: Over the past few years, increasing focus has been placed on using AI to improve the rate of clinical trials that lead to an approved drug. Roughly 90% of entities in phase one trials fail.18 Reducing this failure rate by even a little would lead to getting life-saving therapies to patients faster and reducing clinical trial expenses. While many factors affect how much patients pay for therapies, lowering clinical trial costs would potentially lower costs for patients and healthcare systems alike. Diversity of patient representation is also a key issue. For instance, in a recent phase two trial of the Alzheimer’s drug crenezumab, more than 97% of participants were white, and just 2.8% were Hispanic, even though Hispanic people are 1.2 times more likely than others to develop Alzheimer’s, according to the Improving Representation in Clinical Trials and Research report.19 AI can thus also serve as a powerful tool to promote health equity by enabling more inclusive clinical trials in multiple ways, such as by recruiting and retaining more diverse participants through AI-optimized site and investigator selection or even (if needed) by creating carefully designed synthetic data that are more representative to fill in key gaps. Many life sciences leaders cite organizational initiatives they say will help create more streamlined and representative clinical trials –such as hiring more clinical operations teams and team members focused on diversity, equity and inclusion.20 AI can enhance these efforts by: – Designing tailored messaging for the right populations to recruit and retain participants at a higher rate – Connecting doctors and clinics that are more likely to be able to recruit patients quickly and making them aware of current and upcoming clinical trials – Predicting when participants are at risk of dropping out of a trial and suggesting interventions to prevent them from doing so. Note: Connectors defined as NGOs, advocacy and patient groups, distributors, group purchasing organizations, investors and other organizations in healthcare. Use case/ stakeholders Government Connectors Technology Life sciences/ medtech Providers Payers Clinical trials optimization Relatively more supportive role Relatively more central role 1.3 Use case for acceleration #3: clinical trial optimization Scaling Smart Solutions with AI in Health: Unlocking Impact on High-Potential Use Cases 18
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