Interview with Emily Jordan, co-founder and former COO of Ancora.ai

Emily Jordan Ph.D.

Former COO of Ancora.ai

Disclaimer: Emily Jordan was still working at Ancora.ai at the time of the interview

Could you provide an overview of ancora.ai?

I founded Ancora.ai alongside my co-founders with the primary objective of assisting patients and physicians in finding the best-matching clinical trials.

Our motivation stems from our extensive backgrounds in both healthcare and technology, which have allowed us to witness firsthand the impact these trials can have on patients' lives. It became apparent to us that clinical trials were often discovered by chance rather than through a deliberate and informed process, despite their potential to be a determining factor in life-or-death situations for individuals with cancer.

This realisation inspired us to develop Ancora.ai, a comprehensive platform that empowers both patients and physicians by streamlining the process of identifying relevant oncological trials.

Through the blending of healthcare and technology, Ancora.ai aims to ensure that every patient has equitable access to potentially life-saving clinical trial opportunities.

What is your role as COO at Ancora.ai?

As the Chief Operating Officer (COO) of Ancora.ai, my primary responsibility is overseeing the operational aspects of our organisation. This entails setting up offices, establishing efficient workflows, and ensuring smooth day-to-day operations.

Also, I actively engage with patient organisations such as the American Cancer Society to foster collaborations and gather valuable insights to enhance our platform's effectiveness. 

Why is health equity important for your work?

At Ancora.ai, we recognise the importance of health equity in our work. We are deeply committed to ensuring that access to oncological trials is not limited to a privileged few. To achieve this goal, we actively engage in patient-centric initiatives. By collaborating with patient organisations, we strive to raise awareness among patients about the availability and significance of clinical trials as a potential treatment option.

We go a step further by providing support and guidance to help patients navigate the complex process of accessing these trials. Through these efforts, we are dedicated to promoting health equity and ensuring that all individuals, regardless of their background or circumstances, have equal opportunities to benefit from oncological trials.

Why is achieving health equity so challenging?

Achieving health equity poses significant challenges due to several factors. Firstly, there is a need for extensive efforts in raising awareness and educating individuals about the importance of clinical trials as a viable treatment option.

Historically, clinical trials have often been perceived as something reserved for edge cases or experimental situations, rather than an integral part of general healthcare. Shifting this mindset and instilling a broader understanding of the potential benefits of trials is crucial in promoting health equity.

What is the role of AI in overcoming these challenges?

To overcome the challenges associated with achieving health equity in clinical trials, leveraging AI technology can play a pivotal role.

The vast amount of trial data available presents a perfect natural language processing (NLP) problem for AI. With thousands of trials conducted globally, AI can process and analyse this wealth of information efficiently. AI algorithms can extract relevant details from the highly specific text found in trial descriptions, inclusion criteria, and protocols. By understanding and organising this information, AI can facilitate easier access and comprehension for physicians and patients.

Furthermore, AI can bridge the gap between the technical language used in trial documentation and the understanding of laypersons. By translating complex medical jargon into more accessible terms, AI can empower patients to make informed decisions about participating in trials.

Finally, AI can support the matching of the right patient with the right trials at the right time. By leveraging machine learning algorithms, we can identify and recommend the most suitable trials for individual patients based on their specific characteristics.

What is one thing you wish for in the future for your field?

One wish I have relates to the healthcare and policy side of our work, specifically regarding the utilisation of tech tools. It would be beneficial to see entities like the FDA, pharmaceutical companies, and other stakeholders implementing policies and potentially even laws that require subgroup analysis in clinical trials. This would ensure that the benefits of technology tools can be fully harnessed in a way that considers the diverse needs and characteristics of different patient populations.

It would also incentivise a more diverse patient population for trials. Government-sponsored trials have demonstrated a better track record in terms of inclusivity compared to pharmaceutical companies. By altering the incentive system, we could improve the overall effectiveness and applicability of treatments, ensuring that they are suitable for diverse patient populations.