Using AI and Real-World Evidence to Take the “Trial and Error” out of Prescribing

Advances in computer science have enabled significant innovations in almost every sector, from smart transportation tackling gridlock traffic and parking, to FinTech making financial services more easy and accessible. Nevertheless, as of today in the healthcare sector, trial and error prescribing is still common clinical practice. The advent of cloud computing solutions, artificial intelligence and next generation sequencing, alongside the explosion in real-world data, enables us to take the “trial and error” out of prescribing.

Using AI we can now collect, analyze and leverage previously unavailable genomic, clinical and environmental big data to significantly improve clinical outcomes. We can drastically reduce the cost of treatment as patients find the best treatment earlier, as well as lower the risk of treatment failure reducing serious negative side effects like suicide. Furthermore, we can now analyze and use data generated during the real-world trial and error process, to further personalize and support doctors’ clinical decisions. By better understanding patients’ genetic makeup, health record and their real-world trial experience, we can empower doctors to make more informed therapy choices. In this presentation, I will provide an overview of the issues and approaches used today, with an introduction into how Taliaz is tackling this problem to deliver the promise of precision medicine.