Welcome to the Empowered Patient Podcast with Karen Jagoda. This show is a window into the latest innovations in applying generative AI, novel therapeutics and vaccines, and the changing dynamics in the medical and healthcare environment. One focus is on how providers, pharmaceutical companies, and payers are empowering patients. In addition, conversations are often about how providers, care facilities, pharmaceutical companies, and payers are being empowered by technology to improve patient outcomes and reduce friction across the healthcare landscape.
Popular Topics
- Virtual and digital health
- Use of AI, ML, and LLM in healthcare and drug discovery, development, trials
- Value-based healthcare
- Precision and stratified medicine
- Integration of digital technology into existing workflow and procedures
- Next-generation immuno, cell, and gene therapies
- Vaccines
- Biomarkers, sequencing, and imaging
- Rare diseases
- MedTech and medical devices
- Clinical trials
- Addressing Social Determinants of Health
- Treating chronic conditions like obesity and pain
- Clinician and staff burnout
The audience includes life science leaders, researchers, medical professionals, patient advocates, digital health entrepreneurs, patients, caregivers, healthcare solution providers, students, journalists, and investors.
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Sep 12, 2022
Dr. Jon Morrow, Senior VP of Medical Affairs and Informatics, and Daniel Blumenthal, Vice President of Strategy at MDClone, shed light on the meaning of synthetic data for drug discovery and patient care. Using both original data and statistical models of the data, researchers and clinicians can quickly gain insights that can impact patient care, facilitate collaborations and partnerships, and dramatically shorten the time required for product development.
Daniel explains, "Actually, the system is able to produce synthetic data on the fly on demand. It reacts to the end user's request for data. So, if I'm an end user and I want to build a population, I want to look at a population of patients with diabetes and understand the medications they were on and lab tests that were drawn about them. To understand their disease trajectory over time. I'm able to define that, and the way our engine works is it actually can take that original population. But without sharing that population, as John articulated at the beginning, without actually sharing that original population. Just learn the statistics of that population and then build this brand new set of synthetic data. That synthetic data, every time it's generated, is compared to the original."
Jon elaborates, "So rather than exposing my patient's information or using my patient's information with approval, how about if I ask a system like MDClone ADAMS to generate a synthetic dataset that looks statistically exactly like my population of patients with gestational diabetes? And it's pulling the data from the electronic medical record at my hospital. It's running this process behind the firewall of my hospital, but it's providing me with data on a population that doesn't exist but behaves exactly like my patients at the population level."
@MDClone_ #SyntheticData #LifeScience #ResearchData #RareDiseases
