Trial by Data: Standards, AI and New Data

Featuring: Steve Rosenberg of Oracle and Dr. Sam Volchenboum


This week, we welcome Steve Rosenberg to Trial by Data to discuss the ways clinical trials are being improved through standards, tech, and patient involvement.

Steve is senior vice president and general manager of Oracle Health Sciences. Some of Rosenberg’s key contributions to the life sciences and healthcare industry include cloud-based integrated approaches to clinical trial management, new methodologies for patient-reported outcomes, and the introduction of advanced analytics healthcare payers and providers focused on at-risk populations.


Listen and subscribe: Trial by Data, presented by Litmus Health.

You can listen to the full episode, and others, on iTunes and Stitcher. We appreciate your tuning in!


Each week, we like to pull out some of the key themes from the conversation, and provide you with the references associated with each topic. We believe that when it comes to knowledge and conversation, more is more.

Here are the key topics from this week’s episode:

Mobile data standards are still in their infancy. As mobile data becomes more important in pharma and clinical trials at large, organizations are beginning to compete for who gets to define mobile standards. CDISC (Clinical Data Interchange Standards Consortium) wants to take this process a step farther than has been done in the past. Before, submission standards were the focus of definitions. CDISC wants to not just put standards in place for submissions but also have standards that will allow of interoperability between companies and enable the collection of different kinds of data.

Referenced:

The role of AI and ML in clinical trials. AI and ML can help increase the operational effectiveness of clinical trials as they are conducted. For example, these technologies can find patients that match criteria of clinical trial and help analyze safety events. However, they’re still unable to do the statistical analysis or the science, like understanding biomarkers. Advancements in the technology will likely begin in the clinical care setting first and then get adopted by the clinical trial setting later.

Referenced:

Social media as clinical data. Social media algorithms to predict disease and behavioral conditions are still black boxes, but they’re incredibly useful in their execution. However, while this predictive ability is useful for clinical trials, pharma doesn’t necessarily want all this information. Additionally, they’re wary of social media groupthink — where people report symptoms regardless of if they’re on the same research arm or the same drug.

Referenced:

The accuracy of wearables is not the problem. Few wearable devices are ready for primetime clinical trials usage. However, it’s not an accuracy issue. There will never be a device — clinical grade or otherwise — that will be perfect. Instead we need to be able to measure the errors of the devices and model the error, as would be done with any other lab tool.

Referenced:

Getting more people into clinical trials. Clinical trials often have higher satisfaction rates than traditional treatment. This is likely because clinical trials offer lower costs, higher levels of engagement, and better interactions with a physician. For this reason, doctors need to begin discussing traditional treatments and clinical trial participation side-by-side with patients. Distributed clinical trials are also trying to move the needle to more involvement by allowing patients more flexibility.

Referenced:


Trial by Data, presented by Litmus Health, is a podcast exploring the data-driven technologies and strategies shaping the future of clinical trials. We cover the most pressing issues and questions facing researchers and clinicians today, in an ever-changing landscape. Listen in as we interview leaders and innovators in the field who are at the forefront of developing and using these data-driven approaches.