THOUGHT LEADERSHIP

I was in New York City on March 1, 2020 when the news reported that the first positive case of COVID-19 had been identified in that state. A pharmaceutical company seeking accelerated approval for one of their products had selected PROMETRIKA to re-monitor their study’s efficacy data after a recommendation from the European Medicines Agency. I ended up traveling to NYC on short notice after learning about an urgent need for a monitoring visit at one of the oldest and largest teaching hospitals in the United States. While leading a two-day monitoring visit at that hospital, it was announced that a coronavirus patient had been admitted to their emergency room for the first time. The research staff in my vicinity were anxious about this invisible, contagious, mystery virus, and it had just been confirmed that their colleagues were interacting with a patient that had been exposed to it. I walked back to the hotel that night and didn’t think anything of it when the song, “The Only Living Boy in New York” came up at random on my playlist.

So, your organization has made the decision to transition to a new eTMF system. Congratulations on taking this step! Depending on your organization and your role, you may or may not have been part of the decision-making process for choosing the system. You may be somewhat familiar with the system or encountering it for the first time. Either way, now that the ink on the contract is dry, it is time to begin making the most of your new eTMF.

Buzzwords like “analytics” and “visualization” have been used in the clinical trial space since the ability to collect massive quantities of clinical trial data became a reality. Everyone wants the instant gratification of real-time trend detection, and software developers have responded in kind by providing a dizzying array of analytics options. How do you make an informed decision about which option is the right fit?

Having worked in the world of clinical research for 15 years, I can’t help but be baffled by the rampant misuse of the word “de-identified” as it relates to the classification of clinical data sets.  In the United States, “the HIPAA Privacy Rule provides federal protections for personal health information [PHI] held by covered entities and gives patients an array of rights with respect to that information.”  De-identified health information is not PHI and thus is not protected by HIPAA.

It’s a common misconception that if you remove patients’ names from a set of clinical data, then it becomes de-identified and is no longer governed by HIPAA.  However, to make a set of clinical data truly de-identified, you must remove much more than just the patients’ names. 

Home visits as part of clinical research have accelerated in recent years as we strive to find the right balance between facilitating study participation while accommodating participant’s busy everyday lives. Clinical trials in rare diseases are even more challenging than trials in other diseases due to a number of factors:

• Small number of eligible trial participants
• Complicated by heterogeneity among rare disease patients
• Most have no cure and manifest at a young age
• Less than 10% of rare diseases have a specific treatment
• Many have other debilitating conditions / physical limitations making it difficult to attend frequent study visits

The combination of home study visits and the right technology removes barriers to optimal patient recruitment, compliance and retention.