Data Return Series: Installment 1

Patient Perspective and Logistical Challenges

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Introducing our Data Return Series, created by our Chief Strategy Officer Joseph Kim in collaboration with Legacy Health Strategies.

In our 1st installment, learn why returning patient data was so challenging in the past and the impact it can have on patients and trust. Check out the comic to get inside the patient's mind on the importance of data return!

Before we start, let me share a story...(click on the arrow to read the rest of the comic!)

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Every year about 2,000,000 patients finish their part in some kind of clinical research. And every year, pharma companies lament about how the awareness of and trust in clinical research remains low. Yet countless golden opportunities to build trust and awareness through the return of individual data are passed over. Typical reasons include: 

  • There is no guidance – FALSE 
  • Tough to build the rational and business case - FALSE 
  • No good way to return this information responsibly through conventional means – TRUE  
  • No incentive for sites to communicate this back – TRUE 
  • Patients do not want this data – FALSE and FALSE 

In all fairness, for decades this has indeed been difficult to do, mostly due to operational challenges and misaligned incentives. And so let’s dive into those specific barriers and understand why they make returning data to patients so difficult. 

“No good way to return this information responsibly through conventional means” 

Imagine an 8-week trial with a 9-month recruitment cycle time, where the first patient was enrolled in January. The first patient will exit the study in March, with the rest of them exiting on a rolling basis until September, when that last patient finally enrolls. All things being equal, it would be difficult and perhaps irresponsible to deliver data to each participant their data on a rolling basis – the data may not be fully cleaned, it may lead to unblinding, it will create complexities that might interfere with the science, and so on. 

The alternative would be to deliver data to everyone at the same time, once it has been cleaned and the analysis complete. But that could be months after last patient visit. Remember, the first patient has been out of the study for 9 months before the last patient even enters. Let’s be generous and assume the data will be ready for distribution 6 months post. This means the first patient will have been waiting for 17 months! In the meantime, there is radio silence about the study, let alone news about receiving individual data. 

And when the data is finally ready, how then would this be returned to patients? Assuming patients were initially told about data return, what if they change their mind? Do they need to be reconsented? Will they still be living at the same address? Will they still be alive? Will sites stuff envelopes, lick stamps, and mail them to patients? Which brings us to the next barrier: 

“No incentive for sites to communicate this back” 

In simple terms, sites are currently not compensated to do the work of parsing data into individual files and returning them to patients. Additionally, once the last patient is out and the study is closed, sites have long moved on to other studies that demand their attention. While I have heard legends about some clinic staff doing unpaid detective work to connect the dots between patients and their treatment assignments and primary endpoints, Sponsors certainly do not make it easier for them.  

Furthermore, a manual implementation of this capability would introduce a great deal of risk around the potential for an inadvertent mix up of results and patients or revealing medical information without the necessary context. While the probability of this risk might be low, the impact would be severe. Which leads us to one challenge left out of this installment, as it deserves a much more thorough dissection unto itself.  Tune in for installment 2 of this series for a comprehensive overview of the ethical principles governing responsible return of individual patient data.