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The four types of eSource

Written by Jon Carter, Product Manager
23 Dec 2019

eSource means different things to different people. You may have never even heard of it, so this is the first in a series of posts which aim to unlock what eSource is.

When I ask “What does eSource mean to you?”, I receive many different answers, and quite often they weren’t what I had expected.

Perhaps we should start by reminding ourselves of the current method for collecting and managing data in clinical trials. Most trials use EDC (Electronic Data Capture), where data is collected elsewhere, then transcribed into an EDC system. That data could be initially collected on paper source, or electronically into a site’s EHR system.

“Doing eSource” is a mixture of removing the need for paper source, and also utilizing the data that is already collected and stored electronically. This means we can split eSource into four different categories. Over the course of the next few posts, we will look into each of these categories in some detail.

These four categories boil down to:

Direct Data Capture: Where data is entered directly into the eCRF instead of onto paper or into the site’s EHR.

Electronic Health Records: Many sites already collect lots of data in their EHR system, so can we pull this data directly into the eCRF or EDC system rather than through manual transcription.

Devices and Apps: Electronic source data is also collected from eCOA/ePRO, eConsent, wearable sensors etc.

Non-CRF: Data from other sources, such as Central Labs, Imaging, ECG

What I’m trying to convey is that there is not a single eSource type or system. There are vendors who may market their systems as offering eSource capability, but they typically only mean eSource as Direct Data Capture. But Direct Data Capture is not a full eSource solution. For example, when entering a patient’s medical history into the eCRF, it must have been transcribed from source elsewhere. What if we could automate and utilize the electronic source of THAT data as well?

Imagine the benefits of being able to manage all data, from all sources, together in one system? We believe encapsia is the answer.


Last 3 posts

Modern technologies support agile processes for clinical trials 95% of attendees to our webinar this week agreed with the central message that processes and supporting technologies commonly used in clinical trials are outdated, and furthermore they contributed to the huge disruption of clinical trials caused by the COVID-19 pandemic. Systems and methods for data collection, monitoring, review and analysis have evolved in a piecemeal fashion, and whether well suited or not, are normally “bolted together”.
This was one of several deliberately thought-provoking questions raised in our webinar last month, “COVID-19: The catalyst for long overdue change in clinical trial technologies and processes”. In the first session, Gurpal Ahluwalia (Partner at BDO Life Sciences) assessed the disruptive impact of COVID-19 on clinical trials and the reasons why the industry has found itself in such difficulty. Cmed CEO, David Connelly, then shared some further perspective on the shortcomings of existing technologies and processes and suggested these contributed to the degree of disruption caused by the pandemic.
Question: Your webinar presentation earlier this week was quite provocative. Could you tell us a bit more about your background? TCC: I started writing software and building computers from scratch from a very early age. My father and I ran an educational software company when I was a teenager, and then I went on to read Engineering and Computer Science at Oxford, capping this with a DPhil in AI before co-founding Cmed Technology - a lifetime writing, designing and creating enterprise platforms with a specialism and passion for the clinical trial space.