Why data is important
There are 2 reasons:
- Determining the results of your clinical trials depends on the data
- Governance of the clinical trial depends on the data – but not necessarily the same data.
Data-driven management is an approach that values decisions based on verifiable empirical data. The data-driven approach is crucial to success of modern, regulated business operations as the pace of competition and regulation steps up for the entire clinical research industry.
Data-driven decision making is a way of gaining a speed advantage but this hinges on the quality and timeliness of the data you collect. Data regarding patient compliance is useless if you get it a week late. You need the compliance metrics now.
There are 2 crucial data disconnects in clinical trials today
Disconnect #1 – The disconnect between the study protocol and the CRF/ECRF
This is the literal elephant in the room. Consider two PDF documents with no structural connection, no enforcement of common terminology (except the talent of the people who write the document) and most importantly no mechanism inside the EDC system for enforcing the protocol conduct by the sites. Since the two documents are unstructured text, they are not machine-readable and although rules from the protocol may be translated in a limited fashion into data edit checks – these do not reflect in a clear and direct way – the protocol itself and the means for enforcing the protocol.
This is relatively easy to fix – reconcile the 2 documents before building the EDC. This way you can be sure that the data model and study schedule in the EDC align with the protocol and the CRF.
Disconnect #2 – The patient compliance disconnect between site, CRO and sponsor
Site visits by study monitors are few and far between. A sponsor depends on study monitors and weekly reports that are produced in Excel format sent by email to the sponsor by the data management team. These reports do not always present patient compliance metrics.
The reason for this is that clinical trials, like most life science models are highly-dimensional – a typical trial with connected medical devices will collect 300 to 1000 dimensions of unique data items for a subject.
When placed on a time line – patient compliance over time is hard for a study monitor to track as it requires data preparation, reduction and computation.
Patient compliance assurance is harder to fix. We’ll be talking about it a bit later in this series of articles.
