Richard Young, vice president of Vault CDMS - Veeva Systems gives EPM some insight how a centralised data strategy can effectively manage clinical data.
Key highlights:
- Sponsors have conducted decentralised trials for over a decade, yet companies are challenged to access a more diverse and comprehensive data set.
- During the pandemic, some pharma companies invested considerably in decentralised trial technologies, only to find that data could not be connected or verified.
- Both unifying data management and recording the patient journey are essential for a clinical data strategy and decentralised trials.
- Instead, most organisations use manual methods to aggregate and clean each data silo individually.
- This problem originates in the earliest days of traditional electronic data capture (EDC), where data sources were fragmented and isolated as we prioritised speed of collection over analysis. New trial designs requiring faster decision making and flexible data management are transforming this approach.
- Aggregating data in a central clinical data management system (CDMS) is crucial to achieving the visibility and timeliness that contribute to more effective trials.
The COVID-19 pandemic brought the industry together in a unique way to address digital trials. It led to much innovation, adaptation, and adoption of decentralised clinical trial models allowing patients to participate without repeated site visits, whilst helping sponsors increase enrolment and prevent attrition.
But the hype around patient centricity and decentralised trials drowns out the unfortunate truth that sponsors have few options to aggregate, review, and clean the huge volume of patient data gathered from disparate sources. This problem is not new as sponsors have conducted decentralised trials for over a decade, yet companies are challenged to access a more diverse and comprehensive data set. Both unifying data management and recording the patient journey are essential for a clinical data strategy and decentralised trials.
It is possible for sites to capture patient data both remotely and in real time, however, sponsors cannot verify and reconcile this data quickly. Instead, most organisations use manual methods to aggregate and clean each data silo individually. Systems and processes have not scaled to handle the proliferation of data in a coherent and efficient manner.
One global sponsor said that, for a single trial, it required 27 people working 24/7 for six weeks. The time and costs to do this for every trial are prohibitive, as most big pharma companies run hundreds of trials each year.
Growth of third-party sources and non-static formats
This problem originates in the earliest days of traditional electronic data capture (EDC), where data sources were fragmented and isolated as we prioritised speed of collection over analysis. New trial designs requiring faster decision making and flexible data management are transforming this approach. Today, a typical phase III trial may use 10 data sources generating an average of 3.6 million data points, three times the level we saw 10 years ago. One study found the cost to support data transfers between systems or companies is $156 million annually.
An integrated platform that connects patients and sites with the sponsor’s infrastructure for one point of cleaning and review would eliminate many of these data driven challenges.
Timing is critical to data-driven decision making
In decentralised trials, the time lag between data collection and the availability of clean data makes it more difficult to make informed decisions. Many new data-collection instruments are stand-alone tools that lack data review capabilities. When data needs to be reviewed, it must be transferred to the sponsor or CRO and imported into a separate system. When data managers identify discrepancies, they must often resort to disconnected and time-consuming email exchanges.
During the pandemic, some pharma companies invested considerably in decentralised trial technologies, only to find that data could not be connected or verified. They waited months to extract, clean, and reconcile it with their EDC data, finding unexpected anomalies. There has been no easy way to query data sources and ensure validity.
Delays viewing the data prevent trial practitioners from being able to assure regulators that this is an accurate account of each patient’s experience or make decisions in a timely manner.
Consider a rare disease trial where each patient’s outcomes are potentially meaningful to the others. If one patient’s diagnostic readings trigger a change in the treatment plan, sponsors should be able to make that adjustment instantly. If there is a delay before the data can be cleaned and checked, it leaves them liable for failing to stop potentially harmful treatments.
Establishing end-to-end data flows will be crucial if decentralised trials — and not just data collection — are to run in real time. Not only will it be key to clinical trial agility and ensuring the validity of results, but it will also be a prerequisite for adaptive trials.
Working toward complete and concurrent data
Company strategies for digital and decentralised trials must incorporate plans to connect the myriad sources of patient data into a single clinical data management system. With decentralised trials, the same data for different patients will be collected in different ways. Aggregating data in a central clinical data management system (CDMS) is crucial to achieving the visibility and timeliness that contribute to more effective trials.
Technology providers are exploring different ways to achieve this connection, but some options scale better than others. One approach is to use a clinical database or data workbench that stores clinical data in one place, allowing it to be cleaned and harmonised.
Addressing the data challenge head-on
Life sciences companies are enthusiastically taking up the data challenge. As sponsors develop data strategies for digital and decentralised trials, they might consider the following:
- Assess the effort required to verify third-party patient data.
- Quantify the costs of data aggregation and cleaning, as well as what it will take to simplify the process. What is the opportunity cost for escalation of missed decision opportunities, caused by delayed data availability?
- Gain commitment from the C-suite to make real investments in clinical data management.
- Compare the cost of data management tools to those of those of trial delay or failure, including regulatory rejection of an application.
Such considerations are key to a successful data strategy for decentralised clinical trials. Taking these factors into account, sponsors can better focus on patient needs and keep trial costs and timing on track.