Jens Dommel
Head of Public Sector Healthcare, EMEA, Amazon Web Services
The cloud holds the potential to help hospitals, researchers and doctors to deliver clinical trials that scale and improve patient experience.
The cloud has the potential to help life sciences organisations scale, improve the clinical trial process and offer a more patient-centric experience. Modernised clinical trials allow providers to focus on their role in discovering new treatments and therapies and improve patient outcomes.
Jens Dommel, Head of Public Sector Healthcare, AWS (EMEA) sees patients, doctors and healthcare providers benefiting by having access to more enriched data sets and the ability to see health information that may previously have been unstructured or siloed.
Modernising clinical trials
Quality, structured data is a critical component to the success of clinical trials. Hospitals and providers face challenges in running clinical trials in terms of timescale, cost, enrolment of patients and low patient engagement, as well as managing stakeholders and sharing and analysing the collected data.
Existing data may be unstructured, siloed, kept in paper format or even outdated. This can lead to challenges with steps critical to clinical trial success, like checking patient consent, identifying cohorts and determining the best trial sites for patients.
According to a survey by the Biopharmaceutical Investment & Competitiveness (BCI), execution of clinical trials is historically the most expensive part of the process, accounting for around two-thirds of all R&D costs. Clinical trial protocol design in its current state, explains Dommel “requires a great deal of administrative work.”
Patient engagement
Once a trial is underway, sponsors need agile tools to incorporate the use of mobile technologies, such as wearables and mobile devices, which could generate a wealth of data while promoting better patient engagement and streamlining report generation.
AWS IoT services connect devices to the cloud for secure data collection, management and analysis. Devices can stream data to cloud environments and enable remote communication, reducing the need for patients to visit study sites and decreasing both the cost of clinical trials and the burdens they place on participants.
AI/ML technology can help optimise patient recruitment, predict feasibility of clinical trials and recruitment timelines.
UK-based uMotif has taken this patient-centric approach to capturing clinical trial data, using AWS services to build an easy-to-use cloud-based platform that can be deployed anywhere, and scale when traffic increases. The eClinical platform supports data capture for clinical research and uMotif applications are used to track and submit e-consent, symptom and wearable device data.
“A patient-centric approach has the potential to benefit those who need it most – the patients – particularly in cases of rare diseases, where trials can provide hope with the shared goal of finding treatments and therapies that may provide relief,” says Dommel.
New dimension
He explains that AWS adds a new dimension to how providers collect and store data, and how that data is used for clinical trial cohort selection. “The cloud can help to build data lakes and comprehensive data models that make cohort selection easier and more accurate,” he says.
For example, German EDC software provider, Climedo uses AWS to create secure, cloud-native, scalable solutions to better capture and manage clinical data in a decentralized, patient-centric way. The software is used by pharmaceutical companies, medical device manufacturers, hospitals and public health offices.
Dommel points to artificial intelligence/machine learning technologies and their role in modernising clinical trials. Services like Amazon SageMaker, a fully managed machine learning service, can optimise studies by predicting the most productive trial protocols and study locations. Amazon Comprehend Medical Service can help providers quickly find appropriate patients to enrol for clinical trials by extracting information from medical text, without needing previous experience in machine learning.
“AI/ML technology can help optimise patient recruitment, predict feasibility of clinical trials and recruitment timelines,” adds Dommel.
Scalable solutions
Looking ahead, Dommel says: “The future of clinical trials will require scalable, secure, connected and compliant services. The ability to collect, analyse and secure clinical trial data will be critical in achieving positive clinical trial outcomes that may form the basis for new lifechanging drugs, therapies and vaccines.”
