Technologies that accelerate and improve clinical trials
Artificial intelligence (AI) technology, combined with big data, hold the potential to solve many key clinical trial challenges.
Late phase research, technology solutions and access to RWD are at the forefront of creating efficiencies in all stages of study design and implementation.
In this whitepaper, we discuss our approach to data anonymisation, including risk assessment, dataset validation considerations and a comparison between two processes for data de-identification.Download the whitepaper
As digital health technologies (DHTs) or "wearables" continue to advance, there are key considerations for drug sponsors to consider to ensure that the data generated by DHTs are acceptable to payers.Read the whitepaper
Although mHealth devices and sensors are continuing to evolve, and it is now possible to capture a vast array of physiological data, the operationalization of digital trial is not without challenges. In this whitepaper we discuss a framework for integrating digital health effectively and efficiently:
Watch related webinar recordingRead the whitepaper
Digital health innovations can help people better manage chronic diseases and access healthcare when they need it, improving adherence to medications and preventing complications. Our whitepaper provides an introduction and review of digital health and the current regulatory landscape, with a focus on how various US payers perceive these new innovations. Our original research uncovers how US payer organizations currently evaluate and manage digital therapeutics, and their perspectives for the future.Read the whitepaper
In our latest whitepaper, we follow a theoretical patient through the entire clinical trial journey – from initial contact for an early study through transition to treatment with an approved product. At each stage, we explore how IoMT can increase clinical development programme efficiency by reducing the burden on patients, caregivers, pharma companies and medical device and diagnostic manufacturers.Read the whitepaper
Emerging digital technologies, such as artificial intelligence (AI), robotic process automation (RPA), blockchain and quantum computing, offer significant opportunities to improve R&D productivity.
Infographic: Digital technologies have the potential to reverse declining ROI in pharma R&D (PDF)
Blog: Harnessing Big Data: The raw material of digital transformation
Blog: Top five digital technologies set to transform pharma R&D
Blog: Pharma ROI restoration
Media article: How healthcare can develop through digital innovationRead the whitepaper
In the United States outcomes-based contracting (OBC) as a pricing model has long been proposed as a measure to reward innovation, based on actual performance of treatments and interventions in patient populations. However, the perceived and actual challenges in implementation have prevented many innovative contracts from leaving the drafting table. There are a number of ways AI could help to overcome these challenges.Read the whitepaper
How to develop and deploy novel technologies to reduce patient burden and increase engagement.
Incorporating Digital Health technologies into clinical trial designs has the potential to address many clinical trial challenges, including patient retention and engagement. Furthermore, advancing novel technologies such as AI and machine learning are allowing for richer data generation and collection, driving insights for making better drug and medical device development decisions sooner. In addition to clinical research, Digital Health is increasing the efficacy of therapies in the real world through continuous monitoring, telemedicine and prescription digital therapeutics to help patients better manage their conditions.
Infographic: 5 Steps to successfully incorporate digital health in clinical trials
Blog: Precision medicineRead the whitepaper
New technologies are enhancing the efficiency and scope of clinical trials through:
The surge of digital health technologies modernising clinical research
How patients and developers benefit in a digital health ecosystem
New frontiers for the next generation of clinical trials
The future of automation and digital transformation in late phase research
How the Internet of Medical Things (IoMT) is evolving the role of the patient
mHealth device technology has evolved to the point where it is now possible to collect a vast array of physiological data, sleep and activity data, and use advanced analytics to monitor patients in their own home outside of the hospital environment. However the penetration and use of wearables and devices in the pharmaceutical industry is still limited.
In this article, jointly authored by ICON and Intel, we discuss industry concerns about implementation of this technology in a clinical trial. These concerns focus on a number of key areas: Patient Acceptance, Device Suitability, Data Complexity and Insight Generation, Operationalisation, Privacy and Security Issues, and Regulatory Acceptance.
Key considerations for achieving digital trial successWatch webinar recording now
Disruptive innovation is evolving and presenting real solutions but in order to adapt to the emergence of this innovation, companies will need to be more agile and open to learning and dealing with the impact. The barriers of disruptive innovation are forcing pharmaceutical companies and their partners to reshape how they look at everything they do across the entire spectrum of drug development.
Read the views of three Senior Pharma Executives on how their organisations are approaching innovation.Read the report
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Big Data and AI technologies are complimentary and make possible innovations that are fundamental for transforming clinical trials, such as seamlessly combining phase I and II of clinical trials, developing novel patient-centered endpoints, and collecting and analysing Real World Data.
ICON Insights on AI and clinical trials
Blog: The power of AI to transform clinical trials
Blog: Can AI improve R&D productivity enough to restore ROI to sustainable levels?
Whitepaper: 'The Impact of artificial intelligence on outcomes based contracting'
Blog: Leveraging voice-assistant technology in clinical trials
Blog: The Industry Impact of voice recognition, tech trend of the decade
Late phase research is undergoing rapid transformation due to the impact of healthcare digitalisation and access to Real World Data
With the right technology infrastructure and support, sponsors can more completely leverage RWE across the enterprise for maximum value.
Read our white paper: “On a technology-enabled collision course: clinical research meets clinical practice through Real World Evidence”
RWD such as sleep quality and quantity have clinical relevance in Alzheimer's disease. Review the use of wearables in Alzheimer’s disease to provide objective measures of sleep and activity patterns that are not subject to patient recall bias.
RWD-powered, post-marketing studies require fewer resources and EHRs are an efficient data source to support observational studies. Real World Data from Electronic Health Records can enhance your late phase research studies while decreasing study costs.Read the whitepaper
Cybersecurity vulnerabilities can emerge in any medical device that is or can be connected to another electronic device and/or network, resulting in potential harm to patients or financial loss for providers, posing major challenges for medical device manufacturers.
Checklist: View the cybersecurity checklist to see how well you're prepared.
Blog: How can manufacturers improve responses to medical device cybersecurity vulnerabilities?
Blog: Five cybersecurity threats for medical device manufacturers
Wearable devices and sensors offer great potential in the collection of richer data and insights to enhance our understanding of the effects of treatment. However, implementing wearables and sensors brings new challenges to clinical trial conduct, data management and interpretation.
BYOD promises greater patient-centricity by enabling patients to conduct assessments using the convenience and familiarity of their own hardware devices.
Blockchain technology allows for complete transparency of data, which has immense potential within clinical trials. Blockchain ledgers allow for user confidentiality, so patient privacy can be protected during exchange of data between parties - patient data is the most notable item of transactional nature between networks such as healthcare institutions, patients, and regulators.
Blockchain featured as a disruptive digital technology in our whitepaper 'Digital Disruption in Biopharma' with potential to improve pharma R&D productivity.