Technologies that accelerate and improve clinical trials
The power of AI to transform clinical trials
Artificial intelligence (AI) technology, combined with big data, hold the potential to solve many key clinical trial challenges.
Real World Data
Late phase research, technology solutions and access to RWD are at the forefront of creating efficiencies in all stages of study design and implementation.
The role of digital therapeutics in central nervous system clinical trials
In this article, ICON's Louisa Steinberg and Maureen Glynn consider the role of digital therapeutics in the treatment of central nervous system conditions and how they can be used to improve clinical trials.
AI In Clinical Research: Now And Beyond
In this piece from Forbes, Dr. Greg Licholai, Chief Medical and Chief Innovation Officer talks about recent developments, regulatory considerations, and the promising future of AI in clinical research are reshaping the landscape of drug development and patient care.
A mind for digital therapeutics: Considerations for DTx clinical trials in CNS indications
DTx deliver evidence-based therapeutic intervention using software to prevent, manage, or treat medical disorders or disease. Because of the evidence-based nature of DTx, in many cases a clinical trial is required to demonstrate safety and efficacy. However, since such therapeutics are relatively new, there are many aspects to these clinical trials that differ from trials for traditional therapeutics, and which sponsors will need to consider.
Clinical Trial Tokenisation
Powerful insights for successful outcomes
Tokenisation enables sponsors to link participant information in a blinded and encrypted way for better intelligence on health care outcomes.
Clinical trial data anonymisation and data sharing
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.
Closing the evidence gap: Digital health technologies and drug reimbursement
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.
Whitepaper: Wearables and digital endpoint strategy and validation
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:
- Explore the significance of digital endpoints including digital biomarkers
- Build a case for the broader adoption of digital health technologies and digital endpoints
- Outline a strategy to best harness these innovations with an end-to-end framework to selecting and validating devices and endpoints
- Provide checklists for device selection and data strategy
- Consider the digital health technologies implications of the COVID-19 pandemic
- Demonstrate their future potential and impact on R&D
How will US payers evaluate and manage digital therapeutics?
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.
Reimagining Patient-Centricity with the Internet of Medical Things (IoMT)
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.
How digital technologies will transform R&D productivity
Emerging digital technologies, such as artificial intelligence (AI), robotic process automation (RPA), blockchain and quantum computing, offer significant opportunities to improve R&D productivity.
Blog: Pharma ROI restoration
Media article: How healthcare can develop through digital innovation
The impact of artificial intelligence on outcomes based contracting
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.
Personalising Digital Health
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.
Blog: Precision medicine
Digital Health Ecosystem
New technologies are enhancing the efficiency and scope of clinical trials through:
- Big data and predictive analytics which enable quick identification of promising study subjects and sites
- Artificial intelligence (AI) processing large amounts of data to help guide patient management and protocol design
- Electronic health records increasing data collection reach and efficiency, and help better integrate trials into clinical practice
- Patient-focused technologies, such as mobile sensors and smartphone apps
How to implement successful digital clinical trials
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.
Disruptive Innovation – The Impact
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.
Real World Data
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.
RWD and Alzheimer's
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.
Meeting evidentiary needs with EHRs
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.
Cybersecurity in Medical Devices
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.
mHealth & Wearables
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.
Harnessing blockchain technology and digital disruption
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.
Artificial Intelligence (AI)
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.