デジタル化による既成概念の変化
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.
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 patients and developers benefit in a digital health ecosystem
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.
New Report: Read the views of three Senior Pharma Executives on how their organisations are approaching innovation.
Artificial Intelligence (AI)
In our industry survey and whitepaper, 'Improving Pharma R&D Efficiency' when asked what technologies would have the most impact improving clinical trial efficiency, the top answer from survey respondents was leveraging big data and AI at 36 percent.
Indeed Big Data and AI technologies are complimentary as AI can help to synthesize and analyse ever-expanding data. AI-enabled measures include data integration, data management and interpretation.
They 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.
Blog: The power of AI to transform clinical trials
Blog: Leveraging Voice-Assistant Technology in Clinical Trials
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.
Read our white paper, “On a Technology-Enabled Collision Course: Clinical Research Meets Clinical Practice through Real World Evidence” and learn how to:
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.
Read our whitepaper: Meeting Evidentiary Needs with Electronic Health Records white paper
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.
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.
Blog: Five Cybersecurity Threats for Medical Device Manufacturers
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.
ICON Explores Quantum Computing
Improving Trial Designs for Heterogeneous Populations
Accurately projecting outcomes for diverse patient populations – from the wealth of genomic, phenotypic and outcomes data available through genome sequencing and electronic health records – holds the potential to transform the effectiveness and efficiency of drug and medical device development.
Quantum computing may enable statisticians to quickly explore, understand and interpret these enormous multivariate and often poorly structured data.
ICON is actively exploring this new field of quantum computing, and in spring 2017 co-sponsored a quantum computing workshop.