Digital Disruption

Technologies that accelerate and improve clinical trials

How Will US Payers Evaluate and Manage Digital Therapeutics?

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. 

Read the whitepaper, Payers’ Perspectives on Digital Therapeutics

Reimagining Patient-Centricity with the Internet of Medical Things (IoMT)

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.

Read the whitepaper
How digital technologies will transform R&D productivity

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. Learn the impact of these technologies in our white paper.

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

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.

For insights on how AI could help to overcome these challenges, download our whitepaper, "The Impact of AI on Outcomes Based Contracting"

Utilising emerging technologies

Utilising emerging technologies

Chen Admati (Intel) and Marie McCarthy (ICON) talk about utilising emerging technologies to improve the efficiency of research and development in the pharmaceutical industry in this PharmaVOICE podcast. (20 mins)

Personalising Digital Health

Personalising Digital Health

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.

Read our white paper: “Personalising Digital Health: How to develop and deploy novel technologies to reduce patient burden and increase engagement”

Infographic: 5 Steps to Successfully Incorporate Digital Health in Clinical Trials

Digital Health Ecosystem

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 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


How to implement successful digital clinical trials

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.

Key Considerations For Achieving Digital Trial Success 

Webinar recording: Best Practices for Implementing a Successful Digital Trial

Disruptive Innovation – The Impact

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.

Report: Read the views of three Senior Pharma Executives on how their organisations are approaching innovation.

Latest on mHealth, Wearables, AI and Predictive Informatics

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Artificial Intelligence (AI)

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.

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

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

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.

Read our whitepaper: Meeting Evidentiary Needs with Electronic Health Records white paper

Cybersecurity in Medical Devices

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: How Can Manufacturers Improve Responses to Medical Device Cybersecurity Vulnerabilities?

Blog: Five Cybersecurity Threats for Medical Device Manufacturers

mHealth & Wearables in clinical trials

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.

ICON Explores Quantum Computing

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.

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