Digital Disruption

Mental health disorders affect millions, and with increasing diagnoses, the demand for new, accessible treatments is growing. In this whitepaper, we examine how SaMD, particularly through digital therapeutics, is providing new solutions for mental health management.

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.

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.

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

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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.

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.

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

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

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

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.