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The first application is behavioural support by targeting and tailoring interventions designed to change behaviour and improve patient adherence to therapy. RWD from connected devices create a feedback loop by providing a mechanism to learn which interventions are most effective. Today machine learning models are being applied to deliver interventions, measure the effect of each intervention on the target behaviour, and improve the accuracy of the next intervention based on real-time adherence data. Dose-level adherence data also enhances remote treatment support, in which the data is used to improve clinical treatment decisions, and support ongoing dose titration until the optimal treatment regimen for each patient is reached.
Metcalfe’s law, proposed by Robert Metcalfe the inventor of Ethernet, states that the value of a network grows exponentially as a function of network size5. When RWD is actively used to improve the patient experience, increase their engagement with their care plan, and improve adherence to therapy, Metcalfe’s Law predicts its value will grow exponentially for all stakeholders across the care continuum. For pharmaceutical companies, this benefit will best manifest itself in an ability to measure and improve the realworld effectiveness of drugs. This next frontier in drug delivery combines Moore’s and Metcalfe’s laws to become data-driven, personalised, outcome-based, and accessible. This next frontier is connected therapeutics (CTx).
Solely relying on the clinical efficacy of new medications is not the solution to chronic disease management; rather, we need to find ways to help patients self-manage existing conditions.”
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