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by Ryan Noble
The rise of connected devices and the variety of information they can generate is set to drive an increase in patient adherence to therapies. Our trials have shown that remote and hidden sensing of actual user behaviour can uncover unexpected and significant opportunities to improve the patient experience.
So how can connected technology help in the development stages of a new product, especially in a clinical environment? A recent study – Non-adherence: a direct influence on clinical trial duration and cost – by Moe Alsumidaie highlights the significant costs of non-adherence during pharmaceutical development. The study reported a 40% increase in patient enrolment to allow for non-adherence – adding an estimated $12m to the cost of a Phase 3 study.
We are also starting to see many medical device approvals in the connected space. MobileHealth reported 51 approvals in 2017 alone – the focus being on app-based patient management of disease, especially in the cardiac and diabetes sectors. There were only two respiratory-based systems reported – namely the connected spirometer GoSpiro and a new inhaler monitoring device for AstraZeneca’s Symbicort aerosol inhaler, dubbed the SmartTouch.
These solutions are enabling remarkable new capabilities for patients – and also for payers as we move towards outcomes-based healthcare. But are there steps that can be taken earlier in medical device development that can disrupt the whole process for the benefit of everyone?
What if we took a little bit of time to insert technology into products in either the clinical stage of drug development or early device design phases to understand how patients interact with the device and dosing regime? Two of the main methods to understand what has happened in a clinical investigation is to get patients to fill in a diary during their study and, on return, count the number of doses taken from the inhaler or capsule pack. Not quite 21st century.
Maybe, in the near future, clinical plans will include more advanced technology to enable a more accurate understanding of the efficacy of a new drug in development – was that poor resultant FEV1 clinical endpoint really due to the drug or was it because the patient simply forgot to prime the device and inhaled nothing but fresh air? Being able to unpick the actual usage data, so that these distinctions can be accurately made, could potentially help all stakeholders to better understand what the patient actually did and hence clarify where the subsequent opportunity to improve patient outcome actually lies – be it drug, device or training/education. In essence, it’s about using technology to guide design and development so that the appropriate solution is selected.
Here at CDP we wanted to go further and challenge ourselves to capture some very specific usage data for inhalation, whilst avoiding the Hawthorne effect and without changing the external form factor, thereby minimising any influence on user behaviour. It is common knowledge that all inhalers have associated use errors, so we took a commercially available one that has documented use errors and inserted miniature sensors that would enable both real-time indication and post-usage remote assessment of those use errors – namely priming action, orientation of use and inhalation/exhalation profile. We enabled the data to be time stamped and communicated to an appropriate output, in this case on-screen graphical readouts.
Behind this is the need to understand the volume of specific use data that gets logged and learn how to translate and classify the events represented as peaks and troughs on a graph. At CDP we have a wealth of experience of doing this across several sectors including sports and packaging systems.
If you’re looking for a breath of fresh air in your next respiratory drug delivery development, get in touch via firstname.lastname@example.org or visit us at the RDD 2018 event in Arizona, 22-26 April on exhibit table 6.
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