In his RDD talk, Dr Blakey suggested that the market for wearable health-related devices could fuel the development of digital, cloud-interconnected respiratory healthcare. As a study discussed by Richard Costello at RDD showed, even “simple” monitoring of inhaler medication adherence can make maintenance of asthma patients better and more cost-effective and, importantly, can distinguish between true therapeutic failures such as patients with refractory disease and failures due to non-adherence.
If we only had had that kind of information at the Smartmist launch, things could have possibly turned out differently because those kinds of studies are convincing payers today that such technology is worth paying for.
Today, I believe the greatest challenges and the biggest opportunities lie in integrating personal health care information throughout the whole life cycle of therapeutics and diagnostics – from the beginning of the R&D all the way through to the time we select prophylaxis and treatment for ourselves.
Continuous addition of lifetime healthcare information from an ever growing number of participants and analyses of these large data bases will improve the general pool of health and disease knowledge. Nothing could describe it more succinctly than the title of Eric Topol’s groundbreaking paper “Individualized Medicine from Prewomb to Tomb.”
What is in it for pharma companies?
Just think about the magnitude of the opportunity: almost every pivotal clinical trial in chronic respiratory diseases includes endpoints such as lung function, exacerbations, quality of life outcomes, adverse events, sputum changes, fever, fatigue, distance in the 6 minute walk, and changes of medication or need for rescue medication — all of which it is already possible to collect with a cell phone or inhaler sensors. We can envisage adding, when appropriate, such measurements as blood oxygen, exhaled gases, etc., that can be collected in the same way.
If we create a big interconnected community of individual patients that contribute personal data, we can generate a massive data set that is clearly the best imaginable, and the most representative, placebo population against which the data from the participants taking a new treatment can be compared.
