November 7th: Digital Medicine News Picked for You

What can quantum computing do to healthcare? Are the data of Fitbit’s users safe after the acquisition by Google? What are the challenges of the FDA pre-cert program?

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#Digital Health#Digital Medicine#Parkinson's

Google Buys Fitbit For $2.1 Billion

Google announced that it’s buying wearable company Fitbit for $2.1 billion. According to a separate press release issued by Fitbit, the company will continue to take privacy for health and fitness data seriously, noting that “Fitbit health and wellness data will not be used for Google ads.”

Our take: Google is clearly not as concerned about mounting antitrust sentiment as the news seems to suggest and is looking to spread its influence into healthcare more directly. In a match up with Apple, Google lacked a wearable such as the Apple Watch that would allow it to collect more health-related data than it does now and do so with lower user burden. Google would have faced delays and probably a significantly higher cost to attempt to introduce their own wearable and have people switch to it.

Fitbit’s announcement is an interesting way to tackle the privacy concern. The most compelling use case for data that Google will obtain from Fitbit’s users is not for ads targeting, but for being able to analyze that data to make predictions about health and behavior of those users. This would be a valuable first step in being able to offer in the future a data-driven telemedicine solution and a real world evidence platform for the next generation of EHRs. – An Advisor

Applying Data Science Principles To FDA’s PreCert Program: Some Scary Stuff

There is much to be praised in FDA’s work on the PreCert Program. However, there are fundamental issues that stakeholders must assess when deciding whether to support the program. The biggest issue is how FDA will make its decisions.

Our take: The article offers one of the deeper analyses of the Pre-Cert program, but does so from the perspective of the companies seeking approval. From the perspective of the patient, the key question is whether the technology addresses an unmet need and how impactful it will likely be in doing so. That point of view is missing from both this article and the FDA’s own goals, which are more about streamlining the administrative burden of burgeoning innovative technologies. The most concerning point this article raises is that large companies are the ones with the resources to participate in this program and that this may stifle truly novel approaches, which are usually too risky for established players. – An Advisor

Pear Therapeutics To Explore GI Indications With Its Latest Pharma Partnership

Pear Therapeutics has announced a new deal with Ironwood Pharmaceuticals that will explore the platform’s potential for patients with certain gastrointestinal (GI) conditions. This new pharma deal will certainly draw some comparisons with Pear’s recently interrupted commercialization partnership with Novartis’ Sandoz. But in reality, the collaboration seems to be more in line with a different arrangement between the two companies that was focused on the development for digital therapeutics for multiple sclerosis and schizophrenia.

Our take: The corporate development team at Pear is keeping busy. As this article points out, the key open question is whether Pear has the infrastructure to distribute its products and thus own commercialization. Or, whether it needs big pharma partnerships for that purpose. The example of Akili Labs is instructive in this regard: there is a lot to be learned and data to be collected from keeping ownership of distribution of digital therapeutic products in a way distinct from traditional therapeutics. – An Advisor

WHO Expert Panel On Digital Health Meets For First Time

WHO is convening global experts to develop the Organization’s roadmap to advance the digital health ecosystem. The WHO Digital Health Technical Advisory Group met for the first time this week to discuss topics ranging from data governance, to ethical and equitable use of digital technologies, to helping communities benefit from proven and cost-effective digital health solutions. Meeting focused on better defining WHOs role in supporting global digital transformation.

Our take: The WHO’s involvement in regulating and supporting access to digital health recognizes that many “developing” countries where WHO plays an outsize role have skipped analog (land-line) telecommunications and gone straight to a massive adoption of mobile technology. These same countries face particularly pressing challenges to healthcare access, especially in rural areas. This creates a compelling environment for the rapid adoption of digital health technologies that improve access and capitalize on pervasiveness of mobile in people’s everyday lives. – An Advisor

What Can Quantum Computing Do To Healthcare?

A leap from bits to qubits: this two-letter change could mean entirely new horizons for healthcare. Quantum computing might bring supersonic drug design, in silico clinical trials with virtual humans simulated ‘live’, full-speed whole genome sequencing and analytics, the movement of hospitals to the cloud, the achievement of predictive health, or the security of medical data via quantum uncertainty.

Our take: This article offers a tantalizing glimpse into just a sliver of the possibilities that quantum computing may bring to healthcare. The difference quantum computers offer is their sheer computing power, allowing us to capture the kind and amount of data we cannot with the current binary systems. For example, a quantum computer can theoretically more accurately simulate complex systems, such as the body, allowing researchers to simulate effects of treatments and target them to each individual patient. And yet, this is just what we can imagine now, with a Cambrian explosion of ideas and applications when quantum computing technology becomes widely available. – An Advisor

HealthMode News

Machine learning leads to novel way to track tremor severity in Parkinson’s patients

For a long time there has been a call for an approach that can continuously measure tremors accurately without the need for patients to perform specific tasks. This article shows a method using wearable gyroscope sensors combined with two machine-learning algorithms: gradient tree boosting and LSTM-based deep learning. The results – estimations of resting tremor – turned out to be accurate and corresponding with UPDRS. The method can hugely support continuous passive monitoring for improvement of management – we hope creators will decide to follow a regulatory pathway to enable this method enter medical market in the future.

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