Google, Biogen Idec Unite to Bring New Research to Multiple Sclerosis
Google X has partnered with Biogen Idec, the multiple sclerosis drugmaker, to expand its foray into medical research. The duo will study environmental and biological contributors to the debilitating disease’s progression, according to a report from Bloomberg.
Google and Biogen will use sensors, software and data analysis tools to collect and analyze data from individuals who have multiple sclerosis (MS). The companies aim to explore why MS progresses differently in each patient.
The collaboration is the second major pharma partnership for the life science division of Google X labs, which has also developed creations such as Google Glass and self-driving cars.
“Our central thesis is to change health care from being reactive to proactive,” Andrew Conrad, head of Google X, told the news source. “We’re trying to understand disease at its onset and see if we can intervene early.”
Bloomberg pointed out that Biogen has used digital tools for its disease research in the past. Last month, Biogen announced that it was using Fitbit activity trackers to gather data from people who have MS.
It gave 250 Fitbit bands to participants to track their level of activity and sleep patterns. Last summer, the pharma company worked with Cleveland Clinic to develop an iPad app to assess MS progression.
Last year, Google also made quite a few moves in the digital health space. The company partnered with Novartis, another pharma company, to license its smart contact lens, which Google announced in January 2014 as a noninvasive method of measuring blood glucose levels in people with diabetes.
In October, Google X revealed that it is developing a smart pill that could scan for cancer and send the results to a user’s wearable sensor device. The pill is packed with tiny magnetic particles that can go looking for malignant cells in the bloodstream and report findings via Bluetooth to a wearable device. A week later, Andrew Conrad, the project lead, added that he believes the project is only a few years away from viability.
More data may help Biogen develop more effective drugs, or better understand which drugs should go to which patients. Real-world experience of patients could also help Biogen prove the value of its medicine to insurers and pharmacy benefit managers, which are increasingly cracking down on high-priced drugs. MS treatments typically have wholesale prices of $50,000 a year.
Introducing ClosedLoop - healthcare’s data science platform
ClosedLoop is an Austin-based, AI-driven data science platform built for the healthcare sector. The startup recently beat competition from IBM, Accenture and Deloitte among others to win the Centers for Medicare & Medicaid Services (CMS) Artificial Intelligence (AI) Health Outcomes Challenge.
This $1.6 million award was given to the startup for its work in a national competition aimed at addressing the healthcare gap in the US. Carol McCall, Chief Analytics Officer, tells us what sets the company apart.
What does ClosedLoop offer?
ClosedLoop’s data science platform is purpose-built and dedicated to healthcare and life science organisations. The platform integrates four technology-enabled data science workflows: data onboarding and normalisation, automated feature engineering, model training and validation, and model deployment and MLOps. Each workflow embeds numerous capabilities and functions that facilitate exploration, collaboration, oversight, and management.
What makes ClosedLoop different to other AI platforms?
ClosedLoop.ai is healthcare’s data science platform. We make it easy for healthcare organisations (HCOs) to use AI to improve outcomes and reduce costs. Purpose-built and dedicated to healthcare, ClosedLoop combines an intuitive end-to-end machine learning platform with a comprehensive library of healthcare-specific features and model templates.
ClosedLoop’s customers use explainable AI to drive clinical excellence, operational efficiency, value-based contracts, and enhanced business results. What sets us apart is our commitment and ability to deliver unbiased accuracy and explainable AI solutions at-scale in healthcare.
How does ClosedLoop avoid bias?
The ClosedLoop platform has built-in capabilities for helping to address bias and fairness. This is crucial for AI systems, particularly when they are used to inform decisions about allocating limited resources. Models that systematically underpredict risk for a particular group can lead to that group being unfairly denied resources.
With respect to algorithmic bias, our AI platform systematically assesses for bias in model design, data, and sampling, and makes sure to use measures that are insensitive to differences in disease prevalence between groups.
To assess fairness, ClosedLoop developed a new metric called Group Benefit Equality (GBE). Standard fairness metrics are completely unsuited to healthcare situations - they ignore “false negative” errors, which can leave individuals who would benefit from an intervention unable to get it, or use arbitrary benchmark thresholds that fail to adjust for instances where the alarm rate for the reference group is too low.
The GBE metric addresses these shortcomings. GBE is also easily explained, has transparent procedures, and uses clearly defined thresholds to assess when models are biased.
What is explainable AI?
For ClosedLoop’s customers, Explainable AI is key because it completely reimagines the concept of patient risk profiling. It shifts away from legacy risk “scores” to a comprehensive, personalised forecast that can be delivered directly into a clinical workflow.
Each forecast - which might harness several years of patient-linkable data - surfaces key variables and explains precisely what risks a patient faces and why. Each forecast integrates relevant clinical information and can link to specific interventions that clinical teams use to prevent adverse events, improve outcomes, and reduce unnecessary costs.
How digitally mature does a healthcare provider need to be to use the platform?
The flexibility of ClosedLoop’s platform allows our customers to leverage AI-based solutions no matter where they are on the maturity curve of data science and machine learning. This is because our product has two main pillars of technology.
The first pillar is used by organisations with robust technical or data science teams, and includes a fully-featured automated data science and machine learning platform. The platform’s capabilities allows data science teams to move quickly, build bigger and better models, and operate at the top of their license.
It automates a lot of the mundane work and deep operational details so data science teams can focus on asking the right questions, engaging with clinicians and care management teams, and communicating results.
The second pillar is used by organisations without large (or perhaps any) data science or technical teams. They are able to quickly leverage the platform’s healthcare-specific comprehensive enterprise feature store and an extensive catalogue of off-the-shelf model templates for common healthcare use cases.
These allow customers to quickly train models with data from their specific population, fine tune them to fit the context in which they will be used, and deploy them rapidly into their operational workflows.
The company has won a number of awards, to what do you attribute its success?
One reason is because we have been bold. In the CMS Challenge, for example, we believed we had the right and the ability to win against some huge names. That becomes important when, along the way, advisors become sceptical and suggest we stay focused on the core business. Today, those same people have said, "Hey, this is big!" You have to make bets. You have to decide. While it’s true that the work was aligned with where we were going, the amount of effort it took was unreal. There's no other way to really take a shot than putting every hour of all your best people on it.
What goals do you have for the next few years?
ClosedLoop’s customers share a common goal of achieving the Triple Aim: improve patient outcomes, reduce unnecessary healthcare costs, and enhance the experience of care.
Achieving the Triple Aim has become a national imperative - nearly one-third of Medicare beneficiaries experience an unplanned hospital admission or other adverse event each year, and the US spends almost $1 trillion on healthcare annually that does nothing to improve health outcomes. ClosedLoop’s goal is to make it easy and affordable for every HCO in the country to become AI-enabled and to use our explainable AI solutions to achieve the Triple Aim.