Did you know we are living in the Zettabyte Era? Honestly, did you even know what a zettabyte is? Kilobytes, gigabytes, maybe even terabytes, sure, but zettabytes? Well, if you ran data centers you’d know, and you’d care because demand for data storage is skyrocketing (all those TikTok videos and Netflix shows add up). Believe it or not, pretty much all of that data is still stored on magnetic tapes, which have served us well for the past sixty some years but at some point, there won’t be enough tapes or enough places to store them to keep up with the data storage needs.
That’s why people are so keen on DNA storage – including me.
A zettabyte, for the record, is one sextillion bytes. A kilobyte is 1000 bytes; a zettabyte is 10007. Between gigabytes and zettabytes, by powers of 1000, come terabytes, petabytes, and exabytes; after zettabyte comes yottabytes. Back in 2016, Cisco announced we were in the Zettabyte Era, with global internet traffic reaching 1.2 zettabytes. We’ll be in the Yottabyte Era before the decade is out.
In Partnership with the Duke-Margolis Center for Health Policy, Resolve to Save Lives, Carnegie Mellon University, and University of Maryland, Catalyst @ Health 2.0 is excited to announce the launch of The COVID-19 Symptom Data Challenge. The COVID-19 Symptom Data Challenge is looking for novel analytic approaches that use COVID-19 Symptom Survey data to enable earlier detection and improved situational awareness of the outbreak by public health and the public.
How the Challenge Works:
In Phase I, innovators submit a white paper (“digital poster”) summarizing the approach, methods, analysis, findings, relevant figures and graphs of their analytic approach using Symptom Survey public data (see challenge submission criteria for more). Judges will evaluate the entries based on Validity, Scientific Rigor, Impact, and User Experience and award five semi-finalists $5,000 each. Semi-finalists will present their analytic approaches to a judging panel and three semi-finalists will be selected to advance to Phase II. The semi-finalists will develop a prototype (simulation or visualization) using their analytic approach and present their prototype at a virtual unveiling event. Judges will select a grand prize winner and the runner up (2nd place). The grand prize winner will be awarded $50,000 and the runner up will be awarded $25,000.The winning analytic design will be featured on the Facebook Data For Good website and the winning team will have the opportunity to participate in a discussion forum with representatives from public health agencies.
Phase I applications for the challenge are due Tuesday, September 29th, 2020 11:59:59 PM ET.
Learn more about the COVID-19 Symptom Data Challenge HERE.
Challenge participants will leverage aggregated data from the COVID-19 symptom surveys conducted by Carnegie Mellon University and the University of Maryland, in partnership with Facebook Data for Good. Approaches can integrate publicly available anonymized datasets to validate and extend predictive utility of symptom data and should assess the impact of the integration of symptom data on identifying inflection points in state, local, or regional COVID outbreaks as well guiding individual and policy decision-making.
These are the largest and most detailed surveys ever conducted during a public health emergency, with over 25M responses recorded to date, across 200+ countries and territories and 55+ languages. Challenge partners look forward to seeing participant’s proposed approaches leveraging this data, as well as welcome feedback on the data’s usefulness in modeling efforts.
Indu Subaiya, co-founder of Catalyst @ Health 2.0 (“Catalyst”) met with Farzad Mostashari, Challenge Chair, to discuss the launch of the COVID-19 Symptom Data Challenge. Indu and Farzad walked through the movement around open data as it relates to the COVID-19 pandemic, as well as the challenge goals, partners, evaluation criteria, and prizes.
This article originally appeared in the American Bar Association’s Health eSource here.
By KIRK NAHRA
This piece is part of the series “The Health Data Goldilocks Dilemma: Sharing? Privacy? Both?” which explores whether it’s possible to advance interoperability while maintaining privacy. Check out other pieces in the series here.
Congress is debating whether to enact a national privacy law. Such a law would upend the approach that has been taken so far in connection with privacy law in the United States, which has either been sector specific (healthcare, financial services, education) or has addressed specific practices (telemarketing, email marketing, data gathering from children). The United States does not, today, have a national privacy law. Pressure from the European Union’s General Data Protection Regulation (GDPR)1 and from California, through the California Consumer Privacy Act (CCPA),2 are driving some of this national debate.
The conventional wisdom is that, while the United States is moving towards this legislation, there is still a long way to go. Part of this debate is a significant disagreement about many of the core provisions of what would go into this law, including (but clearly not limited to) how to treat healthcare — either as a category of data or as an industry.
So far, healthcare data may not be getting enough attention in the debate, driven (in part) by the sense of many that healthcare privacy already has been addressed. Due to the odd legislative history of the Health Insurance Portability and Accountability Act of 1996 (HIPAA),3 however, we are seeing the implications of a law that (1) was driven by considerations not involving privacy and security, and (2) reflected a concept of an industry that no longer reflects how the healthcare system works today. Accordingly, there is a growing volume of “non-HIPAA health data,” across enormous segments of the economy, and the challenge of figuring out how to address concerns about this data in a system where there is no specific regulation of this data today.
After absorbing several years of increasingly extravagant promises about the remarkable potential of digital health, investors, physicians, and other stakeholders are now unabashedly demanding: “Show me the data.”
By now, most everyone appreciates the promise of digital health, and understands how, in principle, emerging, patient-focused technologies could help improve care and reduce costs.
The question is whether digital health can actually deliver.
A recent NIH workshop, convened to systematically review the data on digital health, acknowledged, “evidence is sparse for the efficacy of mHealth.”
As Scripps cardiologist Eric Topol and colleagues summarized in JAMA late last year,
“Most critically needed is real-world clinical trial evidence to provide a roadmap for implementation that confirms its benefits to consumers, clinicians, and payers alike.”
What everyone’s asking for now is evidence – robust data, not like the vast majority of wellness studies that experts like Al Lewis and others have definitively shredded.
The goal is to find solid evidence that a proposed innovation actually leads to measurably improved outcomes, or to a material reduction in cost. Not that it could or should, but that it does. Continue reading…
The IT specialist who has the greatest control over it?
The notion of ownership is inadequate for health information. For instance, no one has an absolute right to destroy health information. But we all understand what it means to own an automobile: You can drive the car you own into a tree or into the ocean if you want to. No one has the legal right to do things like that to a “master copy” of health information.
All of the groups above have a complex series of rights and responsibilities relating to health information that should never be trivialized into ownership.
“Come to think of it, there’s a certain class of rhetoric I’m going to call the ‘one-way hash‘ argument. Most modern cryptographic systems in wide use are based on a certain mathematical asymmetry: You can multiply a couple of large prime numbers much (much, much, much, much) more quickly than you can factor the product back into primes. A one-way hash is a kind of ‘fingerprint’ for messages based on the same mathematical idea: It’s really easy to run the algorithm in one direction, but much harder and more time consuming to undo. Certain bad arguments work the same way — skim online debates between biologists and earnest ID (Intelligent Design) aficionados armed with talking points if you want a few examples: The talking point on one side is just complex enough that it’s both intelligible — even somewhat intuitive — to the layman and sounds as though it might qualify as some kind of insight … The rebuttal, by contrast, may require explaining a whole series of preliminary concepts before it’s really possible to explain why the talking point is wrong.”
In an effort to help women make informed decisions about where to deliver their babies, we set out to collect a comprehensive, nationwide database of hospitals’ C-section rates. Knowing that the federal government mandates surveillance and reporting of vital statistics through the National Vital Statistics System, we contacted all 50 states’ (+Washington D.C.) Departments of Public Health (DPH) asking for access to de-identified birth data from all of their hospitals. What we learned might not surprise you — the lack of transparency in the United States healthcare system extends to quality information, and specifically C-section data. Continue reading…
If you’ve yet not discovered Alexis Madrigal’s fascinating Atlantic article (#longread), describing “how a dream team of engineers from Facebook, Twitter, and Google built the software that drove Barack Obama’s re-election,” stop right now and read it.
In essence, a team of technologists developed for the Obama campaign a robust, in-house platform that integrated a range of capabilities that seamlessly connected analytics, outreach, recruitment, and fundraising. While difficult to construct, the platform ultimately delivered, enabling a degree of logistical support that Romney’s campaign reportedly was never able to achieve.
It’s an incredible story, and arguably one with significant implications for digital health.
(1) To Leverage The Power of Data, Interoperability Is Essential
Data are useful only to the extent you can access, analyze, and share them. It increasingly appears that the genius of the Obama campaign’s technology effort wasn’t just the specific data tools that permitted microtargeting of constituents, or evaluated voter solicitation messages, or enabled the cost-effective purchasing of advertising time. Rather, success flowed from the design attributes of the platform itself, a platform built around the need for inoperability, and guided by an integrated strategic vision.
U.S. Sen. Charles Grassley (R-Iowa) sent a letter today to the Health Resources and Services Administration, criticizing its decision to remove a public version of the National Practitioner Data Bank, which has helped reporters and researchers to expose serious gaps in the oversight of physicians.
“Shutting down public access to the data bank undermines the critical mission of identifying inefficiencies within our health care system – particularly at the expense of Medicare and Medicaid beneficiaries,” Grassley wrote to HRSA Administrator Mary Wakefield. “More transparency serves the public interest.”
Grassley, ranking Republican on the Senate Judiciary Committee, continued: “Generally speaking, except in cases of national security, the public’s business ought to be public. Providers receive billions of dollars in state and federal tax dollars to serve Medicare and Medicaid beneficiaries. Accountability requires tracking how the money is spent.”
Value-based healthcare is gaining popularity as an approach to increase sustainability in healthcare. It has its critics, possibly because its roots are in a health system where part of the drive for a hospital to improve outcomes is to increase market share by being the best at what you do. This is not really a solution for improving population health and does not translate well to publicly-funded healthcare systems such as the NHS. However, when we put aside dogma about how we would wish to fund healthcare, value-based healthcare provides us with a very useful set of tools with which to tackle some of the fundamental problems of sustainability in delivering high quality care.
What is value?
Defined by Professor Michael Porter at Harvard Business School, value is defined as a function of outcomes and costs. Therefore to achieve high value we must deliver the best possible outcomes in the most efficient way, outcomes which matter from the perspective of the individual receiving healthcare and not provider process measures or targets. Sir Muir Gray expands on the idea of technical value (outcomes/costs) to specifically describe ‘personal value’ and ‘allocative value’, encouraging us to focus also on shared decision making, individual preferences for care and ensuring that resources are allocated for maximum value. This article seeks to demonstrate that the role of data and informatics in supporting value-based care goes much further than the collection and remote analysis of big datasets – in fact, the true benefit sits much closer to the interaction between clinician and patient.
Despite (some might say, because of) a raft of new biological methods, pharma R&D has struggled with its EROOM problem, the fact that the cost of successfully developing a new drug, including the cost of failures, has been relentlessly increasing, rather than decreasing, over time (EROOM is Moore spelled backwards, as in Moore’s Law, describing the rapid pace of technology improvement over time).
Given the impact of technology in so many other areas, the question many are now asking is whether technology could do its thing in pharma, and make drug development faster, cheaper, and better.
Many major pharmas believe the answer has to be yes, and have invested in some version of a by-now familiar data initiative aimed at aggregating and organizing internal data, supplementing this with available public data, and overlaying this with a set of analytical tools that will help the many data scientists these pharmas are urgently hiring to extract insights and accelerate research.