The dashboard is the potent symbol of our age. It offers the elegant visualization of data, and is intended to capture and represent the performance of a system, revealing at a glance current status, and pointing out potential emerging concerns. Dashboards are a prominent feature of most every “big data” project I can think of, offered by every vendor, and constructed to provide a powerful sense of control to the viewer. It seemed fitting that Novartis CEO Dr. Vas Narasimhan, a former McKinsey consultant, would build (then tweet enthusiastically about) “our new ‘control tower’” – essentially a multi-screen super dashboard – “to track, analyse and predict the status of all our clinical studies. 500+ active trials, 70+ countries, 80 000+ patients – transformative for how we develop medicines.” Dashboards are the physical manifestation of the ideology of big data, the idea that if you can measure it you can manage it.
I am increasingly concerned, however, that the ideology of big data has taken on a life of it’s own, assuming a sense of both inevitability and self-justification. From measurement in service of people, we increasingly seem to be measuring in service of data, setting up systems and organizations where constant measurement often appears to be an end in itself.
My worries, it turns out, are hardly original. I’ve been delighted to discover over the past year what feels like an underground movement of dissidents who question the direction we seem to be heading, and who’ve thoughtfully discussed many of the issues that I stumbled upon. (Special hat-tip to “The Accad & Koka Report” podcast, an independent and original voice in the healthcare podcast universe, for introducing me to several of these thinkers, including Jerry Muller and Gary Klein.)
Health Datapalooza is coming up quick at the end of April, so I sat down with Bruce Greenstein, CTO of HHS about why all of THCB’s health tech friends should attend. Plus, we get into what’s happening with the open data movement and how Bruce’s past-life at Microsoft is going to shape how he and HHS work with those consumer tech companies that are pushing harder and harder into healthcare.
The healthcare AI space is frothy. Billions in venture capital are flowing, nearly every writer on the healthcare beat has at least an article or two on the topic, and there isn’t a medical conference that doesn’t at least have a panel if not a dedicated day to discuss. The promise and potential is very real.
And yet, we seem to be blowing it.
The latest example is an investigation in STAT News pointing out the stumbles of IBM Watson followed inevitably by the ‘is AI ready for prime time’ debate. If course, IBM isn’t the only one making things hard on itself. Their marketing budget and approach makes them a convenient target. Many of us – from vendors to journalists to consumers – are unintentionally adding degrees to an already uphill climb.
If our mistakes led to only to financial loss, no big deal. But the stakes are higher. Medical error is blamed for killing between 210,000 and 400,000 annually. These technologies are important because they help us learn from our data – something healthcare is notoriously bad at. Finally using our data to improve really is a matter of life and death.
One of the more interesting companies playing in the analytics space is Ayasdi. We’ve featured them at Health 2.0 a couple of times, but at HIMSS I got a chance to talk a little more in depth with chief medical officer Francis Campion about exactly how they parse apart huge numbers of data points, usually from EMRs, and then operationalize changes for their clients. The end result is more effective care and lower variability across different facilities, for example changing when drugs are delivered before surgery in order to improve outcomes. And increasingly their clients are doing this over multiple clinical pathways. They’re really on the cutting edge of how data will change care delivery (a tenet of our definition of Health 2.0) so watch the interview to hear and see more!
This weekend the NYTimes published an editorial titled Give Up Your Data to Cure Disease. When we will stop seeing mindless memes and tropes that cures and innovation require the destruction of the most important human and civil right in Democracies, the right to privacy? In practical terms privacy means the right of control over personal information, with rare exceptions like saving a life.
Why aren’t government and industry interested in win-win solutions? Privacy and research for cures are not mutually exclusive.
How is it that government and the healthcare industry have zero comprehension that the right to determine uses of personal information is fundamental to the practice of Medicine, and an absolute requirement for trust between two people?
Why do the data broker and healthcare industries have so little interest in computer science and great technologies that enable research without compromising privacy?
Today healthcare “innovation” means using technology for spying, collecting, and selling intimate data about our minds and bodies.
This global business model exploits and harms the population of every nation. Today no nation has a map that tracks the millions of hidden data bases where health information is collected and used, inaccessible and unaccountable to us. How can we weigh risks when we don’t know where our data are held or how data are used? See www.theDataMap.org .
The aging of populations worldwide is leading to many healthcare challenges, such as an increase in dementia patients. One recent estimate suggests that 13.9% of people above age 70 currently suffer from some form of dementia like Alzheimer’s or dementia associated with Parkinson’s disease. The Alzheimer’s Association predicts that by 2050, 135 million people globally will suffer from Alzheimer’s disease.
While these are daunting numbers, some forms of cognitive diseases can be slowed if caught early enough. The key is early detection. In a recent study, my colleague and I found that machine learning can offer significantly better tools for early detection than what is traditionally used by physicians.
One of the more common traditional methods for screening and diagnosing cognitive decline is called the Clock Drawing Test. Used for over 50 years, this well-accepted tool asks subjects to draw a clock on a blank sheet of paper showing a specified time. Then they are asked to copy a pre-drawn clock showing that time. This paper and pencil test is quick and easy to administer, noninvasive, and inexpensive. However, the results are based on the subjective judgment of clinicians who score the tests. For instance, doctors must determine whether the clock circle has “only minor distortion” and whether the hour hand is “clearly shorter” than the minute hand.
I am a clinician and a clinical trialist. Medical research in some form or another (performing it, consuming it, reviewing it, editing it, etc.) occupies much of my time. Therefore, you can imagine my excitement while watching Apple’s product announcement yesterday when they introduced a new open source software platform called ResearchKit. Apple states ResearchKit could:
“revolutionize medical studies, potentially transforming medicine forever”
ResearchKit allows clinical researchers to have data about various diseases collected directly from a study participant’s iPhone (and perhaps other devices in the future — see below). The software is introduced as a solution to several important problems with current clinical studies, such as:
limited participation (the software allows everyone to participate; anyone with an iPhone can download a specific app for every study they want to participate in)
frequent data entry (patients can enter data as often as required/desired, rather than only at limited opportunities such as hospital or clinic visits)
data fidelity (currently-used paper patient “diaries” are prone to entering implausible or impossible values — the iPhone can limit the range of data entered)
Specifically, the website states:
ResearchKit simplifies recruiting and makes it easy for people to sign up for a study no matter where they live in the world. The end result? A much larger and more varied study group, which provides a more useful representation of the population.
This is a bold claim. We’ll see below that it doesn’t yet ring true.
As government involvement in U.S. health care deepens—through the Affordable Care Act, Meaningful Use, and the continued revisions and expansions of Medicaid and Medicare—the politically electric watchword is “socialism.”
Online, of course, social media is not a latent communist threat, but rather the most popular destination for internet users around the world.
People, whether out of fear for being left behind, or simply tickled by the ease with which they can publicize their lives, have been sharing every element of their public (and very often, their private) lives with ever-increasing zeal. Pictures, videos, by-the-minute commentary and updates, idle musings, blogs—the means by which people broadcast themselves are as numerous and diverse as sites on the web itself.
Even as the public decries government spying programs and panics at the news of the latest massive data-breach, the daily traffic to sites like Facebook and Twitter—especially through mobile devices—not only stays high, but continues to grow. These sites are designed around users volunteering personal information, from work and education information, to preferences in music, movies, politics, and even romantic partners.
I’ve been thinking a lot about “big data” and how it is going to affect the practice of medicine. It’s not really my area of expertise– but here are a few thoughts on the tricky intersection of data mining and medicine.
First, some background: these days it’s rare to find companies that don’t use data-mining and predictive models to make business decisions. For example, financial firms regularly use analytic models to figure out if an applicant for credit will default; health insurance firms can predict downstream medical utilization based on historic healthcare visits; and the IRS can spot tax fraud by looking for fraudulent patterns in tax returns. The predictive analytic vendors are seeing an explosion of growth: Forbes recently noted that big data hardware/software and services will grow at a compound annual growth rate of 30% through 2018.
Big data isn’t rocket surgery. The key to each of these models is pattern recognition: correlating a particular variable with another and linking variables to a future result. More and better data typically leads to better predictions.
It seems that the unstated, and implicit belief in the world of big data is that when you add more variables and get deeper into the weeds, interpretation improves and the prediction become more accurate.Continue reading…
In December, THCB asked industry insiders and pundits across health care to give us their armchair quarterback predictions for 2015. What tectonic trends do they see looming on the horizon? What’s overrated? What nasty little surprises do they see lying in wait? What will we all be talking about this time next year? Over the next few weeks, we’ll be featuring their responses in a series of quick takes.
Joe DeSantis, Vice President of HealthShare Platforms, InterSystems
Information Exchange is dead. Long live Information Exchange: There was a lot of talk in 2014 about the failure of information exchange. When people take a closer look, they are going to see there are actually some good examples of this working and changing how care is delivered. We’ll see lots more examples in 2015.
(Big) garbage in, (big) garbage out: People are looking to big data and analytics to tackle population health and other problems. They will soon find that without addressing data quality and conditioning up front, the results will be disappointing at best. This will be the year of clean data.
Keep it simple: The mobile revolution has not yet had the impact on healthcare that it has had in other sectors. Recreating desktop applications on a phone is not the answer, nor are retreads of messaging standards. We will have to rethink how healthcare information is presented and used.
One portal, please: Everyone agrees that patient engagement is essential – but giving me four separate portals, six more for my wife and three more for my mother makes me enraged, not engaged! Thought leaders will begin to realize that patient engagement must be built atop true information sharing.Continue reading…