You know all that “magic” that machine learning is meant to bring to seemingly lackluster healthcare data and our limited understanding of it? Komodo Health’s co-founder & CEO Arif Nathoo demystifies the wizardry of one of our favorite buzz phrases, “The Algorithm,” and gives us a colorful overview of how his startup is making data useful to the way payers, health systems, and pharma co’s study populations at-scale. Komodo’s raised $314M to-date, closing a MASSIVE $220M Series E backed by Tiger Global Management, Casdin Capital, ICONIQ Growth, Andreessen Horowitz, and SVB Capital in April, and after hearing this enthusiastic explanation of what they’re working on – and the market potential for it – we understand why.
At its most simplistic, Komodo is using de-identified healthcare claims data as a base from which to learn how patients flow through the healthcare system. Other data sets are brought in and layered onto that “patient-flow, dollar-flow” claims trail in effort create a new vantage point for seeing what’s happening within the system, at a population level. That insight can then be used to predict patient behavior and provide evidenced analysis for how the system can be improved. Don’t worry: Arif provides lots of detailed examples and talks through exactly what kind of data can (and currently can’t) be pulled into the mix. If you want to get smart on the “Big Data” opportunity in healthcare and how it’s going to be impacting the future of care delivery and virtual care delivery, this is one chat you won’t want to miss!
When you left the story your hero had just arranged for Best Buy to attempt delivery on Tuesday afternoon last week. I was in SF for the “can’t miss” Rock Health Summit. I was waiting at the apartment when I got about 4 calls from the same random number in 3 minutes but when I answered no one was there. I called back, no answer. Then I got a voicemail saying the delivery team was outside. I ran outside! No they weren’t! At that point I gave up and had lunch. But then for now the 5th time I called Best Buy and lined up a new delivery. I stressed about 10 times that the delivery team could NOT leave next time without seeing me. There may have been some shouting…..
Monday was the next available day for delivery and it was day that Best Buy was going to finally get it right. I got an email saying they’d be there at 1.30pm
I was across town in a meeting at 12.30 and noticed 4 missed calls from the same number. Being of a very suspicious nature, I called the number, and yes it’s the delivery team. They were outside the apartment, and they were 60 mins early! Thankfully the delivery crew agreed to wait, and I went over to meet them. So at 6th time of asking, the crew was there, the equipment was there, I was there, and we all went into the apartment.
When you ask the ‘big data guy’ at a massive health system what’s wrong with EMRs, it’s surprising to hear that his problem is NOT with the EMRs themselves but with the fact that health systems are just not using the data they’re collecting in any meaningful way. Atul Butte, Chief Data Scientist for University of California Health System says interoperability is not the big issue! Instead, he says it’s the fact that health systems are not using some of the most expensive data in the country (we are using doctors to data entry it…) to draw big, game-changing conclusions about the way we practice medicine and deliver care. Listen in to find out why Atul thinks that the business incentives are misaligned for a data revolution and what we need to do to help.
Filmed at Health Datapalooza in Washington DC, March 2019.
Jessica DaMassa is the host of the WTF Health show & stars in Health in 2 Point 00 with Matthew Holt.
Get a glimpse of the future of healthcare by meeting the people who are going to change it. Find more WTF Health interviews here or check out www.wtf.health.
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.