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November 07 2013

TERRA 823: The Venom Trail

The Venom Trail explores the path venom takes through a body and how the same chemicals are used to make medicine to combat the symptoms. Produced by Steve Spence.

August 24 2012

The Direct Project has teeth, but it needs pseudonymity

Yesterday, Meaningful Use Stage 2 was released.

You can read the final rule here and you can read the announcement here.

As we read and parse the 900 or so pages of government-issued goodness, you can expect lots of commentary and discussion. Geek Doctor  already has a summary and Motorcycle Guy can be expected to help us all parse the various health IT standards that have been newly blessed. Expect Brian Ahier to also be worth reading over the next couple of days.

I just wanted to highlight one thing about the newly released rules. As suspected, the actual use of the Direct Project will be a requirement. That means certified electronic health record (EHR) systems will have to implement it, and doctors and hospitals will have to exchange data with it. Awesome.

More importantly, this will be the first health IT interoperability standard with teeth. The National Institute of Standards and Technology (NIST) will be setting up an interoperability test server. It will not be enough to say that you support Direct. People will have to prove it. I love it. This has been the problem with Health Level 7 et al for years. No central standard for testing always means an unreliable and weak standard. Make no mistake, this is a critical and important move from the Office of the National Coordinator for Health Information Technology (ONC).

(Have I mentioned that I love that Farzad Mostashari — our current ONC — uses Twitter? I also love that he has a sense of humor!)

Now we just need to make sure that patient pseudonymity is supported on the Directed Exchange network. To do otherwise is to force patients to trust the whole network rather than to merely trust their own doctors. I have already made that case, but it is really nice to see both Arien Malec (founding coordinator of the Direct Project) and Sean Nolan (chief architect at Microsoft HealthVault) have weighed in with similar thoughts. Malec wrote a  lovely piece that details how to translate patient pseudonymity into NIST assurance levels. Nolan talked about how difficult it would be for HealthVault to have to do identity proofing on patients.

In order to emphasize my point in a more public way, I have beat everyone to the punch and registered the account of DaffyDuck@direct.healthvault.com. Everyone seems to think this is just the kind of madness that we need to avoid. But this is just the kind of madness that patients need to really protect their privacy.

Here’s an example. Lets imagine that I am a pain patient and I am seeking treatment from a pain specialist named Dr. John Doe who works at Pain No More clinic. His Direct address might be john.doe@direct.painnomore.com

Now if I provide DaffyDuck@direct.healthvault.com to Dr. Doe and Dr. Doe can be sure that he is always talking to me when he communicates with that address, then there is nothing else that needs to happen here. There never needs to be a formal cryptographic association between DaffyDuck@direct.healthvault.com and Fred Trotter. I know that there is a connection and my doctor knows that there is a connection and those are the only people that need to know.

If any cryptographic or otherwise published association were to exist, then anyone who had access to my public certifications and/or knew of communication between john.doe@direct.painnomore.com and DaffyDuck@direct.healthvault.com could make a pretty good guess about my health care status. I am not actually interested in trusting the Directed Exchange network. I am interested in trusting through the Directed Exchange network. Pseudonymity gives both me and my doctor that privilege. If a patient wants to give a different Direct email address to every doctor they work with, they should have that option.

This is a critical patient privacy feature of the Direct protocol and it was designed in from the beginning. It is critical that later policy makers not screw this up.

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January 25 2012

AI will eventually drive healthcare, but not anytime soon

TechCrunch recently published a guest post from Vinod Khosla with the headline "Do We Need Doctors or Algorithms?". Khosla is an investor and engineer, but he is a little outside his depth on some of his conclusions about health IT.

Let me concede and endorse his main point that doctors will become bionic clinicians by teaming with smart algorithms. He is also right that eventually the best doctors will be artificial intelligence (AI) systems — software minds rather than human minds.

That said, I disagree with Khosla on almost all of the details. Khosla has accidentally embraced a perspective that too many engineers and software guys bring to health IT.

Bear with me — I am the guy trying to write the "House M.D." AI algorithms that Khosla wants. It's harder than he thinks because of two main problems that he's not considering: The search space problem and the good data problem.

The search space problem

Any person even reasonably informed about AI knows about Go, an ancient game with simple rules. Those simple rules hide the fact that Go is a very complex game indeed. For a computer, it is much harder to play than chess.

Almost since the dawn of computing, chess was regarded as something that required intelligence and was therefore a good test of AI. In 1997, the world chess champion was beaten by a computer. In the year after, a professional Go player beat the best Go software in the world with a 25 stone handicap. Artificial intelligence experts study Go carefully precisely because it is so hard for computers. The approach that computers take toward being smart — thinking of lots of options really fast — stops working when the number of options skyrockets, and the number of potentially right answers also becomes enormous. Most significantly, Go can always be made more computationally difficult by simply expanding the board.

Make no mistake, the diagnosis and treatment of human illness is like Go. It's not like chess. Khosla is making a classic AI mistake, presuming that because he can discern the rules easily, it means the game is simple. Chess has far more complex rules than Go, but it ends up being a simpler game for computers to play.

To be great at Go, software must learn to ignore possibilities, rather than searching through them. In short, it must develop "Go instincts." The same is true for any software that could claim to be a diagnostician.

How can you tell when software diagnosticians are having search problems? When they cannot tell the difference between all of the "right" answers to a particular problem. The average doctor does not need to be told "could it be Zebra Fever?" by a computer that cannot tell that it should have ignored any zebra-related possibilities because it is not physically located in Africa. (No zebras were harmed in the writing of this article, and I do not believe there is a real disease called Zebra Fever.)

The good data problem

The second problem is the good data problem, which is what I spend most of my time working on.

Almost every time I get over-excited about the Direct Project or other health data exchange progress, my co-author David Uhlman brings me back to earth:

What good is it to have your lab results transferred from hospital A to hospital B using secure SMTP and XML? They are going to re-do the labs anyway because they don't trust the other lab.

While I still have hope for health information exchange in the long term, David is right in the short term. Healthcare data is not remotely solid or trustworthy. A good majority of the time, it is total crap. The reason that doctors insist on having labs done locally is not because they don't trust the competitor's lab; it's more of a "devil that you know" effect. They do not trust their own labs either, but they have a better understanding of how and when their own labs screw up. That is not a good environment for medical AI to blossom.

The simple reality is that doctors have good reason to be dubious about the contents of an EHR record. For lots of reasons, not the least of which is that the codes they are potentially entering there are not diagnostically helpful or valid.

Non-healthcare geeks presume that the dictionaries and ontologies used to encode healthcare data are automatically valid. But in fact, the best assumption is that ontologies consistently lead to dangerous diagnostic practices, as they shepherd clinicians into choosing a label for a condition rather than a true diagnosis. Once a patient's chart has a given label, either for diagnosis or for treatment, it can be very difficult to reassess that patient effectively. There is even a name for this problem: clinical inertia. Clinical inertia is an issue with or without computer software involved, but it is very easy for an ontology of diseases and treatments to make clinical inertia worse. The fact is, medical ontologies must be constantly policed to ensure that they do not make things worse, rather then better.

It simply does not matter how good the AI algorithm is if your healthcare data is both incorrect and described with a faulty healthcare ontology. My personal experiences with health data on a wide scale? It's like having a conversation with a habitual liar who has a speech impediment.

So Khosla is not "wrong" per-se; he's just focused on solving the wrong parts of the problem. As a result, his estimations of when certain things will happen are pretty far off.

I believe that we will not have really good diagnostic software until after the singularity and until after we can ensure that healthcare data is reliable. I actually spend most of my time on the second problem, which is really a sociological problem rather then a technology problem.

Imagine if we had a "House AI" before we were able to feed it reliable data? Ironically it would be very much like the character on TV: constantly annoyed that everyone around him keeps screwing up and getting in his way.

Anyone who has seen the show knows that the House character is constantly trying to convince the other characters that the patients are lying. The reality is that the best diagnosticians typically assume that the chart is lying before they assume that the patient is lying. With notable exceptions, the typical patient is highly motivated to get a good diagnosis and is, therefore, honest. The chart, on the other hand, be it paper or digital, has no motivation whatsoever, and it will happily mix in false lab reports and record inane diagnoses from previous visits.

The average doctor doubts the patient chart but trusts the patient story. For the foreseeable future, that is going to work much better than an algorithmically focused approach.

Eventually, Khosla's version of the future (which is typical of forward-thinking geeks in health IT) will certainly happen, but I think it is still 30 years away. The technology will be ready far earlier. Our screwed up incentive systems and backward corporate politics will be holding us back. I hardly have to make this argument, however, since Hugo Campos recently made it so well.

Eventually, people will get better care from AI. For now, we should keep the algorithms focused on the data that we know is good and keep the doctors focused on the patients. We should be worried about making patient data accurate and reliable.

I promise you we will have the AI problem finished long before we have healthcare data that is reliable enough to train it.

Until that happens, imagine how Watson would have performed on "Jeopardy" if it had been trained on "Lord of the Rings" and "The Cat in the Hat" instead of encyclopedias. Until we have healthcare data that is more reliable than "The Cat in the Hat," I will keep my doctor, and you can keep your algorithms, thank you very much.

Meaningful Use and Beyond: A Guide for IT Staff in Health Care — Meaningful Use underlies a major federal incentives program for medical offices and hospitals that pays doctors and clinicians to move to electronic health records (EHR). This book is a rosetta stone for the IT implementer who wants to help organizations harness EHR systems.

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