Category Archives: Big Data

Getting patients back to normal

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All opinions my own and do not necessarily reflect those of Novo Nordisk.

Inspired in part by this column from David Shaywitz

Here is a story I had the privilege to hear from Fred Modell, one of the founders of the Jeffrey Modell Foundation (check them out; they’re a great group):  Fred was at their annual picnic, where they host kids with immune system defects.  Fred walked by two early teenage girls, and as he passed by he heard one of them asking the other, “You really kissed a boy?”  Which seems like a common enough thing for two teenage girls to be talking about.

Only in this case it wasn’t.  If your immune system doesn’t function like most everyone else’s, then kissing a boy is not just part of growing up.  It can be dangerous to your health. It’s something  about which you have to think hard, and try your best to understand the implications, and you need to be careful, cautious and measured.  Everything your first kiss really shouldn’t be.

For these girls, though, because of groups like the Modell Foundation and the treatments they’ve helped pioneer and support, these girls could experience the spontaneity of an event that so many kids take for granted.  And they could feel normal, like their friends in school. Continue reading →

Fielding percentage for UK surgeons

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All opinions are my own and do not necessarily reflect those of Novo Nordisk.

Last week I posted on how our measurements of defense in baseball have become a lot more sophisticated, and how that gave me hope for the evaluation of innovation.  If baseball, one of the most tradition-bound of US sports can adopt to new metrics, surely business can too.

I was reminded of this with the publication of a recent article about the National Health Service (NHS) in the United Kingdom and their plan to publicize the surgical success rates of clinicians across their country.  Surgeons in eight different specialities will have their mortality rates for specific procedures, length of hospital stays post surgery, and other elements published in tables for anyone to access.  The first group to have this information released is vascular surgeons.

A fascinating aspect of how this is being done is that publication of one’s rates is voluntary, but if a surgeon chooses not to have his or her rates published, that surgeon will be named.  It’s not quite putting people into stocks in the public square, but it is definitely a form of public shaming meant to increase participation.

Nevertheless, six surgeons have opted out and been named.  Game theory might predict these are surgeons on the low end of the measured metrics, who are taking a calculated risk that the negatives associated with not publishing their rates are less than the negatives that would come with disclosure of their rates.  But that’s not the case.  The NHS has stated that none of these surgeons lie outside the normal range for the reported metrics.

Instead, these doctors are protesting that the metrics are not measuring the right things.   They suggest the metrics don’t take into account the subtleties involved in surgical cases, how procedure names alone don’t properly capture how difficult or easy a procedure might be for a given patient.  Are there comorbidities?  Is a patient in generally poor health?  Is a surgeon one who specializes in tricky, difficult cases which would therefore lead to a lower success rate even though the surgeon him or herself might be highly skilled and effective?  Could these metrics scare new surgeons away from performing more difficult procedures?

This echoes the debate about defense in baseball, and whether standard metrics such as fielding percentage are the best for measuring defensive ability, or if more elaborate measures better reflect reality.

Still, while I agree with the viewpoint that we should always try to improve metrics, I also think the NHS is doing the right thing.  I think in this case the proper analogy might be baseball defense back at the time before the invention of fielding percentage.  In the practice of medicine world-wide there is a surprising lack of information about measures like success rates and efficacy.  As Sir Bruce Keogh said to the BBC: “This has been done nowhere else in the world, and I think it represents a very significant step.”  To take another quote from the article, Professor Ben Bridgewater commented, “We’ve been collecting data on cardiac surgery since 1996 and we’ve been publishing it at individual surgeon level since 2005, and what we’ve seen associated with that is big improvements in quality: the mortality rates in cardiac surgery today are about a third of what they were ten years ago.” That which we don’t measure, we can’t improve.

In the US, that idea is becoming more prominent.  Recent articles in Time and the New York Times have highlighted how transparency is lacking in the United States healthcare system, and the Obama Administration’s emphasis on comparative effectiveness is another thrust in that direction.  What the NHS is doing is a great model and a great start, and I hope they continue to both make these aspects of healthcare more transparent and work to refine their metrics so that they accurately reflect the difficulty of practicing good medicine.

Cheetahs hunting: a coda on sensors

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I really need to keep up with my back issues of Wired.  After posting about the possible use of portable sensors in sports to monitor defense and skills, I came across this feature  in Wired about different kinds of sensors, including one made by X2 Biosystems to measure head impacts.  This system uses a small, adhesive sensor, placed behind the athlete’s ear, to monitor the force of head impacts in real time.  This provides additional data that coaches and medical staff on the sideline can use in judging whether a player should be allowed back on the field or not after a blow to the head.

I’m curious how sensitive these sensors are and whether they could or do record less extreme events like rapid acceleration and deceleration while players run on the field.  The nice thing about the X2 system is that information is collected in an application that allows collation of health care provider information and clinical results as well, keeping data in one place.   I’m also curious if the general, anonymized data will be made publicly available.  According to the Wired article, a number of collegiate and amateur sports organizations are gathering data as part of a central initiative, presumably to monitor and prevent concussions.  It would be interesting to see if other things could be studied from that data.

Cheetahs hunting redux: the next step in measuring baseball defense?

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I had another thought about the collars that were used to measure cheetah hunting behaviors.  For a summary that is not behind a paywall, see here.  How long will it be before tools like these are used to measure baseball players, playing defense on the field?  Tools like FIELDf/x quantify the behavior of baseball players from an external viewpoint.  Sportvision’s cameras record elements of the game like positioning, how quickly a defender moves, the kind of jumps he takes when getting to (or missing) the ball, and overall range.  This allows a much clearer view of defender territory, ability to reach difficult balls, and general quality.

Now, what if that were combined with the kinds of tools that were used to measure cheetahs?  As the authors of the article point out, the collars they designed could record “some of the highest measured values for lateral and forward acceleration, deceleration and body-mass-specific power for any terrestrial mammal.”  If it can do that for cheetahs, it can certainly do that for Brendan Ryan and Mike Trout, much less Derek Jeter or Raul Ibanez.  By the way, this would obviously not be implemented as a collar.  You don’t have to drug and tag shortstops.  At least not for these purposes.

Instead, these monitoring devices would be attached to the body, and possibly in multiple places, to capture kinesthetics.  Now, one might say, can’t all this data just be captured by  image capture from the Sportvision feed, and algorithmically extracting things like acceleration, body positioning, etc?  Quite possibly; I don’t know enough about that technology.  But what about actions taken on fields which are not equipped with Sportvision cameras, which is to say, most of them?

That might end up being the sweet spot for implementing this technology, as an adjunct to training, coaching and scouting.  Being able to measure how quickly a high school shortstop actually reacts to the batted ball, based on his lateral acceleration and ability to accelerate/decelerate would provide a more proximal measure of athleticism when making scouting evaluations.  It can also allow quantification of both areas for improvement, as well as a measure of improvement during coaching.  And using these kinds of monitors can also help answer questions on what really is important for defense, based on a comparison of proximal, immediately measured body motions and more distal metrics such as are measured by things like UZR.

Like any of these kinds of quantified self tools, though, it remains to be seen how useful this extra data will be.  However, for the savvy organization at any level, I think these kinds of tools are worth thinking about.

Cheetahs hunting and the quantified self

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Who doesn’t love cheetahs?  A young person of my acquaintance went so far as to spend a large portion of her time, at a certain age, cavorting on all fours and yipping and chirping like a cheetah.  And of course we all know that cheetahs are the fastest land animals, and that’s how they catch their prey, by outrunning them.

Only that’s wrong.

Yes cheetahs are wicked fast, reaching about 60 miles per hour, but a recent report in Nature has shown, via novel monitoring techniques, that maneuverability and deceleration skills are the keys to successful hunting.  The researchers designed a new type of monitoring collar that included GPS and accelerometers.  No word on whether the collars also allowed cheetahs to play Words with Friends.

This report highlights the things we can learn as we get better and better at measuring.  Conventional wisdom may remain or be turned on its head, and either outcome is fine.  The key is that we have a better  basis upon which to understand that wisdom, that we don’t take things for granted, that we question our assumptions.

The cheetah collars also point to how we can gather so much more data on individuals, whether furry or bipedal (or both), than we ever could before.  I’ve recently been made aware of the quantified self movement (HT @bkolko), and what they hope to do is in line with what was done with these cheetahs.  Take individual monitoring and data gathering to new heights.  No, it won’t involve tracking collars (unless, you know, that’s your thing).  But it will involve using technology to measure what previously we could only guess at, and enable decision making and research in new and powerful ways.