Are electronic medical records really about data?

Having spent my morning at the doctor’s office (I’m fine, Mom–just a physical), I passed the time by asking my doctor about the system they use for electronic medical records. Our GE work (1, 2) and seeing her gripe and sigh as truly awful-looking screen after screen flew past on her display caught my interest. And as someone who has an odd fascination with bad interfaces, I just had to ask…

Perhaps the most surprising bit was that without explicitly saying so, she seemed to find the EMR system most useful not as a thing that aggregates data, or makes her work easier, but instead as a communication tool. It combats the (very real, not just an overused joke) penmanship issues of fellow doctors, but equally as important, it sets a baseline or common framework for the details of a visit. The latter part is obvious, but the actual nature of it is more subtle. For instance, she would often find herself deciphering a scribble that says “throat, amox” by another doctor, and it says nothing of dosage, frequency, type of Amoxicillin, much less the nature of the throat trouble. A patient (particularly a sick patient) is also not the person to provide precise details. How many would remember whether they were assigned a 50, 150 or 500 milligram dosage (very different things, you might say). And for that matter, they’re probably equally likely to think they’re on a 500 kilogram dose. (“No, that’s too high. Must be 5 kilogram.”)

My doctor might be seeing such a patient because their primary care doctor (the mad scribbler) was out, or the patient was a referral, or had just moved offices, or whatever. But it makes an interesting point for the transience of medical data: importance increases as it’s in motion, which is especially true since the patient it’s attached to is not a static entity (from changing health conditions to changing jobs, cities, and doctors).

Or from a simpler angle, if you’re sick enough that you have to be seen by someone other than your primary care doctor, then it’s especially important for the information to be complete. So with any luck, the EMR removes a layer of translation that was required before.

As she described things off the top of her head, the data only came up later. Ok, it’s all data, but I’m referring to the numbers and the tests and the things that can be tracked easily over time. The sort of reduce-the-patient-to-numbers things we usually think of when hearing about EMRs. Readouts that display an array of tests, such as blood pressure history, is an important feature, but it wasn’t the killer app of EMRs. (And that will be the last time I use “killer app” and “electronic medical records” together. Pun not intended.)

The biggest downside (she’s now using her second system) is that the interfaces are terrible, usually that they do things in the wrong order, or require several windows and multiple clicks to do mundane tasks. She said there were several things that she liked and hated about this one, but that it was a completely different set of pros/cons from the other system she used. (And to over-analyze for a moment, I think she even said “like” and “hate” not “love” and “hate” or “like” and “dislike”. She also absentmindedly mentioned “this computer is going to kill me.” She’s not a whiner, and may truly believe it. EMRs may be killing our doctors! Call The New York Times, or at least Fox 25.) This isn’t surprising, I assume it’s just that technology purchasers are several levels removed from the doctors who have to use the equipment, which is usually the case for software systems like this, so there’s little market pressure for usability. If you’re big enough to need such a beast, then it means that the person making the decision about what to buy is a long ways removed. But I’m curious about whether this is a necessity of how big software is implemented, or a market opportunity.

At some point she also stated that it would be great if the software company had asked a doctor for their input in how the system was implemented. I think it’s safe to assume that there was at least one M.D.–if not an arsenal of individuals with a whole collection of alphabet soup trailing their names–who were involved with the software. But I was struck with how matter-of-fact she was that nobody had even thought about it. The software was that bad, and to her, the flaws were that obvious. The process by which she was forced to travel through the interface had little to do with the way she worked. Now, for any expert, they might have their own way of doing things, but that’s probably not the discrepancy here. (And in fact, if the differences between doctors are that great, then that itself should be part of the software: the doctor needs to be able to change the order in which the software works.) But it’s worth noting that the data (again, meaning the numbers and test history and easily measurable things) were all easily accessible from the interface, which suggests that like so many data-oriented projects, the numbers seduced the implementors. And so those concrete numbers (fourth or so on ranked importance for this doctor) won out over process (the way the doctor spends their day, and their time with the patient).

All of which is a long way of wondering, “are electronic medical records really about data?”

Monday, October 5, 2009 | healthcare, interact, thisneedsfixed  

Visualizing Data Book CoverVisualizing Data is my book about computational information design. It covers the path from raw data to how we understand it, detailing how to begin with a set of numbers and produce images or software that lets you view and interact with information. Unlike nearly all books in this field, it is a hands-on guide intended for people who want to learn how to actually build a data visualization.

The text was published by O’Reilly in December 2007 and can be found at Amazon and elsewhere. Amazon also has an edition for the Kindle, for people who aren’t into the dead tree thing. (Proceeds from Amazon links found on this page are used to pay my web hosting bill.)

Examples for the book can be found here.

The book covers ideas found in my Ph.D. dissertation, which is basis for Chapter 1. The next chapter is an extremely brief introduction to Processing, which is used for the examples. Next is (chapter 3) is a simple mapping project to place data points on a map of the United States. Of course, the idea is not that lots of people want to visualize data for each of 50 states. Instead, it’s a jumping off point for learning how to lay out data spatially.

The chapters that follow cover six more projects, such as salary vs. performance (Chapter 5), zipdecode (Chapter 6), followed by more advanced topics dealing with trees, treemaps, hierarchies, and recursion (Chapter 7), plus graphs and networks (Chapter 8).

This site is used for follow-up code and writing about related topics.