Table of Contents

Summary

I wouldn’t recommend this for beach reading, but that’s hardly the point. Hacking Healthcare provides a really concise, if slightly outdated look at what it means to work in IT with health data. Unfortunately, I’ve come away pretty underwhelmed at the impact that open source software has had in healthcare. While many industries seem to be going full bore into software built by collaboration, with support provided by for-pay companies, healthcare still seems dominated by proprietary software. What this book shows, is largely why that is so.

Between ontologies (specific words coded for reference), HIPPA-compliance, and the general un-sexiness of health care data, it is amazing anyone at all spends their time trying to make doctor’s lives easier. And even more challenging, a high percentage of docs are well-educated and just technical enough to think they know the best way to solve a problem. That leads to very inflexible ways of thinking, for better or worse.

There’s also the reality that paper really does work very well for healthcare. Before you replace something, you have to understand the value of it, and the value of a patient chart is huge. Doctors and nurses have shorthand; they can scribble in text when checkboxes don’t provide the context they need; they can write down three different possible ways to diagnosis someone and come back later to review their notes, to ensure codes in bills match patient conditions. It should come as no surprise then, that computer-aided solutions often fall flat.

Recently I spent an afternoon in the ER. The RN on duty was quietly cursing under her breath as she struggled with an electronic health record input that required her to code everything. Going back to ontologies, that means that when she used rubbing alcohol to clean off my scrapes, that required her to lookup and specify ICD10 diagnosis code S40.212A, “Abrasion of left shoulder, initial encounter.” What the fuck? No wonder she was frustrated. Nevermind if it turned out to be my right shoulder but she was tired from being on the end of a ten hour shift.

And of course, this doesn’t even get into the politics of ontology. The AMA maintains it’s own list of DX codes, but they charge money for access to them. Meanwhile, there’s also LOINC, a free standard, but which is not accepted by all insurers. And of course, Medicare and Medicaid have their own standard which maps, roughly, to ICD10 and LOINC. BUt don’t forget Snowmed … sigh.

And that’s just ontologies. There’s also the simple matter of what IS a patient chart? Is it just the patient? What happens when a patient changes their name? Should we assign an ID to the patient? But how do we track the ID across the various systems they might travel through when they are referred?

What about billing? Oh my. Let’s not even start with that.

Suffice it to say, reading this book was mostly humbling. I will likely return to it as a reference in the future, as I mostly skim read it this time. But it is a fantastic overview of the state of healthcare IT from 2015.

Notes

The VistA effect, as explained in Longman’s book about the VA, is where the quality healthcare outcomes are continuously measured to enforce higher levels of patient safety and care.

This hinges on “meaningful use” measurements, which include:

  • Demographics
  • medication lists
  • problem lists
  • vital signs

These are trivial for a clinician to understand, but very difficult to model in software.

Given how difficult some of these problems are to reason about (is Fred Trotter and Frederick Trotter the same person?), are there opportunities, as a forward thinking healthcare problem solver to open source certain tools that make expunging HIPPA data easier? Or perhaps to rectify demographic information? Can we, while still making money and not tipping our hand too much, help those who are technical to advance the state of the art in healthcare IT?