Archetype Query Language, The Confusing Bits, II

This is part 2 of a series of posts discussing the particulars and as the title says, confusing bits of AQL. Part 1 is here. I’m hoping to discuss what choices implementers of clinical data query language designers have and the implications of such choices.

AND operator takes the stage

So let’s talk a bit more about the FROM clause, and the query semantics it may express. Let’s take the previous simple AQL query from part 1 and extend it a bit. First, the simplest form I used:

    EHR E

Let’s assume that instead of the above query, our user is interested in fetching data related to a scenario in which a clinician observes some clinical condition, then instructs something. Our user is therefore looking for data that’ll be in a COMPOSITION that contains an OBSERVATION and an INSTRUCTION:. Apologies if you’re a clinical modeller and I just butchered my way through openEHR modelling, you’ll have to live with it for the moment.

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Archetype Query Language, The Confusing Bits

AQL is one of the most clever things openEHR offers: a query language that allows users to access data they’re interested in, using the elements of openEHR reference model. Its primary author is Chunlan Ma, a real veteran of health IT, who has been a cornerstone of Ocean Informatics (Ocean Health Systems) for many years now. Heath Frankel is the other person from Ocean who made AQL possible.

It has a specification that explains its syntax, and how you can use it. Well, more or less. In case you have not seen it yet, Google is your friend.

As other openEHR vendors began to implement and market their platforms, AQL became a frequently used tool in both developing applications and analysing data. There is a lot to say about domain specific query languages, but I won’t digress, at least for the moment. I’d like to stick to some problems users (and even implementers) may find confusing and discuss the nature of the confusion.

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openEHR for policymakers

As of end of 2018, I’ve been working on openEHR for almost 15 years, beginning with my exposure to openEHR archetypes during a European Union research project, around 2003 or so.

During these fifteen years, I tried to explain my (sometimes incorrect at the time) understanding of openEHR to many people who occupied various positions: junior software developers, product managers, general managers, investors, academics, ministers of health, marketing professionals. It would be a long list.

Looking back, I can see that I have not been able to articulate some key points clearly when I was talking to policy makers. That is, people who get to cast a vote or make a decision when it comes to choosing how to use technology in healthcare.

This post is an attempt to focus on aspects of openEHR that are relevant to policy makers, but it should be of interest to many people in other roles since we’re all affected by the decisions of policy makers as patients, if not as people in other roles in healthcare(IT).Read More »

A discussion about Archetype Query Language semantics

This is a copy/paste of a few responses I sent to a discussion in the openEHR lists. I’m copying them here because images in my responses and responses themselves are not properly archived anywhere yet.

If you want more: I wrote a PhD thesis on this stuff, so if you want a deeper discussion of the topic here it is but I suggest you read the following first.

Here is the whole exchange from openEHR mail lists, with all responses, including mine:Read More »

Why US will have better clinical decision support than the EU

For many, smarter healthcare through the use of computers is an exciting idea. This has been the case since the sixties, and I belong to the current generation of people who try to make this happen.

There are so many misunderstood things about making computers help clinicians. It would take a lot of space to discuss these things, a luxury neither the blogger nor the reader can afford, so let’s stick to a key component of making CDS happen: data.

One form of CDS that benefits from data is based on building mathematical models using the data, then using these models to assess clinical scenarios. The more data you have, the more robust and accurate your decision making models are. This is why the big data hype is acting similar to rents in London: there appears to be no limit to its rise. Big data is basically breaking very large amounts of data that would not possibly fit into a single computer to smaller chunks and process it using hundreds and if necessary thousands of computers. It is not conceptually new, but it became easier and cheaper in the last decade. With this improved approach to processing larger data, the possibility of better decision making models arise, and (some) clinicians, vendors and investors begin to think: “This is it! We’ll now be able to have sci-fi smart computers”. Not so easy.Read More »