Welcome to this webinar titled CQL for Clinicians and Quality Professionals.
All lines have been muted to prevent any background noise.
After each speaker's remarks, there will be a brief question and answer session.
I'll now turn the floor over to Shanna Hartman. Please go ahead.
Thank you and thanks everyone for joining our webinar today on clinical quality language for clinicians and quality professionals
what it means for you. I'll be presenting today along with Floyd Eisenberg.
Topics for today's session, we're going to discuss what is Clinical Quality Language or CQL
how does CQL help explain electronic clinical quality measure eCQM math, the importance of eliminating ambiguity
CQL and eCQMs, what does the change mean for you, tools and resources, and an opportunity for questions and answers.
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Here are some acronyms that will be used during today's presentation.
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What is CQL? Next slide, please.
CQL is a Health Level Seven HL7 International standard that aims to unify the expression of logic for eCQMs and Clinical Decision Support.
HL7 is a standards developing organization that provides a framework and standards for the exchange, integration, sharing,
and retrieval of electronic health information that supports clinical practice and the management, delivery and evaluation of health services.
CQL provides the ability to better express logic defining measure population, to improve the accuracy and clarity of eCQMs.
It is the standard language for expressing clinical knowledge that is readable, shareable and computable.
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eCQMs will be transitioned to use the clinical quality language standard
for logic expression beginning with the calendar year 2019 reporting period.
Measure developers have successfully tested CQL for expressing eCQMs from 2016 through 2017.
CMS will publish CQL-based eCQMs in spring 2018.
This is applicable to eligible hospitals and critical access hospitals,
eligible professionals and eligible clinicians participating in the following programs:
The Hospital Inpatient Quality Reporting Program (IQR),
the Medicare Electronic Health Record Incentive Program for Eligible Hospitals and Critical Access Hospitals,
the Medicaid EHR Incentive Program for Eligible Professionals, Eligible Hospitals, and Critical Access Hospitals,
as well as the Quality Payment Program, the Merit-based Incentive Payment System, and Alternative Payment Models.
CQL is a language that can be used to share precise machine readable as well as human readable measure definitions.
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There are different methods used by electronic health record vendors to manage logic within their software.
These include Native or their own language, JavaScript, .Net, or Structured Query Language, SQL.
The EHR implementer can directly translate the ELM into their own method for providing logic.
Quality measure calculation systems will either directly evaluate the ELM or translate it into other formats such as JavaScript, .Net or SQL.
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And at this time I'm going to hand the presentation over to Floyd.
Great, thank you very much, Shanna.
So, let’s start with how does CQL help to explain the electronic clinical quality measure mathematics.
So, let's move to the next slide.
What we'll start with is, let’s just go back to a basic definition of what do we mean by a quality measure.
It's a quantitative tool that assesses performance, clinical performance related to a specific clinical process or outcome.
An eCQM is an electronic representation of a quality measure
with the goal of enabling the measure to be evaluated as automatically as possible
retrieving data directly from the clinical software and calculating it.
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So, let's consider a guideline is analogous to a recipe. So, let's be cautious that I'm not indicating anything about cookbook medicine
just indicating when you, when you have a recipe, what are the components of what you have to think about?
So, there's suggestions for common considerations,
There are indications of what to avoid, allowances for variation
and so a performance measure is basically assessing adherence to a guideline, accounting for variation, exclusions and exceptions.
So, let's move to the next slide.
And we'll start with just the components of a recipe to try to be very high level here. Let's look at eggs benedict.
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So, in a recipe, there's general information about it. Where it comes from, there's number of servings, prep time.
What units it uses, calories, there’s a list of ingredients and from those ingredients you're going to develop a shopping list.
So, there, these are the things you need to make sure you can make the recipe.
If I were looking at a measure, I would say there are data elements and those data elements have to map to what you have locally.
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So, if I see if I could do that, uh, it seems OK, the next slide here is, there's also instructions. So, once you have your ingredients
it's telling you how to put the things together to make the, the item you're trying to make.
So, it's putting them in order, how long it takes to do the, each step within the recipe and come out with an output.
And, there's often some missing information. If I look at the instructions, the ingredients, it doesn't describe a couple of contents.
It assumes you know what they are, it has instructions that you assume that you know what they are
and it has components like here the hollandaise sauce, which you might have different ways of making it.
So, there are variations that could still provide a valuable output, and still get to the point of having something
that- that produces what a customer would look for or an outcome.
So, next slide.
Let's just think about the ambiguity that might happen. And I'm going to give one example on the next slide here.
If you look at XL and think about it, what does XL mean to you? And this is not related to the recipe, but
if I were thinking about Latin, they could just be two different consonants.
If I was thinking about Roman numerals, it might be the number forty.
If I were thinking of clothing, I might say it's extra-large. So, ambiguity in terms is problematic.
Well, we see that in measures as well and that's why we have clear, clearer definitions and we want to make sure things are properly stated.
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So, let's now take a guideline in the same concept when we talked about the recipe.
There's general information, we call metadata, or the header, the table at the top of the measure that's telling you
what's the name of the measure and the one example here is children with evidence of receiving recommended vaccines
or having it documented clinical history or serologic evidence of the illness the vaccine is intended to prevent.
Meaning every child under age two has either immunity or appropriate vaccinations for that age group.
Who did it come from, who created it, the measure steward, the measurement period, and background.
Then there are data elements. So, I need to know children aged two and under- or actually age two during the measurement year
I need to know specific vaccines and there are just a few examples here.
What is the Diphtheria Tetanus, acellular Pertussis combination vaccine, Measles Mumps Rubella, Varicella vaccine,
Varicella diagnosis. So, there's a whole list of different data elements and they are identified by value sets
so you can identify what the right elements are. And that helps to define what you need.
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So, once you have all that combined, you need instructions. The instructions are basically telling you, using an expression language
what is meant by DTaP vaccination and MMR vaccination, Varicella or clinical diagnosis or serologic diagnosis
and how do you add it all up so that you can come up with an answer and assure that there is no missing information
so that if you are looking for a vaccination that was performed that would use one kind of code, it’s a procedure
or a vaccination that was administered, and that would look for the vaccination code in the administration record.
So, where do you find the information and a measure has in it the content to allow for some local variation.
In some cases, you won’t have the vaccine record in the EHR, so you’ll have the procedure was performed
and in others, you’ll have both.
So, the measure is designed to allow both but it’s the calculation of it that we need to talk about next, so next slide.
So, what we do is we use CQL, Clinical Quality Language, to describe that calculation.
What does it do? It lets you define the exact time relationships needed.
Are the data elements separated by seconds, minutes, days or hours?
How can you identify when something starts and when something ends?
In the previously used Quality Data Model logic, there was some ambiguities in time, some ambiguities in some of the details and
relationships and some complexities. CQL allows it to be much more precise and much clearer
and calculations in a format that is much more suitable for computer processing.
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So, it also- CQL allows for simple calculations stating basic addition, subtraction, multiplication
that wasn’t possible with the earlier logic expression from the QDM.
So, in an example if I wanted to, one of the measures was looking at change in the depression scale
over six months to see if it decreased. The previous logic wasn’t able to say the first minus the last is X amount of improvement, now it can.
So, another measure would be looking for 10ml of mercury minimum decrease in systolic blood pressure for a patient with hypertension
and an elevated blood pressure, systolic blood pressure. And in the past, the logic wasn’t able to state that
but the CQL is providing that. How would you calculate a low-density lipoprotein value
by subtracting the high-density lipoprotein from total cholesterol?
Again, that wasn’t allowable which is why you don’t see it in any of the existing eCQMs.
Another item was cumulative medication duration
to assure that the patient was on prescriptions covering at least 180 days of therapy.
In prior measures with QDM logic
that information was placed in guidance so someone would know what to do with their local data
but it wasn’t computable because it couldn’t be stated in logic.
So, these are new changes that CQL helps in addition to the timing issues making precision
for whether we’re talking about days, months, minutes and separating items. Next slide.
And, so given that we can state these things in a better way, what does it mean for you? Let’s go to the next slide.
So, what it means is, anytime something's new that you have to read does take a bit of a learning curve but once
you start looking at the CQL expressions
they are significantly more readable to a human eye than the previously provided QDM logic statements.
They are more direct, they’re more mathematical and clearly detail the intent of the measures.
They also better explain how the measure outcomes are to be interpreted.
So, to the human eye, they can be read more clearly and more easily. Next slide.
Example here- when I mentioned earlier the cumulative medication duration example, before all of the information was only in the guidance,
now in the logic within the measure with the CQL
you can see that if you’re looking at medication that was dispensed you can calculate
from the number of doses dispensed divided by the number of doses per day
and then add all dispensing events to come up with the total number of days covered
and that’s actually present in the logic so not only is it readable
but it’s computer processable because the information is in the logic.
And if you’re using administering versus dispensing, the calculation is a little different it’s the number of administration
events from the beginning of the first to the end of the last over the period of time.
So, these are much better provided today or can be with the CQL. Next slide.
And again, as far as the meaning is to you. They’re more accurately representing the performance
by allowing use of precise methods to define activities.
And improves the ability to read and interpret and understand the logic.
Enables more automation as Shanna was showing earlier, the CQL can be translated
relatively easily into a language that the local software can understand
to actually retrieve the data and calculate more directly.
It also allows for prospective evaluation of the patient’s record by allowing retrieval of data prospectively to see
who might be meeting the measure criteria and who might not to improve
either through clinical decision support or other workflow changes, improve performance in real time. Next slide.
So, by- the change also may impact your results. There’s been discussion that
the results based on eCQMs using the QDM logic have not been consistent with the expected performance
or the performance identified through direct chart review.
The expectation is that with more precise definition and more computability,
the data may allow more consistent results and allows you to work with your IT staff to help prospectively
improve your chances of doing well by understanding where data are coming from and being retrieved from
and to re-use the logic in the measures locally for clinical decision support to improve performance
also to understand whether there are any unexpected results of measure scores
and improving those by recommending new interventions and giving feedback. Next slide.
So, CMS will be releasing – Shanna, do you want to take this over again?
Sure, so CMS will be releasing CQL-based eCQMs for calendar year 2019
CMS quality reporting programs this spring 2018.
CMS will also be releasing guidance on “How to read an eCQM” with incorporation of Clinical Quality Language for logic.
And the draft measures are expected to be posted to the CQMs JIRA project today through 3/30/2018 for your review and public feedback.
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And then here is a list of resources that you can use for
finding additional information about the CQL specification or the CQL-based HQMF Implementation Guide.
The eCQI Resource Center at ecqi.healthit.gov/cql has a CQL space that includes information about clinical quality language
as well as eCQI Resource Center events and education on past clinical quality language presentations.
To submit an issue or question for CQL, you can visit the ONC JIRA site at the oncprojecttracking.healthit.gov
and the project is CQLIT.
And then to view the draft 2019 eCQMs, listservs did go out today but that is also on the CQM JIRA issue tracker.
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I'd like to thank you all for joining today's conference call. Until next time, you may all have a great day.