(upbeat music)
- Hello, my name is Samuel Sayer from the MITRE Corporation
and I'm here to talk to you today
about how we can use
HL7 FHIR to reduce the burden of quality measurement
while still increasing the value.
2018, MITRE delivered an eCQM strategy report to CMS.
This report had a number of recommendations
about increasing value and reducing burden.
But specifically today I am gonna be talking mostly
about data standards and most specifically
about the FHIR standard and the promise it holds
to increase the quality of care
and to reduce the burden on our providers.
When we interview people for the eCQM strategy projects,
one of the issues they noted was the difficulty in mapping
data from their own electronic health records
to the quality data model.
The quality data model is only used for quality measurement
and because this is only used during this process
people do not find value in doing this work
only for quality measurement.
Because it is only used in quality measurement,
additional work needs to be done by clinicians
in order to capture this data
as a part of the clinical workflow.
This adds additional burden on providers.
To address these issues FHIR has a number of benefits.
It simplifies this data mapping process that many find
onerous under the current QRDA-based architecture.
It improves the align with clinical practice
to ensure that the clinical workflow
is not overly burdensome.
It streamlines the reporting process
so that you can interact with your quality data,
you can get your quality measurement,
and you can find value in the quality measurement process.
Last but not least, this promotes interoperability.
We have adopted standards that can interoperate with other
standards used across clinical care.
We no longer have a use case where technology is only used
for quality measurement and not in clinical care.
Quality measurement on FHIR uses a standard called
Quality Improvement Core or QI-Core.
QI-Core is how we represent clinical data elements in FHIR
that can also align to other standards,
such as what's called US Core.
US Core is the basis for most standards being developed
in the United States now.
This allows us to ensure that the clinical care is aligned
with quality measurement.
We also provide mappings
because we wanna unease the transition from QRDA to FHIR.
So we can map from the quality data model to QI-Core.
Now that we're moving to QI-Core
and we have aligned with other standards
that are used across clinical practice,
this allows us to also align with clinical decision support
which is already moving to a FHIR-based architecture.
By more closely aligning clinical decision support
and quality measurement
we can reduce the burden on providers,
increase the value of quality measurement,
and improve the accuracy of quality scores
that are calculated during the quality measurement process.
Because FHIR is based on an application programming
interface or an API,
it allows for an interactive quality measurement process,
rather than just submitting your data at one point
and then hoping that you get the scores that you want.
Throughout the year you can interact with this API.
You can calculate your quality scores.
You can submit additional data as it becomes available.
And you can also look and see
what is the data that I need to be submitting
in order to be successful with quality measurement.
Letting you know what data you need
and allowing you to look that up in your own applications,
allows you to ensure that you are confident
that you are submitting the data that you need to reflect
the quality of care that you were giving.
Next we'll cover how FHIR promotes interoperability.
We've already discussed how we align with clinical practice.
This is done by aligning with the standards that are used
for clinical practice.
What we have is really a pyramid,
where at the top we have the more specific use cases,
such as DEQM and then we have some more general use cases.
Like QI-Core which can be used for both clinical decision
support and it can be used for quality measurement.
Below that we have US Core, which is used generally
in all United States-based clinical care.
And below that we have FHIR
which is used all around the world
and has a very general application.
We have two different arrows here because we're showing
that as we introduce new data elements,
we can find consensus with the community
and elements that find consensus with the community
can then be promoted to those more general use cases.
And likewise, those consensus-based data elements
that are brought in from other use cases can then make
their way into our own more focused use cases.
And this allows us to ensure that all the data elements
that we are all interested in,
and then cross the various parts of care,
can then be used across the spectrum
and ensure that we are interoperable
across all these different use cases.
While FHIR hold a lot of promise
there's still a lot of work left to do.
We are currently in the pilot stage with FHIR,
testing out how we can use it to transmit quality data
from electronic health records
and from other providers to payers
and other quality measurement systems.
We are also investigating how we can adapt current measures
to use the FHIR based architecture
and how we can continue to refine these standards
to make sure that we are raising value
and reducing the burden of quality measurement.
Thank you everyone for joining me today.
I'd also like to thank my colleague
Floyd Eisenburg from ESAC
who initially collaborated with me on this content
that we created for Hims.
Again, my name is Samuel Sayer
and thank you everyone for listening to me today.
(upbeat music)