A fully archetype-based openEHR clinical system covering a number of hospital departments in the Ljubljana Medical Centre. The experience in construction of this system and its ongoing development shows that the openEHR archetype, template and querying approach can change the rules of software engineering economics.
Married to standard IHE services, the overall system is proving extremely flexible and adaptable to the continuous stream of new requirements.
This presentation covers the experience and lessons from this system over its 2 year development lifecycle, and discusses how it can inform strategic thinking for EHR / CDR development in three key areas:
1. the use of openEHR archetypes and templates to flexibly and efficiently store and retrieve all clinical content
2. the power of the Archetype Query Language (AQL), it's use in clinical applications and decision support systems
3. a new approach to enhance the IHE ecosystem with content querying capabilities based on archetypes enabling answers to population queries
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OpenEHR and IHE Ecosystem
1. Changing
the
Rules:
the
openEHR
and
IHE
ecosystem
Tomaž
Gornik,
Vice
President,
Marand
Borut
Fabjan,
Senior
Architect,
Marand
www.marand.com/thinkmed
thinkmed@marand.si
2. Agenda
• Developing
software
with
openEHR
• Querying
the
EHR
• Using
openEHR
and
IHE
• Summary
T.
Gornik,
B.
Fabjan,
2012
2
4. University
Children’s
Hospital
Ljubljana,
Slovenia
• 210
bed
teaching/research
hospital
• All
pediatric
specialties
including
oncology,
surgery
and
PICU
• New,
state-‐of-‐the-‐art
facilities
and
equipment
• No
legacy
IT
system
• Motivated
staff
• 2
year
timeframe
T.
Gornik,
B.
Fabjan,
2012
4
5. Software
development
challenges
• Constant
change
in
– Information
– Care
Process
– Technology
– Patient
needs
– Legislation
Requires
a
new
approach
to
managing
clinical
data
T.
Gornik,
B.
Fabjan,
2012
5
7. openEHR
• Separation
of
content
and
technology
• Archetypes
-‐
Detailed
Clinical
Models
• Existing
archetypes
for
many
clinical
terms
• Templates
customize
data
set
for
each
use
case
T.
Gornik,
B.
Fabjan,
2012
7
10. The
architecture
java
Int’l
Nat’l
/
local
Nat’l
/
local
archetypes
archetypes
templates
Template-‐
C#
based
artefacts
etc
re
f
s
e
ts
terminology
canonical
Querying
openEHR
data
All
data
=
same
information
model
T.
Gornik,
B.
Fabjan,
2012
10
13. Lessons
learned
• Clinician
involvement
– Using
CKM
to
develop
archetypes/templates
– Produce
their
own
local
archetypes
– Much
easier
than
HL7
v3
T.
Gornik,
B.
Fabjan,
2012
13
14. Lessons
learned
• Faster
development
cycle
– Data
model,
GUI
• Flexibility
– Archetype
reuse,
versioning
• Generation
of
downstream
artefacts
• EHR
independent
of
application
(vendor)
T.
Gornik,
B.
Fabjan,
2012
14
24. IHE
–
Core
IT
Infrastructure
PIX/PDQ
Query
Pa*ent
Iden*ty
XRef
Mgr
A87631
M8354673993
PMS M8354673993
14355
Physician Office L-‐716
14355
L-‐716
A87631
ED Application
Document
Document Registry
PACS
Repository
Document
Repository
EHR System
Query
Document
Register
Document
(using
Pa*ent
ID) (using
Pa*ent
ID)
Provide &
Register
PACS
Retrieve Document
Maintain
Lab Info.
Time
System Maintain Teaching Hospital
Community Clinic Time
Maintain
Record Audit Time
Event Audit record repository
Time server
ATNA CT Record Audit Event
T.
Gornik,
B.
Fabjan,
2012
24
25. IHE
IHE
Profiles
are
NOT
an
architecture
• It
is
a
collection
of
architectural
components
• To
build
into
new
or
existing
systems
• To
aid
in
integration
T.
Gornik,
B.
Fabjan,
2012
25
26. IHE
Benefits
Shortcomings
• Querying
limited
to
document
• Integration
profiles
metadata
• Strong
industry
support
• Minimal
data-‐set
content
• Focused
on
document
Profiles
/
coarse
grained
data
sharing
• Mostly
CDA
L1/L2
• Aids
integration
• Non-‐computable
health
data
T.
Gornik,
B.
Fabjan,
2012
26
27. Simple
population
questions?
• How
many
patients
have
been
diagnosed
with
Sickle
Cell
disease
last
year?
• How
many
diabetes
patients
are
controlling
their
sugar?
• What
is
the
percentage
of
patients
with
high
BMI?
T.
Gornik,
B.
Fabjan,
2012
27
28. Semantic
underpinning
• What
to
use
as
standard
RM?
• Tried
HL7
RIM
• Decided
to
use
openEHR
–
template
generated
XML
– Semantically
consistent/validated
– Directly
transforms
into
archetypes
– Enables
querying
T.
Gornik,
B.
Fabjan,
2012
28
29. openEHR
&
IHE
can
coexist
• Benefits
– Archetypes
–
maximal
data
set
–
key
for
agreement
on
data
structures
– Use
Templates
to
generate
XML
structures
to
embed
in
CDA
L2/L3*
– Distributed
EHR
–
supports
the
federated
model
T.
Gornik,
B.
Fabjan,
2012
29
30. Solution?
• We
need
a
new
IHE
profile
– Addressing
content
query
needs
• Specifying
the
query
parameters
• Based
on
reference
model
– Quick-‐win:
IHE
On-‐Demand
extended
with
Query
parameters
• This
will
take
years
(at
least
two)
• What
can
we
do
immediately?
T.
Gornik,
B.
Fabjan,
2012
30
31. Solution?
• Define
CDA
template
based
on
OpenEHR
Template/
Archetype
• Leverage
IHE
Infrastructure
for
Document
Sharing
• Use
IHE
DSUB
to
subscribe
/
retrieve
“archetypical”
topics
• Use
IHE
On-‐Demand
for
access
to
dynamic
information
T.
Gornik,
B.
Fabjan,
2012
31
32. Example:
Child
health
screening
1. HC
provider
publishes
Pediatric
Screening
Note
in
CDA
L2/L3*
format
2. Public
Health
Authority
uses
DSUB
profile
to
be
notified
of
new
published
document
3. PHA
stores
the
document
in
openEHR
registry
and
registers/
replaces
Growth
Chart
Document
CDA
L2/L3*
• HC
provider
can
search
for
new
Growth
Chart
and
use
the
data
• PHA
can
query
the
registry
for
reporting
and
CDS
T.
Gornik,
B.
Fabjan,
2012
32
33. IHE
XDS
+
OpenEHR
+
DSUB
X.
Subscribe
to
document
metadata
of
Public
Health
DSUB.Broker
Authority
interest
DSUB.Publisher
6.
Search
for
“computed”
documents
3.
No*fica*on
on
new
XDS.Registry
document
availability
OpenEHR
2.
Registers
the
documents
metadata
and
pointer
with
the
4.
Retrieve
no*fied
Registry
document
from
HealthCare
Provider
Repository
(-‐ies)
1.
Sources
post
document
to
the
5.
Post
“computed”
Repository
Source
of
document
to
the
Documents
Repository
XDS.Repository
T.
Gornik,
B.
Fabjan,
2012
33
34. Next
step
• A
system
with
openEHR
data
about
a
patient
could
register
this
in
an
IHE
registry
• An
AQL
query
could
be
sent
out
to
those
sites
and
return
an
XML
result
set
• Other
IHE
profiles
such
as
Consent
can
be
used
T.
Gornik,
B.
Fabjan,
2012
34
35. Thinking
outside
the
box
Enhance
QED
with
support
for
openEHR?
T.
Gornik,
B.
Fabjan,
2012
35
36. CIMI
Group
led
by
Stan
Huff,
IMH
• Cambio
Healthcare
Systems
• Mayo
Clinic
• Canada
Health
Infoway/Inforoute
Santé
• MOH
Holdings
Singapore
Canada
• National
Institutes
of
Health
(USA)
• CDISC
• NHS
Connecting
for
Health
• Electronic
Record
Services
• Ocean
Informatics
• EN
13606
Association
• openEHR
Foundation
• GE
Healthcare
• HL7
• Results4Care
• IHTSDO
• SMART
• Intermountain
Healthcare
• South
Korea
Yonsei
University
• Kaiser
Permanente
• Veterans
Health
Administration
T.
Gornik,
B.
Fabjan,
2012
36
37. Summary
• openEHR
changes
software
development
economics
• AQL
offers
several
advantages
in
querying
EHR
data
• openEHR
and
IHE
are
complementary
– openEHR
provides
data,
context,
semantics,
querying
– IHE
provides
interoperability/infrastructure
T.
Gornik,
B.
Fabjan,
2012
37
38. Changing
the
Rules:
the
openEHR
and
IHE
ecosystem
Tomaž
Gornik,
Vice
President,
Marand
Borut
Fabjan,
Senior
Architect,
Marand
www.marand.com/thinkmed
thinkmed@marand.si