2. Cleveland
Clinic
 1300
bed
main
hospital
 9
Regional
Hospitals
 54,000
admissions,
2
million
visits
 Group
practice
of
2700
salaried
physicians
and
scientists
 3000+
research
projects
 Innovative
Medical
School
 30
spin
off
companies
 Office
of
Patient
Experience

3. Lethal
Lag
Time
 It
takes
an
average
of
17
years
to
implement
clinical
research
results
into
daily
practice
 Unacceptable
to
patients
 Can
Electronic
Medical
Records
and
Clinical
Decision
Support
Systems
change
this?

4. Electronic
Medical
Records
 Comprehensive
medical
information
 Images
 Communication
with
other
physicians,
medical
professionals
 Communication
with
patients
 3
million
active
patients,
10
years

5. EMR
Inputs
and
Outputs
Inputs
EMR
Tools
Outputs
• Clinical
• Alerts
Secondary
Use
• Labs
• Best
practices
• Data
sets
• Devices
• Smart
sets
• Registries
• Remote
monitoring
• Workflow
• Quality
reports
• Pt
outcomes
• Communication
to
• Omics
other
providers,
• Social
media?
patients

6. Clinical
Workflow
Workflow

7. Clinical
Decision
Support
 Process
for
enhancing
health-­‐related
decisions
and
actions
with
pertinent,
organized
clinical
knowledge
and
patient
information
 to
improve
health
and
healthcare
delivery.
 Information
recipients
can
include
patients,
clinicians
and
others
involved
in
patient
care
delivery
http://www.himss.org/ASP/
topics_clinicalDecision.asp

8. Like a GPS, CDS supplies
information tailored to the current
situation, and organized for
maximum value.

9. Diagnostic
Cockpit

10. CDS
Example:
Order
Sets

11. CDS
as
a
Strategic
Tool
• CDS
should
be
used
as
a
strategic
tool
for
achieving
an
organization’s
priority
care
delivery
objectives.
•
These
objectives
are
driven
by
external
forces
such
as
• payment
models
• regulations
related
to
improving
care
quality
and
safety
• internal
needs
for
improving
quality
and
patient
safety
•
reducing
medical
errors
• increasing
efficiency

12. EMR
Alert
Types
Clinical
Decision
Support
Target Area of Care
Example
Preventive care
Immunization, screening, disease management
guidelines for secondary prevention
Diagnosis
Suggestions for possible diagnoses that match a
patient’s signs and symptoms
Planning or implementing Treatment guidelines for specific diagnoses, drug
treatment
dosage recommendations, alerts for drug-drug
interactions
Followup management
Corollary orders, reminders for drug adverse event
monitoring
Hospital, provider efficiency
Care plans to minimize length of stay, order sets
Cost reductions and improved Duplicate testing alerts, drug formulary guidelines
patient convenience

13. Clinical
Decision
Support
Examples
 New
diagnosis
of
Rheumatoid
Arthritis,
prompted
to
refer
to
preventive
cardiology

14. Clinical
Decision
Support
Examples
 Age
>
50
and
a
fragile
fracture
diagnosis
–
order
set
for
bone
density
scan
and
appropriate
medication
regimen
 Go
to
Smart
Set

15. Clinical
Decision
Support
Examples
 Solid
organ
transplant
–
chemoprevention
for
skin
cancer

16. The
CDS
Toolbox
(more
examples)
 Drug-­‐Drug
Interactions
 Rules
to
meet
strategic
 Drug-­‐Allergy
interactions
objectives
(core
measures,
antibiotic
usage,
blood
 Dose
Range
Checking
management)
 Standardized
evidence
based
 Documentation
templates
ordersets
 Relevant
data
displays
 Links
to
knowledge
references
 Point
of
care
reference
 Links
to
local
policies
information
(i.e.
InfoButtons)
 Web
based
reference
information
 Diagnostic
decision
support
tools

17. Virtuous
Cycle
of
Clinical
Decision
Support
Registry
Measure
Practice
Guideline
CDS
http://www2.eerp.usp.br/Nepien/DisponibilizarArquivos/tomada_de_decis%C3%A3o.pdf

19. EMRs
and
Quality
of
Care

20. EMR
and
Quality
of
Care
 Diabetes
care
was
35.1
percentage
points
higher
at
EHR
sites
than
at
paper-­‐based
sites
 Standards
for
outcomes
was
15.2
percentage
points
higher

Across
all
insurance
types,
EHR
sites
were
associated
with
significantly
higher
achievement
of
care
and
outcome
standards
and
greater
improvement
in
diabetes
care
 Better
Health
Greater
Cleveland

21. Meaningful
Use

22. The
Role
of
Registries
 EMR
data
available
to
create
a
registry
for
any
condition
 Study
the
condition
–
progression,
treatments
 Comparative
effectiveness
of
treatments
 Recruit
for
clinical
trials
 Develop
clinical
decision
support

23. Chronic
Kidney
Disease
Registry
 Chronic
Kidney
Disease
Registry
 Established
2009
 60,000
patients
from
the
health
system
 Cohort
–
Adults
with
two
eGFRs
less
than
60
within
3
months,
outpatient
results
only,
or
diagnosis
of
CKD
 http://www.chrp.org/pdf/
HSR_12022011_Slides.pdf

24. Validation
Results
 Our
dataset’s
agreement
with
EHR-­‐extracted
data
for
documentation
of
the
presence
and
absence
of
comorbid
conditions,
ranged
from
substantial
to
near
perfect
agreement.
 Hypertension
and
coronary
artery
disease
were
exceptions
 EMR
data
accurate
for
research
use

25. Registry
Results
 2011
 5
out
of
5
abstracts
accepted
to
American
Society
of
Nephrology
annual
meeting
 Three
papers
accepted
to
nephrology
journals
 NIH
grant
 Partnerships
with
other
research
centers

26. Pediatric
Surgical
Site
Infection
 Data
from
the
EMR
and
the
operative
record
 When
did
antibiotics
start?
 Was
pre-­‐op
skin
prep
done?
 Was
the
time-­‐out
and
checklist
observed
in
the
OR
 Post-­‐op
care
quality

27. Patient
Reported
Outcomes
 Understanding
the
outcomes
of
treatment
incomplete
without
 Patient
Reported
Outcomes
Measurement
Information
System
http://www.nihpromis.org/
 Patient-­‐Centered
Outcomes
Research
Institute
http://www.pcori.org/

28. Patient
Reported
Outcomes
 Quality
of
life
 Activities
of
daily
living
 Recording
weight,
diet,
exercise
using
apps
 Quantified
Self

29. Population
Health
 New
tools
to
enable
the
study
of
disease
trends
and
epidemics
 PopHealth
-­‐
submission
of
quality
measures
to
public
health
organizations
http://projectpophealth.org
 Query
Health
–
standards
to
enable
Distributed
Health
Queries
http://wiki.siframework.org/Query+Health

30. Predictive
Models
 Predicting
6-­‐Year
Mortality
Risk
in
Patients
With
Type
2
Diabetes
 Cohort
of
33,067
patients
with
type
2
diabetes
identified
in
the
Cleveland
EMR
 Prediction
tool
created
in
this
study
was
accurate
in
predicting
6-­‐year
mortality
risk
among
patients
with
type
2
diabetes
 Diabetes
Care
December
2008,
vol.
31
no.
12:
2301-­‐2306

31. Postoperative nomogram based on 996 patients treated at The Methodist Hospital, Houston,
TX, for predicting PSA recurrence after radical prostatectomy.
Nomograms
bring
into
visual
perspective
the
effect
exerted
by
continuous
variables
against
measured
end
points
Kattan M W et al. JCO 1999;17:1499-1499
©1999 by American Society of Clinical Oncology

32. Risk
Calculators
Type
2
Diabetes
Predicting
6-­‐Year
Mortality
Risk

33. Algorithms
clevelandclinicmeded.com/
medicalpubs/micu/

34. Against
Diagnosis
 The
act
of
diagnosis
requires
that
patients
be
placed
in
a
binary
category
of
either
having
or
not
having
a
certain
disease.

These
cut-­‐points
do
not
adequately
reflect
disease
biology,
may
inappropriately
treat
patients

Risk
prediction
as
an
alternative
to
diagnosis
 Patient
risk
factors
(blood
pressure,
age)
are
combined
into
a
single
statistical
model
(risk
for
a
cardiovascular
event
within
10
years)
and
the
results
are
used
in
shared
decision
making
about
possible
treatments.
 Annals
of
Internal
Medicine,
August
5,
2008vol.
149
no.
3
200-­‐203

35. Information
Overload
 New
information
in
the
 Information
about
an
medical
literature
individual
patient
 PubMed
adding
over
 Lab
results
670,000
new
entries
per
 Vitals
year
 Imaging
 Genomics

36. Personalized
Medicine

The
boundaries
are
fading
between
basic
research
and
the
clinical
applications
of
systems
biology
and
proteomics
 New
therapeutic
models

Journal
of
Proteome
Research
Vol.
3,
No.
2,
2004,
179-­‐196.

37. Example–Parkinson’s
Disease
 New
Cleveland
Clinic
partnership
with
23andMe
to
collect
DNA
from
Parkinson’s
patients
 Looking
for
Genome
Wide
Associations
(GWAS)
 23andme.com/pd/

39. Precision
Medicine

”state-­‐of-­‐the-­‐art
molecular
profiling
to
create
diagnostic,
prognostic,
and
therapeutic
strategies
precisely
tailored
to
each
patient's
requirements.”

”The
success
of
precision
medicine
will
depend
on
establishing
frameworks
for
…
interpreting
the
influx
of
information
that
can
keep
pace
with
rapid
scientific
developments.”
 N
Engl
J
Med
2012;
366:489-­‐491,
2/
9/2012

40. Artificial
Intelligence
in
Medicine
 Developing
a
search
engine
that
will
scan
thousands
of
medical
records
to
turn
up
documents
related
to
patient
queries.
 Learn
based
on
how
it
is
used
 “We
are
not
contemplating
―
unless
this
were
an
unbelievably
fantastic
success
―
letting
a
machine
practice
medicine.”
 http://www.health2news.com/
2012/02/10/the-­‐national-­‐library-­‐of-­‐
medicine-­‐explores-­‐a-­‐i/

41. IBM
Watson
 Medical
records,
texts,
journals
and
research
documents
are
all
written
in
natural
language
–
a
language
that
computers
traditionally
struggle
to
understand.
A
system
that
instantly
delivers
a
single,
precise
answer
from
these
documents
could
transform
the
healthcare
industry.
 “This
is
no
longer
a
game”
 http://tinyurl.com/3b8y8os

42. Digital
Humans
Convergence
of:
 Genomics
 Social
media
 mHealth
 Rebooting
Clinical
Trials

43. Conclusion
-­‐
1
 EMR
as
the
platform
for
the
future
of
medicine
 Data
incoming
 Clinical
 Patient
Reported
 Genomic
 Proteomic
 Home
monitoring

44. Conclusion
-­‐
2
 Exploit
all
uses
of
the
EMR
to
 Improve
practice
efficiency
 Ensure
patient
safety
 Learn
about
your
patients
(registries)
 Compare
treatments
 Engage
with
patients

45. Conclusion
-­‐
3
 Understand
Personalized
and
Precision
medicine
 How
will
we
integrate
genomic
data
in
clinical
practice
in
the
future?

46. Conclusion
-­‐
4
 Predictive
models
inform
care
 How
do
we
integrate
these
into
practice
in
the
EMR?

47. Conclusion
-­‐
5
 How
can
we
reduce
the
lethal
lag
time?
 Getting
medical
findings
into
practice
more
rapidly
 How
can
we
engage
patients?
 Real
time
data
on
populations
 New
technology
for
Big
Data
in
health
care

48. Contact
me
 @JohnSharp
 Ehealth.johnwsharp.com
 Linkedin.com/in/johnsharp
 Slideshare.net/johnsharp
______________________
 ClevelandClinic.org
 @ClevelandClinic
 Facebook.com/ClevelandClinic
 youtube.com/clevelandclinic