Translating Clinical Guidelines
into Knowledge-Guided
Decision SupportBlackford Middleton, MD, MPH, MSc, FACP, FACMI, FHIMSS
Corporate Director, Clinical Informatics Research & Development
Chairman, Center for Information Technology Leadership
Harvard Medical School
Overview
• The Evidence for CDS
• The Value Potential of CDS
• Current examples and R&D Projects
• The Clinical Decision Support Consortium
Flexner Report
Abraham Flexner,
Medical Education in the United States and
Canada. Boston: Merrymount Press, 1910
"...The curse of medical education is the
excessive number of schools. The
situation can improve only as weaker
and superfluous schools are
extinguished."
“Society reaps at this moment
but a small fraction of the
advantage which current
knowledge has the power to
confer.”
The Evidence for CDS
• CDS yields increased adherence to guideline-based care, enhanced
surveillance and monitoring, and decreased medication errors
• (Chaudhry et al., 2006)
• CDS, at the time of order entry in a computerized provider order entry
system can help eliminate overuse, underuse, and misuse.
• (Bates et al., 2003; Austin et al., 1994; Linder, Bates and Lee, 2005; Tierney et
al., 2003)
• For expensive radiologic tests and procedures this guidance at the point of
ordering can guide physicians toward ordering the most appropriate and
cost effective, radiologic tests.
• (Bates et al., 2003; Khorasani et al., 2003)
• Showing the cumulative charge display for all tests ordered, reminding
about redundant tests ordered, providing counter-detailing during order
entry, and reminding about consequent or corollary orders may also
impact resource utilization
• (Bates and Gawande, 2003; Bates, 2004; McDonald et al., 2004).
The Value of Ambulatory CDS
• Savings potential: $44 billion
• reduced medication, radiology, laboratory, and ADE-
related expenses
• Advanced CDS systems
• Savings potential only with advanced CDS
• cost five times as much as basic CDS
• generate 12 times greater financial return
• A potential reduction of more than 2 million
adverse drug events (ADEs) annually
Johnston et al., 2003
http://www.citl.org
Serious Medication Error
Rates Before and After CPOE
Bates et. al. JAMA 1998.
CAD/DM Smart Form
Smart View: Data Display
Smart Assessment, Orders, and Plan
Assessment and recommendations generated from rules engine
Smart Documentation
• Lipids
• Anti-platelet therapy
• Blood pressure
• Glucose control
• Microalbuminuria
• Immunizations
• Smoking
• Weight
• Eye and foot examinations
CAD/DM Smart Form
Medication Orders
Lab Orders
Referrals
Handouts/Education
Preliminary Results:
Smart Form On Treatment Analysis
0% 10% 20% 30% 40% 50% 60% 70% 80%
Up-to-date BP result
Change in BP therapy if above goal
Up-to-date height and weight
Change in therapy if A1C above goal
Up-to-date foot exam documented
Up-to-date eye exam documented
# of deficiencies addressed
Smart Form Used Control
<0.001
<0.001
<0.001
<0.001
<0.001
0.05
0.004
0.006
CAD Quality Dashboard
Targets are 90th percentile for HEDIS or for Partners providers
Zero defect care: • Aspirin• Beta-blockers• Blood pressure• Lipids
Red, yellow, and green indicators show adherence with targets
Discrepancy
Details
Patient Journal Causes
Provider Activation
Grant RW et al. Practice-linked Online Personal Health Records for Type 2
Diabetes: A Randomized Controlled Trial. Arch Intern Med. 2008 Sep
8;168(16):1776-82. .
More medication changes in visits after diabetes journal submission:
A Roadmap for National Action on Clinical Decision Support
“to ensure that optimal, usable and effective clinical decision support is widely available to providers,
patients, and individuals where and when they need it to make health care decisions.”
Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. J. Am. Med. Inform.
Assoc. 2007;14(2):141-145.
CDS Consortium Goal
To assess, define, demonstrate, and
evaluate best practices for knowledge
management and clinical decision support in
healthcare information technology at scale –
across multiple ambulatory care settings and
EHR technology platforms.www.partners.org/cird/cdsc
Guideline Model Chronology
1980 1990 2000
ONCOCIN EON(T-Helper) GLIF2
Arden
MBTA
GEODE-CM
EON2
GLIF3
Asbru
Oxford System
of MedicineDILEMMA PROforma
PRESTIGE
PRODIGY
Decision Tables GEM
PRODIGY3
P. L. Elkin, M. Peleg, R. Lacson, E. Bernstam, S. Tu, A. Boxwala, R. Greenes, & E. H. Shortliffe.
Toward Standardization of Electronic Guidelines. MD Computing 17(6):39-44, 2000
Narrative Recommendation layer
Narrative text of the recommendation from the published guideline.Semi-Structured Recommendation layer
Breaks down the text into various slots such as those for applicable clinical scenario, the recommended intervention, and evidence basis for the recommendation
Standard vocabulary codes for data and more precise criteria (pseudocode)
Abstract Representation layer
Structures the recommendation for use in particular kinds of CDS tools
• Reminder and alert rules
• Order sets
A recommendation could have several different artifacts created in this layer, one for each kind of CDS tool
Machine Executable layer
Knowledge encoded in a format that can be rapidly integrated into a CDS tool on a specific HIT platform
E.g., rule could be encoded in Arden Syntax
A recommendation could have several different artifacts created in this layer, one for each of the different HIT platforms
CDSC Multilayered
Knowledge Representation
Narrative Guideline
Semistructured Recommendation
Abstract Representation
Machine Execution
Enterprise CDS Framework
Run Rules
Controller
O
R
C
H
E
S
T
R
A
T
O
R
Supporting Services
CCD
Factory
CCD
Translation
Services
Classification
Services
Pt Data
Access
ECRS
Metadata
Query
CDS Consumers
External to
PHS
Vendor,
nonCache
Internal,
Cache
Vendor Products
Rule
Authoring
Patient DataReference
Data
Rule Execution
Server
Rule DB
Action
Patient
Factory
An external repository of clinical
content with web-based viewer
Search Criteria
Content Type…
Specialty
“I conclude that though the individual physician is not perfectible, the system of care is, and that the computer will play a major part in the perfection of future care systems.”
Clem McDonald, MD NEJM 1976Thank you!Blackford Middleton, [email protected]
Where are we?