Translating Clinical Guidelines into Knowledge-guided Decision Support. Middleton B. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)

1. Translating Clinical Guidelines
into Knowledge-Guided
Decision Support
Blackford Middleton, MD, MPH, MSc, FACP, FACMI, FHIMSS
Corporate Director, Clinical Informatics Research & Development
Chairman, Center for Information Technology Leadership
Harvard Medical School

2. Overview
• The Evidence for CDS
• The Value Potential of CDS
• Current examples and R&D Projects
• The Clinical Decision Support Consortium

3. Flexner Report
"...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.”
Abraham Flexner,
Medical Education in the United States and
Canada. Boston: Merrymount Press, 1910

4. 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).

5. 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
http://www.citl.org
Johnston et al., 2003

6. Serious Medication Error
Rates Before and After CPOE
Bates et. al. JAMA 1998.

7. CAD/DM Smart Form
Smart View: Smart Smart
Data Display Documentation Assessment,
Orders, and Plan
Assessment and
recommendations generated from
rules engine
• Lipids
• Anti-platelet therapy
• Blood pressure
• Glucose control
• Microalbuminuria
• Immunizations
• Smoking
• Weight
• Eye and foot examinations

8. CAD/DM Smart Form
Medication Orders
Lab Orders
Referrals
Handouts/Education

9. Preliminary Results:
Smart Form On Treatment Analysis
Smart Form Used Control
0% 10% 20% 30% 40% 50% 60% 70% 80%
Up-to-date BP result <0.001
Change in BP therapy if above goal 0.05
Up-to-date height and weight 0.004
Change in therapy if A1C above goal
0.006
Up-to-date foot exam documented <0.001
Up-to-date eye exam documented <0.001
# of deficiencies addressed <0.001

10. CAD Quality Dashboard
Targets are 90th percentile for
Red, yellow, and green indicators show HEDIS or for Partners providers
adherence with targets
Zero defect care:
• Aspirin
• Beta-blockers
• Blood pressure
• Lipids

12. Discrepancy
Details

13. Patient Journal Causes
Provider Activation
More medication changes in visits after diabetes journal submission:
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. .

14. 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.

15. 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

16. Guideline Model Chronology
Decision Tables GEM
Arden
GEODE-CM
ONCOCIN EON(T-Helper) GLIF2 GLIF3
MBTA
EON2
Asbru
PRODIGY PRODIGY3
Oxford System
DILEMMA PROforma
of Medicine
PRESTIGE
1980 1990 2000
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

17. CDSC Multilayered
Knowledge Representation
Machine Execution
Abstract Representation
Semistructured Recommendation
Narrative Guideline
Narrative Recommendation layer
Semi-Structured Recommendation layer
Narrative text of the recommendation from the published guideline.
Abstract Representation layer
Breaks down the text into various slots such as those for applicable
Machine Executable layer
Structures the recommendation for use inintervention, andCDS tools
clinical scenario, the recommended particular kinds of evidence
• basis for theencoded in a format that can be rapidly integrated into a
Knowledge recommendation
Reminder and alert rules
Standard vocabularyspecific HIT platform
• Order sets on a codes for data and more precise criteria
CDS tool
A(pseudocode) be encoded in Arden Syntax
E.g., rule could could have several different artifacts created in this layer,
recommendation
Aone for each kind of CDS tool
recommendation could have several different artifacts created in this
layer, one for each of the different HIT platforms

18. Enterprise CDS Framework
CDS Consumers
ECRS
O
External to
R Vendor Products
PHS Metadata
C
H Query Rule
Rule Execution Authoring
E Action
Server
Internal, S
Cache T
Run Rules
R
A Controller
T Rule DB
O
Vendor,
nonCache R
Patient
Factory
Supporting Services
CCD
CCD Pt Data Classification Translation
Factory Access Services Services
Patient Data Reference
Data

19. An external repository of clinical
content with web-based viewer
Search Criteria
Content Type…
Specialty

20. Where are we?
“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 1976
Thank you!
Blackford Middleton, MD
bmiddleton1@partners.org