Presentation by Dr Glenn Edwards at Pathology Horizons 2016 conference in Galway, entitled: "Knowledge management in context: Implications for clinical pathologists."
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Knowledge management in context: Implications for clinical pathologists by Dr Glenn Edwards
1. Knowledge management in context:
Implications for clinical pathologists
Dr Glenn Edwards
glenn.edwards@sjog.org.au
Disclosures
Former shareholder, CEO, Medical Director of Pacific Knowledge Systems
Ad hoc Abbott Diagnostics consultancy
3. • Key issues
– Most evidence for process outcomes
– Remaining challenges
• Demonstrate impact on outcomes, cost, users
• Means to augment uptake and effectiveness
• Integration into workflow
• Deployment across diverse settings
• Transformation role
• “Broad penetration of CDSS will require aggressively seeking a
better understanding of what the right information is and
when and how it should be delivered to the right person..”
Impact of CDSS: 2012 systematic review
(Bright et al Ann Int Med 2012;157(1):29)
5. BNP use /1000 patients / PCT
Still extremely low use
in many areas:
•Excess costs
•Poor patient
experience
•Failure to adopt
innovation
Map from Atlas of Variation
6.
7. UK standards for authorisation and reportingUK standards for authorisation and reporting
• Comment on all reports: 5%
• 42% no policy
• 31% consider highlighting “abnormals” to
constitute an interpretation of the result
Prinsloo P. & Gray T. Ann Clin Biochem 2003;40:149-55
8. 8
How would you interpret theseHow would you interpret these
results?results?
39 year old female
Cholesterol 5.1 mmol/L
Triglyceride 3.5 mmol/L *
HDL cholesterol 0.9 mmol/L *
LDL cholesterol 2.6 mmol/L
9. “Canned” text comments
• LDL calculation formula
• Assay methods
• Interpretation
– “Common causes of hyperlipidaemia include…”
• Advice
– “See www.cvdcheck.org.au to calculate risk…”
10. Context-specific opinion
“Dyslipidaemic pattern. Note previous results
indicating poorly controlled diabetes mellitus,
which likely accounts for the lipid disorder.
Suggest review glycaemic control (HbA1c to
follow) and check urine ACR, which is now
overdue. Monitor lipid response to intensified
management. Note current statin therapy may
be insufficient.”
11. Tools to manage context
• Conventional LIS rules/middleware
• Expert systems
– Rules
– Case-based rules
• Ripple down rules
• Artificial intelligence
– Machine learning
– Other ?
12. Familial Hypercholesterolaemia
Maternal grandmother
-South African
-died at age 50
Aunt
-died at age 50
(heart attack)
Aunt
-died at age 60 (heart
attack) high
cholesterol
Uncle
-died at age 50
(heart attack)
-died at age 50
(heart attack)
-had a bypass
-by age 38
2x bypasses
2x heart attacks
-died age 40
2x bypasses
Heart attack
-by age 48
4x bypasses
-age 26
High cholesterol
-age 28
High cholesterol
-by age 46
High cholesterol
3x bypasses
Ms. D (38)
High
cholesterol
(9.2 mmol/L)
High
cholesterol
DNA testing at PathWest,
RPH, mutation detected
13. Impact of Pathologists’ advice on LDL
cholesterol levels
Bell DA et al Clin Chim Acta 2013;422:21-25
Interpretative
comment
Control Significance
Number of individuals 96 100
Repeat LDL-cholesterol
Number (%)
63
(71%)
70
(70%)
NS
Mean reduction in LDL-
cholesterol (mmol/L)
3.0 2.3 p<0.005
Specialist referral
(whole group)
4
(4%)
1
(1%)
p=0.20
Specifically suggesting
referral in interpretative
comment.
3
26 individuals
(11.5%)
1
(1%)
p<0.05
14. Impact of context-sensitive interventions
Prospective case control study
• Context-specific intervention to improve specialist
referral for at-risk patients
• Significant benefit
– Controls 8/96 (8%) vs Cases 24/135 (18%) were referred
following pathologist advice
• First prospective case-control study to demonstrate
a positive benefit of pathologist report interpretation
R. Bender et al Pathology 2016;48(5):463
15. Incremental knowledge acquisition
Rules built per day
0
10
20
30
40
50
60
13/10/2009
27/10/2009
10/11/2009
24/11/2009
8/12/2009
22/12/2009
5/01/2010
19/01/2010
2/02/2010
16/02/2010
2/03/2010
16/03/2010
30/03/2010
13/04/2010
27/04/2010
21. Canned comments:
Simple knowledge models
IF Triglyceride is HIGH
AND HDL is LOW
AND LDL-C < 2.5
THEN “Common causes of dyslipidaemic pattern include….”
Rules: 1
Conditions: 3
Validation: Straightforward
Value: Low
22.
23. Validation trade-off
• Conventional KBS : pre-implementation testing and
validation.
– Presumes final, complete knowledge base
– Reliant on knowledge engineers and formal, resource-
intensive methods
• Context-specific KBS (Rippledown)
– Early deployment and incremental knowledge acquisition
– Accelerated buy-in and uptake
– Pathologist validation provides ongoing exposure to
thousands of valid, real-world cases
– Far more extensive validation than formal methods
– No formal validation methodology
25. Free text analysis in CDS
D. Sittig et al J Biomed Inform 2008;41:387
•Free text (Challenge #9 of “10 grand challenges”)
•> 50% of key information resides in the free text
portions of the EHR
•We need methods for accessing and reasoning with
free text
•=> enable more specific CDS interventions
– highly tailored alerts and reminders,
– even condition-specific or patient specific order sets
26.
27. Natural Language Processing
• Named Entity Recogniser (NER)
– Eg: Mayo system (cTAKES) J Am Med Inform Assoc
2010;17:507)
• Issues
– Conflicts
– Training sets
– Informality of language (eg Web vs journalistic articles)
– Situated context
• NER + RDR wrapper
– Improves Web document analysis
28. Situated context
• What is the meaning of this:
“Diabetes check”
• Context 1
–HbA1c used for monitoring known diabetes
• Context 2
–New reimbursement item:
–HbA1c used for diagnosis of diabetes
30. Value
• What do stakeholders want?
– Doctors, Patients, Community
– Payers
• Current model is not sustainable
– Reactive
– Raw test results
• We need to demonstrate, and articulate, the value of
pathology (clinical, financial)
And..
• Design and build Pathology 2.0
St John et al Clinical Biochemistry 2015;48:823
A call for a value based approach to laboratory medicine funding
31. Knowledge management in context:
Implications for clinical pathologists
Dr Glenn Edwards
glenn.edwards@sjog.org.au
Editor's Notes
One point to make – data from Rick Jones, Map from Atlas of Variation:
Same source of data with repeat measures allows uptake of innovation to be monitored and displayed goegraphically.