A Population-Based
Laboratory Information
Strategy
Michael McNeely MD FRCPC
Consultant in Medical Informatics,
Victoria BC
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Overview
There will be an ever-increasing need for laboratory results to
be knowledge-based: to be interpreted, to guide treatment,
and to smoothly integrate with the medical record.
Canada Health Infoway is a government of Canada project
whose goal is to have electronic medical records (EMR) for
80% of Canada’s population by 2010.
The Provincial Laboratory Information Solution is a BC
project to provide a unified database of all laboratory results
produced in the province. These two projects are at an early
stage but eventually (phase III-IV) will incorporate knowledge
support.
The presentation will, by way of a review, discuss the
potential for these initiatives to carry forward existing
programs involving laboratory utilization control, risk
management, chronic disease management, telepathology,
epidemiology, genominformatics, and sample management.
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Canada Health Infoway
http://www.infoway-inforoute.ca/
What is Infoway?
– Canada Health Infoway Inc. invests with public sector
partners across Canada to implement and reuse
compatible health information systems that support a
safer, more efficient healthcare system. Infoway is an
independent, not-for-profit organization whose
Members are Canada's 14 federal, provincial and
territorial Deputy Ministers of Health. Launched in
2001, Infoway and its public sector partners have
over 100 projects, either completed or underway,
delivering electronic health record (EHR) solutions to
Canadians – solutions that bring tangible value to
patients, providers and the healthcare system.
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Canada Health Infoway
http://www.infoway-inforoute.ca/
Mission
– To foster and accelerate the development and
adoption of electronic health information systems with
compatible standards and communications
technologies on a pan-Canadian basis, with tangible
benefits to Canadians.
– To build on existing initiatives and pursue
collaborative relationships in pursuit of our mission.
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Canada Health Infoway
http://www.infoway-inforoute.ca/
Vision
– A high-quality, sustainable and effective Canadian
healthcare system supported by an infostructure that
provides residents of Canada and their healthcare
providers with timely, appropriate and secure access
to the right information when and where they enter
into the healthcare system. Respect for privacy is
fundamental to this vision.
Goal
– To have an interoperable EHR in place across 50 per
cent of Canada (by population) by the end of 2009.
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Canada Health Infoway
http://www.infoway-inforoute.ca/
Components of the HER
–
–
–
–
–
–
–
–
–
Patient and provider registries $110 m
Laboratory Results $ 150 m
Medical Imaging $ 220 m
Drugs $ 185 m
Interoperable EHR $ 175 m
Telehealth $ 150 m
Public Health $ 100 m
Innovation and adoption $ 60 m
Infostructure $ 25 m
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Evolution of EHR

Order entry and results viewing for
laboratory tests, medications and
images.
Alert notification (eg. duplicate
tests, drug interaction)
Provisioning of leading practices
(i.e., CPG’s)
Scheduling


Functionality and Value Chain Optimization

Generation 3 plus complex
Decision Support
Generation 4
The Mentor
 Patient demographics
 Provider demographics
Generation 3
 Location demographics
The Helper
 Encounters
Generation 2
The Documenter
Generation 1
The Foundation
Enablers
Results Viewing

Laboratory test results

Dispensed medications

Diagnostic image results
Includes investments to support project management, user-adoption, change
management, knowledge transfer, standards and benefits evaluation, representing 30%
of program investments overall
End of 2009
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Canada Health Infoway
http://www.infoway-inforoute.ca/
Progress to date
– Standards adoption:
HL 7
LOINC
SNOMED CT
– Provincial Projects
Ontario
Others
British Columbia
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BC - The Provincial Strategy
http://www.healthservices.gov.bc.ca/cpa/publications/ehealth_framework.pdf
An HER provides each British Columbian with a
secure and private lifetime record of their key
health history and care within the health system.
The record is available electronically to
authorized health care providers and the
individual anywhere, anytime, in support of highquality care.
For more information on the Electronic Health
Record, please see:
http://healthnet.hnet.bc.ca/index.html
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Provincial Laboratory Information
Solution (PLIS)
Planning and development activities to support Technology
Transformation are being led by a dedicated PLIS Office within the
PLCO, working with the Ministry of Health’s Knowledge
Management Branch. A joint PLCO/Ministry strategy which will lead
to the creation of a Provincial Laboratory Information Solution
(PLIS) for British Columbia.
The overall guiding vision behind the creation of a Provincial
Laboratory Information Solution (PLIS) for British Columbia is to
provide access to clinical laboratory information (results, orders and
decision support) to care providers at the point of care anywhere in
British Columbia. PLIS is also a leading initiative within the Ministry
of Health's broader E-Health strategy to develop the Electronic
Health Record and support IT infrastructure for health care in BC.
The Provincial Laboratory Information Solution (PLIS) will:
provide a standardized province-wide approach to presenting a
patient's lab test results
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Provincial Laboratory Information
Solution (PLIS)
electronically distribute lab test results to ordering and/or copied
physicians
make historical lab test results from both public and private
laboratories within the province available to physicians
create an electronic lab test ordering system with decision support
tools
improve the ability to aggregate laboratory information in order to
support both administrative and clinical decision-making
provide a provincial capacity to measure and manage the provision
and utilization of laboratory services
contribute to the realization of the provincial Electronic Health
Record (EHR)
Through the use of technology and standards, the new system will
ensure laboratory information is: of a high quality, available to
authorized health care providers and administrators throughout the
province, part of each patient's provincial Electronic Health Record
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Provincial Laboratory Information
Solution (PLIS)
Features
Organizational structure
Unique bid process – Joint Services RFP
Development
Time frame
FUTURE COMPONENTS OF INTEREST
Data Mining
Clinical Decision / Knowledge Support
Telepathology
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Data Mining
Utilization Control
Number
5000
4500
4000
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3000
2500
2000
1500
1000
500
>=100
10.0-19.9
20.0-49.9
<40
50.0-99.9
9.0-9.4
9.5-9.9
8.0-8.4
8.5-8.9
7.0-7.4
7.5-7.9
5.5-5.9
6.0-6.4
60-70
6.5-6.9
4.0-4.4
4.5-4.9
PSA Value
5.0-5.4
3.0-3.4
3.5-3.9
1.5-1.9
2.0-2.4
2.5-2.9
0-0.49
1.0-1.4
0
0.5-0.99
– Reduce unnecessary
duplication of testing
– Ensure adherence to
utilization protocols
– Facilitate data
evaluation in order to
design utilization
strategies
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Chronic Disease Management
– Clinical Practice Guidelines
Provide objective data for CPG development
Outcomes analysis
Follow-up of adherence
Follow-up for outcomes studies
Makes more elaborate CPGs possible
– Disease epidemiology
– Assist individual physician’s patient tracking (e.g. lists
of diabetics in a physician’s practice).
– Provide physician reminders re chronic disease
patient reviews
– Provide availability to a “package” of physician
specific database searches on their own patients (e.g.
a list of all “registered” diabetics in a given practice
with statistics on their frequency of A1C testing
compared to provincial norms).
– Patient reminders
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Special Disease Registries/Services
– Automated development of registries of diseases characterized by
laboratory test results (e.g. hemoglobinopathies, hypercholesterolemia,
diabetes, hemochromatosis, and many others as genetic testing
expands)
– Specialized knowledge support tools and information for both physicians
and patients
– “Mailing list” of physicians/patients to be informed when new information
becomes available.
Epidemiology
– Classic infectious disease epidemiology (but closer to “real-time”)
– Real-time epidemiology for epidemics (e.g. SARS) and bioterrorism
– Chronic disease epidemiology (non-infectious)
Health Care System Management
–
–
–
–
–
Outcomes data
Utilization management
Population trends
Test usage and deployment of resources
Physician ordering profiles
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In 1982 I gave a talk on this very same subject. I
covered the following types of automated
interpretations.
Level 1: Standard comment on every report of a specific
test.
Level 2: Result specific comment: 1-test.
Level 3: Result specific comment: 2-or more tests, over
time, or other clinical information
Level 4: More sophisticated approaches.
Now, in 2006 we haven’t managed Levels 1-3
completely but we’re now looking at Level 4
and various projects may bring Level 4 to
fruition within the next few years.
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“Canned” Comments
GOOD THINGS
Demonstrated ability to change physician behaviour
Demonstrated ability to enhance use of laboratory
testing (e.g. utilization, diagnosis)
CAUTIONS
Limited clinical information
Comment added whether needed or not
Consume space on a paper report
Paper report has a rigid format
Some doctors feel threatened/insulted
Patient overreaction (patient access)
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Human Generated Comments
Questions:
– Are the interpretations part of the legal report?
– Should the interpretations be added to EMR?
– Who should be permitted to prepare such interpretations?
Human generated reports have error rate of up to 50%
(Lim Clin Chem 2004)
Marshall & Challand (Ann Clin Biochem 2000)
– Variation amongst interpreters
– Communication style variable
– Clinical information available is not always appropriate to the test
being interpreted
– Little feedback regarding usefulness
– Interpretations should be recipient specific
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Laposata (Clin Chem 2004; 50: 471)
Laposata has championed the need for humangenerated, patient-specific narrative
interpretations
He has criticized the “canned” comment
BUT he compares Apples and Oranges
Laposata makes the case for why Knowledge
Support is needed.
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Knowledge Support
a.k.a. Clinical Decision Support
Two forms:
– Static: PubMed, Lab Tests On-Line, ARUP
– Dynamic or CARTKS (Context Appropriate
Real Time Knowledge Support)
Specific Interpretations
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The “Case” for Knowledge Support
/ Clinical Decision Support
Hundreds of publications have
demonstrated its potential usefulness
Several publications have pointed out
potential problems but none has undercut
the basic premise.
Clinical Practice Guidelines:
– Ever increasing numbers
– Poorly applied (~ 25% adherence)
– Limited complexity
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“It is likely that when electronic knowledge support tools
become a standard feature of medical practice, the
protocol and CPG approach will be maximized.”
McNeely Clinics of Laboratory Medicine 2002; 22: 1-10
“It is so apparent that computerization will enhance the
application of CPGs that it may be unethical to continue
to perform trials to answer this question.”
Ellson and Connolly JAMA 1998; 279: 989.
“To be widely accepted by practicing clinicians,
computerized support systems for decision making must
be integrated into the clinical work flow. They must
present the right information, in the right format, at the
right time, without requiring special effort.”
James BC NEJM 1999; 340: 1202.
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Ripple-Down Rules
Developed by Paul Compton and Gordon
Edwards of St. Vincent’s Hospital, Sydney AU
Original system PIERS
Now marketed by Pacific Knowledge Systems
http://www.pks.com.au/ as LabWizard™
Rule-Based but no knowledge engineer
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Ripple-Down Rules
Knowledge Base &
Inference Engine
Lab Completes Test
Verified Result
Combination?
No
Yes
LIS Reports: Result
And Interpretation
Result Combo
Interpreted
Integrator
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LabWizard (example)
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BloodLink
Clin Chem 2002; 48:
605.
Marc van Wijk MD PhD
Delft, The Netherlands
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BloodLink – Evaluation
50
GPs
Test
reduction
Two Groups
of 19.6%
1-Year
BloodLink Restricted
CONTROL
BloodLink Guideline
TEST
Number
of
Number of Tests
Requested
Requisitions
12,786
87,634
12,700
70,479
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Laboratory Advisory System
Chang E, McNeely MDD, Gamble K. Strategies for
choosing the next test in an expert system. Proceedings
of the congress on medical informatics. AAMSI 1984;
2:198-202.
McNeely MDD, Smith B. An interactive expert system
for the ordering and interpretation of laboratory tests to
enhance diagnosis and control utilization. Canadian
Medical Informatics. May/June 1995;16-19.
Smith BJ and McNeely MDD. The Influence of an Expert
System for Test Ordering and Interpretation on
Laboratory Investigations. Clinical Chemistry 1999;
45(8): 1168-1175.
Clinical-Laboratory.com Old Marlebone Rd, London,
England
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Results of a trial
Paper
Computer
Mean # of tests
32.7
17.8
Mean # of samples
7.5
5.8
$ 232
$ 194
Turnaround time (days)
3.2
1
Diagnostic accuracy
66%
100%
12
0
Cost ($ CDN)
Referrals to specialists
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The LAS – Study Conclusion
The development of test ordering
strategies can be enhanced.
The interpretation of the test results can
be enhanced.
A statistical database of diagnosis, clinical
information, test orders, and results can be
readily derived. Such information is unique
and is available for optimizing and
developing testing strategies and for
laboratory management.
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The LAS – study conclusion (con’t)
An appropriate search of the database
would enable clinician-targeted
education and utilization feedback to be
derived.
Examination of the database at the time
of ordering would enable the
development of a module to identify
unnecessary, duplicate testing.
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Contextualized Report
Dr. Jonathan Kay (Oxford)
Drs. Bruce Friedman and Jules Berman Lab Medicine 2006; 37: 121.
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Smith, John H.
Male
46 yoa
Dr. Louis Pasteur
DOS June 7, 2006
23957988-1
Reference
Interval
Test Name
Result
Alkaline Phosphatase
128  20 – 105 U/L
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Smith, John H.
Male
Dr. Louis Pasteur
DOS June 7, 2006
Test Name
Result
46 yoa
23957988-1
Reference
Interval
128  20 – 105 U/L ALERT !! Patient is
taking Chlorpromazine
Analytical Information – Alkaline Phosphatase
which is known to cause
Cholestasis with
1. Laboratory validation studies
increased Alk Phos.
2. Method reference
3. Instrument validation studies
4. Proficiency testing record
5. Complete Bibliography –Click here 
Alkaline Phosphatase
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Smith, John H.
Male
46 yoa
Dr. Louis Pasteur
DOS June 7, 2006
23957988-1
Reference
Interval
Test Name
Result
Alkaline Phosphatase
128  20 – 105 U/L
Analytical Information – Alkaline Phosphatase
1.
2.
3.
4.
Laboratory validation studies
Method reference
Instrument validation studies
Proficiency testing record
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Smith, John H.
Male
46 yoa
Dr. Louis Pasteur
DOS June 7, 2006
23957988-1
Reference
Interval
Test Name
Result
Alkaline Phosphatase
128  20 – 105 U/L
Reference Interval – Alkaline Phosphatase
1.
2.
3.
4.
Literature Reference
In-house studies
Notes
Graphical Presentation
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Smith, John H.
Male
46 yoa
Dr. Louis Pasteur
DOS June 7, 2006
23957988-1
Reference
Interval
Test Name
Result
Alkaline Phosphatase
128  20 – 105 U/L
Reference Interval – Alkaline Phosphatase
1.
2.
3.
4.
Literature Reference
In-house studies
Notes
Graphical Presentation
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Smith, John H.
Male
46 yoa
Dr. Louis Pasteur
DOS June 7, 2006
23957988-1
Reference
Interval
Test Name
Result
Alkaline Phosphatase
128  20 – 105 U/L
Interpretation – Alkaline Phosphatase
1.
2.
3.
4.
Causes of an increased Alkaline Phosphatase
Causes of an decreased Alkaline Phosphatase
Specific Interpretation of this result
Request a personal consultation on this result
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Genoinformatics
Screening tests
PCR Testing
Proteomics
Physician Understanding
Patient Information - Counselling
Family Studies
Long-term
Follow-up
New Knowledge
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Knowledge Assembly
Major problem is the creation/assembly of
Knowledge Support tools (e.g. 1 rule per hour or
committee)
Must have AUTOMATED knowledge assembly
Must have generic Inference Engines
Must rely on the integrative intelligence of the
user
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Inference Engine
IF
Alkaline Phosphatase > ULN
AND
Age > 70
THEN
Consider Paget’s Disease
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Knowledge Assembly
Facts:
ND most
2
– From Electronic Medical Record
important
– Added at time of ordering
– Added during interpretation
slide
Rules
– Grunt approach
– Formal Committees (worldwide?)
Constructed
– CPGs
– Wikipedia format
– Medical Literature
Automated
– Database

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Wikipedia
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Medical Literature
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Data Mining
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Telepathology
Goals of Province-wide program – VISION
Organizational structure
Overview
Standards
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Evolution Step
•Feasibility trials
of S&F
•Thinking
about V
Infrastructure
STDS
Accred Rules
Privacy
PLIS PACS
Existing
•Education
Record of image (not image)
•Routine S&F
Linkage to API LIS
•Some trial
virtual
•Mature use of
S &F
•Some HA use V
routinely for
limited APS trial
HA-HA
HL7
SNOWMED.CT
LOINC
Intra Dept
Storage
Licensing ?
DICOM
2009
Image
Repository
•Mature S&F
•Routine, limited
ApV, routine HAHA
•Additional Aps &
more common
use
PACS
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Most
Conclusion
important
The EHR 2009-2010 – Clinical Decision slide
Support: If Decision Support is expected in 2 4 years then planning MUST start now.
If Knowledge Support is to be meaningful then
building the Knowledge Bases must begin soon
– but, we will need to know how they will be
executed and what the Inference Engine will
look like.
If Laboratory Professiolnals expect to be
involved in the interpretation of the results they
produce they must get involved in the
development of the Decision Support modules
or risk being disintermediated.
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Conclusion
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