SlideShare a Scribd company logo
1 of 31
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
Uluru ’93
• 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)
Runciman et al MJA 197 (2) · 16 July 2012
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
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
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
“Canned” text comments
• LDL calculation formula
• Assay methods
• Interpretation
– “Common causes of hyperlipidaemia include…”
• Advice
– “See www.cvdcheck.org.au to calculate risk…”
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.”
Tools to manage context
• Conventional LIS rules/middleware
• Expert systems
– Rules
– Case-based rules
• Ripple down rules
• Artificial intelligence
– Machine learning
– Other ?
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
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
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
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
Auto-validation
Eugenio H. Zabaleta, Ph.D.
MedCentral Health System, OH
Validation of knowledge-based
systems
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
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
Free text analysis in clinical
decision support systems
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
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
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
CLN August 2014
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
Knowledge management in context:
Implications for clinical pathologists
Dr Glenn Edwards
glenn.edwards@sjog.org.au

More Related Content

What's hot

Stage2mu part2-ptengagementtochie-121005114900-phpapp02
Stage2mu part2-ptengagementtochie-121005114900-phpapp02Stage2mu part2-ptengagementtochie-121005114900-phpapp02
Stage2mu part2-ptengagementtochie-121005114900-phpapp02Carla Pitcher
 
1 cartwright-ifa telehealth presentation may 2012
1 cartwright-ifa telehealth presentation may 20121 cartwright-ifa telehealth presentation may 2012
1 cartwright-ifa telehealth presentation may 2012ifa2012
 
Ifa telehealth presentation may 2012
Ifa telehealth presentation may 2012Ifa telehealth presentation may 2012
Ifa telehealth presentation may 2012ifa2012
 
Engaging multidisciplinary teams in translational research and quality improv...
Engaging multidisciplinary teams in translational research and quality improv...Engaging multidisciplinary teams in translational research and quality improv...
Engaging multidisciplinary teams in translational research and quality improv...Cancer Institute NSW
 
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using...
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using...Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using...
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using...Koray Atalag
 
The referral process as imagined versus the referral process as done: co-deve...
The referral process as imagined versus the referral process as done: co-deve...The referral process as imagined versus the referral process as done: co-deve...
The referral process as imagined versus the referral process as done: co-deve...Cancer Institute NSW
 
Building the bridge from discovery-to-delivery: A Community of Practice in Ca...
Building the bridge from discovery-to-delivery: A Community of Practice in Ca...Building the bridge from discovery-to-delivery: A Community of Practice in Ca...
Building the bridge from discovery-to-delivery: A Community of Practice in Ca...Cancer Institute NSW
 
Technology Assessment/Outcome & Cost-Effectiveness Analysis 2016
Technology Assessment/Outcome & Cost-Effectiveness Analysis 2016Technology Assessment/Outcome & Cost-Effectiveness Analysis 2016
Technology Assessment/Outcome & Cost-Effectiveness Analysis 2016evadew1
 
NAC PRA update - 2014 Ottawa Conference
NAC PRA update - 2014 Ottawa ConferenceNAC PRA update - 2014 Ottawa Conference
NAC PRA update - 2014 Ottawa ConferenceMedCouncilCan
 
Measuring the Effectiveness of eHealth Initiatives in Hospitals
Measuring the Effectiveness of eHealth Initiatives in HospitalsMeasuring the Effectiveness of eHealth Initiatives in Hospitals
Measuring the Effectiveness of eHealth Initiatives in HospitalsHealth Informatics New Zealand
 
ctt-mediaKit_010411v1_spreads
ctt-mediaKit_010411v1_spreadsctt-mediaKit_010411v1_spreads
ctt-mediaKit_010411v1_spreadsTracy Critchfield
 
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...ANZICS
 
Knowledge Translation: Practical Strategies for Success v1
Knowledge Translation: Practical Strategies for Success v1Knowledge Translation: Practical Strategies for Success v1
Knowledge Translation: Practical Strategies for Success v1Imad Hassan
 

What's hot (19)

Same-Day ART Start for HIV: Can we expedite our treatment goals?
Same-Day ART Start for HIV: Can we expedite our treatment goals?Same-Day ART Start for HIV: Can we expedite our treatment goals?
Same-Day ART Start for HIV: Can we expedite our treatment goals?
 
Stage2mu part2-ptengagementtochie-121005114900-phpapp02
Stage2mu part2-ptengagementtochie-121005114900-phpapp02Stage2mu part2-ptengagementtochie-121005114900-phpapp02
Stage2mu part2-ptengagementtochie-121005114900-phpapp02
 
1 cartwright-ifa telehealth presentation may 2012
1 cartwright-ifa telehealth presentation may 20121 cartwright-ifa telehealth presentation may 2012
1 cartwright-ifa telehealth presentation may 2012
 
Ifa telehealth presentation may 2012
Ifa telehealth presentation may 2012Ifa telehealth presentation may 2012
Ifa telehealth presentation may 2012
 
Engaging multidisciplinary teams in translational research and quality improv...
Engaging multidisciplinary teams in translational research and quality improv...Engaging multidisciplinary teams in translational research and quality improv...
Engaging multidisciplinary teams in translational research and quality improv...
 
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using...
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using...Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using...
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using...
 
The referral process as imagined versus the referral process as done: co-deve...
The referral process as imagined versus the referral process as done: co-deve...The referral process as imagined versus the referral process as done: co-deve...
The referral process as imagined versus the referral process as done: co-deve...
 
Gpt buchman
Gpt buchmanGpt buchman
Gpt buchman
 
Finding the Clinical Trial That's Right for Me
Finding the Clinical Trial That's Right for MeFinding the Clinical Trial That's Right for Me
Finding the Clinical Trial That's Right for Me
 
Building the bridge from discovery-to-delivery: A Community of Practice in Ca...
Building the bridge from discovery-to-delivery: A Community of Practice in Ca...Building the bridge from discovery-to-delivery: A Community of Practice in Ca...
Building the bridge from discovery-to-delivery: A Community of Practice in Ca...
 
Pera Health
Pera HealthPera Health
Pera Health
 
Wepdc201
Wepdc201Wepdc201
Wepdc201
 
Technology Assessment/Outcome & Cost-Effectiveness Analysis 2016
Technology Assessment/Outcome & Cost-Effectiveness Analysis 2016Technology Assessment/Outcome & Cost-Effectiveness Analysis 2016
Technology Assessment/Outcome & Cost-Effectiveness Analysis 2016
 
NAC PRA update - 2014 Ottawa Conference
NAC PRA update - 2014 Ottawa ConferenceNAC PRA update - 2014 Ottawa Conference
NAC PRA update - 2014 Ottawa Conference
 
bagp test
bagp testbagp test
bagp test
 
Measuring the Effectiveness of eHealth Initiatives in Hospitals
Measuring the Effectiveness of eHealth Initiatives in HospitalsMeasuring the Effectiveness of eHealth Initiatives in Hospitals
Measuring the Effectiveness of eHealth Initiatives in Hospitals
 
ctt-mediaKit_010411v1_spreads
ctt-mediaKit_010411v1_spreadsctt-mediaKit_010411v1_spreads
ctt-mediaKit_010411v1_spreads
 
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...
ANZICS S&Q 2014 - Abstract Presentation: Kyle Brooks on Impact of night time ...
 
Knowledge Translation: Practical Strategies for Success v1
Knowledge Translation: Practical Strategies for Success v1Knowledge Translation: Practical Strategies for Success v1
Knowledge Translation: Practical Strategies for Success v1
 

Similar to Knowledge management in context: Implications for clinical pathologists by Dr Glenn Edwards

Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Perficient, Inc.
 
Knowledge transfer research examples
Knowledge transfer research examplesKnowledge transfer research examples
Knowledge transfer research examplestaem
 
Confirmation of the Validity of the Central Line Bundle as a Measure of a Hea...
Confirmation of the Validity of the Central Line Bundle as a Measure of a Hea...Confirmation of the Validity of the Central Line Bundle as a Measure of a Hea...
Confirmation of the Validity of the Central Line Bundle as a Measure of a Hea...Heather Gilmartin
 
Provenance abstraction for implementing security: Learning Health System and ...
Provenance abstraction for implementing security: Learning Health System and ...Provenance abstraction for implementing security: Learning Health System and ...
Provenance abstraction for implementing security: Learning Health System and ...Vasa Curcin
 
Norwegian clinical genetics analysis platform ”genAP”, Thomas Grünfeld and To...
Norwegian clinical genetics analysis platform ”genAP”, Thomas Grünfeld and To...Norwegian clinical genetics analysis platform ”genAP”, Thomas Grünfeld and To...
Norwegian clinical genetics analysis platform ”genAP”, Thomas Grünfeld and To...The Research Council of Norway, IKTPLUSS
 
Evidence based medicine
Evidence based medicineEvidence based medicine
Evidence based medicineDrShrey Bhatia
 
Implementing Clinical Decision
Implementing Clinical DecisionImplementing Clinical Decision
Implementing Clinical DecisionCMDLMS
 
A standards-based approach to development of clinical registries
A standards-based approach to development of clinical registriesA standards-based approach to development of clinical registries
A standards-based approach to development of clinical registriesHealth Informatics New Zealand
 
Apr 13 improving methods and processes codependent techs mg
Apr 13 improving methods and processes codependent techs mgApr 13 improving methods and processes codependent techs mg
Apr 13 improving methods and processes codependent techs mgOffice of Health Economics
 
From Clinical Decision Support to Precision Medicine
From Clinical Decision Support to Precision MedicineFrom Clinical Decision Support to Precision Medicine
From Clinical Decision Support to Precision MedicineKent State University
 
Natural Language Processing to Curate Unstructured Electronic Health Records
Natural Language Processing to Curate Unstructured Electronic Health RecordsNatural Language Processing to Curate Unstructured Electronic Health Records
Natural Language Processing to Curate Unstructured Electronic Health RecordsMMS Holdings
 
The Future of Personalized Medicine
The Future of Personalized MedicineThe Future of Personalized Medicine
The Future of Personalized MedicineEdgewater
 
Clinical Healthcare Data Analytics
Clinical Healthcare Data AnalyticsClinical Healthcare Data Analytics
Clinical Healthcare Data Analyticsdansouk
 
High Performance Computing and the Opportunity with Cognitive Technology
 High Performance Computing and the Opportunity with Cognitive Technology High Performance Computing and the Opportunity with Cognitive Technology
High Performance Computing and the Opportunity with Cognitive TechnologyIBM Watson
 
The Learning Health System: Thinking and Acting Across Scales
The Learning Health System: Thinking and Acting Across ScalesThe Learning Health System: Thinking and Acting Across Scales
The Learning Health System: Thinking and Acting Across ScalesPhilip Payne
 
Advanced Laboratory Analytics — A Disruptive Solution for Health Systems
Advanced Laboratory Analytics — A Disruptive Solution for Health SystemsAdvanced Laboratory Analytics — A Disruptive Solution for Health Systems
Advanced Laboratory Analytics — A Disruptive Solution for Health SystemsViewics
 
Simplifying semantics for biomedical applications
Simplifying semantics for biomedical applicationsSimplifying semantics for biomedical applications
Simplifying semantics for biomedical applicationsSemantic Web San Diego
 
Testing the applicability of digital decision support on a nationwide EHR
Testing the applicability of digital decision support on a nationwide EHRTesting the applicability of digital decision support on a nationwide EHR
Testing the applicability of digital decision support on a nationwide EHRICDEcCnferenece
 

Similar to Knowledge management in context: Implications for clinical pathologists by Dr Glenn Edwards (20)

Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
 
Knowledge transfer research examples
Knowledge transfer research examplesKnowledge transfer research examples
Knowledge transfer research examples
 
Confirmation of the Validity of the Central Line Bundle as a Measure of a Hea...
Confirmation of the Validity of the Central Line Bundle as a Measure of a Hea...Confirmation of the Validity of the Central Line Bundle as a Measure of a Hea...
Confirmation of the Validity of the Central Line Bundle as a Measure of a Hea...
 
Provenance abstraction for implementing security: Learning Health System and ...
Provenance abstraction for implementing security: Learning Health System and ...Provenance abstraction for implementing security: Learning Health System and ...
Provenance abstraction for implementing security: Learning Health System and ...
 
2015 EMS 3.0
2015 EMS 3.02015 EMS 3.0
2015 EMS 3.0
 
Norwegian clinical genetics analysis platform ”genAP”, Thomas Grünfeld and To...
Norwegian clinical genetics analysis platform ”genAP”, Thomas Grünfeld and To...Norwegian clinical genetics analysis platform ”genAP”, Thomas Grünfeld and To...
Norwegian clinical genetics analysis platform ”genAP”, Thomas Grünfeld and To...
 
Evidence based medicine
Evidence based medicineEvidence based medicine
Evidence based medicine
 
Implementing Clinical Decision
Implementing Clinical DecisionImplementing Clinical Decision
Implementing Clinical Decision
 
A standards-based approach to development of clinical registries
A standards-based approach to development of clinical registriesA standards-based approach to development of clinical registries
A standards-based approach to development of clinical registries
 
Apr 13 improving methods and processes codependent techs mg
Apr 13 improving methods and processes codependent techs mgApr 13 improving methods and processes codependent techs mg
Apr 13 improving methods and processes codependent techs mg
 
From Clinical Decision Support to Precision Medicine
From Clinical Decision Support to Precision MedicineFrom Clinical Decision Support to Precision Medicine
From Clinical Decision Support to Precision Medicine
 
Natural Language Processing to Curate Unstructured Electronic Health Records
Natural Language Processing to Curate Unstructured Electronic Health RecordsNatural Language Processing to Curate Unstructured Electronic Health Records
Natural Language Processing to Curate Unstructured Electronic Health Records
 
The Future of Personalized Medicine
The Future of Personalized MedicineThe Future of Personalized Medicine
The Future of Personalized Medicine
 
HL7: Clinical Decision Support
HL7: Clinical Decision SupportHL7: Clinical Decision Support
HL7: Clinical Decision Support
 
Clinical Healthcare Data Analytics
Clinical Healthcare Data AnalyticsClinical Healthcare Data Analytics
Clinical Healthcare Data Analytics
 
High Performance Computing and the Opportunity with Cognitive Technology
 High Performance Computing and the Opportunity with Cognitive Technology High Performance Computing and the Opportunity with Cognitive Technology
High Performance Computing and the Opportunity with Cognitive Technology
 
The Learning Health System: Thinking and Acting Across Scales
The Learning Health System: Thinking and Acting Across ScalesThe Learning Health System: Thinking and Acting Across Scales
The Learning Health System: Thinking and Acting Across Scales
 
Advanced Laboratory Analytics — A Disruptive Solution for Health Systems
Advanced Laboratory Analytics — A Disruptive Solution for Health SystemsAdvanced Laboratory Analytics — A Disruptive Solution for Health Systems
Advanced Laboratory Analytics — A Disruptive Solution for Health Systems
 
Simplifying semantics for biomedical applications
Simplifying semantics for biomedical applicationsSimplifying semantics for biomedical applications
Simplifying semantics for biomedical applications
 
Testing the applicability of digital decision support on a nationwide EHR
Testing the applicability of digital decision support on a nationwide EHRTesting the applicability of digital decision support on a nationwide EHR
Testing the applicability of digital decision support on a nationwide EHR
 

More from Cirdan

Big Data Provides Opportunities, Challenges and a Better Future in Health and...
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Big Data Provides Opportunities, Challenges and a Better Future in Health and...
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Cirdan
 
LIMS in Modern Molecular Pathology by Dr. Perry Maxwell
LIMS in Modern Molecular Pathology by Dr. Perry MaxwellLIMS in Modern Molecular Pathology by Dr. Perry Maxwell
LIMS in Modern Molecular Pathology by Dr. Perry MaxwellCirdan
 
Morphologomics - Challenges for Surgical Pathology in the Genomic Age by Dr. ...
Morphologomics - Challenges for Surgical Pathology in the Genomic Age by Dr. ...Morphologomics - Challenges for Surgical Pathology in the Genomic Age by Dr. ...
Morphologomics - Challenges for Surgical Pathology in the Genomic Age by Dr. ...Cirdan
 
The Practical Utility of Social Media Platforms in Pathology and Laboratory M...
The Practical Utility of Social Media Platforms in Pathology and Laboratory M...The Practical Utility of Social Media Platforms in Pathology and Laboratory M...
The Practical Utility of Social Media Platforms in Pathology and Laboratory M...Cirdan
 
Computer Aided Diagnosis in Pathology: Pros & Cons by Dr. Liron Pantanowitz
Computer Aided Diagnosis in Pathology: Pros & Cons by Dr. Liron PantanowitzComputer Aided Diagnosis in Pathology: Pros & Cons by Dr. Liron Pantanowitz
Computer Aided Diagnosis in Pathology: Pros & Cons by Dr. Liron PantanowitzCirdan
 
The impact of international pathology guidance on the management of patients ...
The impact of international pathology guidance on the management of patients ...The impact of international pathology guidance on the management of patients ...
The impact of international pathology guidance on the management of patients ...Cirdan
 
Dealing with change: Taking you on the journey by Judy Fitzgerald
Dealing with change: Taking you on the journey by Judy FitzgeraldDealing with change: Taking you on the journey by Judy Fitzgerald
Dealing with change: Taking you on the journey by Judy FitzgeraldCirdan
 
Spectral analysis for tumour diagnosis and classification in surgical patholo...
Spectral analysis for tumour diagnosis and classification in surgical patholo...Spectral analysis for tumour diagnosis and classification in surgical patholo...
Spectral analysis for tumour diagnosis and classification in surgical patholo...Cirdan
 
Detection and Analysis of Long Non-Coding RNAs (IncRNAs) in Anopheles funestu...
Detection and Analysis of Long Non-Coding RNAs (IncRNAs) in Anopheles funestu...Detection and Analysis of Long Non-Coding RNAs (IncRNAs) in Anopheles funestu...
Detection and Analysis of Long Non-Coding RNAs (IncRNAs) in Anopheles funestu...Cirdan
 
Integrative Genomics of Non-Small Cell Lung Cancer by Peter McLoughlin
Integrative Genomics of Non-Small Cell Lung Cancer by Peter McLoughlinIntegrative Genomics of Non-Small Cell Lung Cancer by Peter McLoughlin
Integrative Genomics of Non-Small Cell Lung Cancer by Peter McLoughlinCirdan
 
Anthony Gill on Lessons learnt for pathologists from the International Cancer...
Anthony Gill on Lessons learnt for pathologists from the International Cancer...Anthony Gill on Lessons learnt for pathologists from the International Cancer...
Anthony Gill on Lessons learnt for pathologists from the International Cancer...Cirdan
 
Ronan Herlihy on Engaging Clinicians with data on their ordering practices
Ronan Herlihy on Engaging Clinicians with data on their ordering practicesRonan Herlihy on Engaging Clinicians with data on their ordering practices
Ronan Herlihy on Engaging Clinicians with data on their ordering practicesCirdan
 
Damian Fogarty on Pathology in the era of connected health: Linking patients,...
Damian Fogarty on Pathology in the era of connected health: Linking patients,...Damian Fogarty on Pathology in the era of connected health: Linking patients,...
Damian Fogarty on Pathology in the era of connected health: Linking patients,...Cirdan
 
Malcolm Pradhan on Pathology in Clincial Decision Support and the role of Dee...
Malcolm Pradhan on Pathology in Clincial Decision Support and the role of Dee...Malcolm Pradhan on Pathology in Clincial Decision Support and the role of Dee...
Malcolm Pradhan on Pathology in Clincial Decision Support and the role of Dee...Cirdan
 
Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...
Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...
Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...Cirdan
 
David Snead on The use of digital pathology in the primary diagnosis of histo...
David Snead on The use of digital pathology in the primary diagnosis of histo...David Snead on The use of digital pathology in the primary diagnosis of histo...
David Snead on The use of digital pathology in the primary diagnosis of histo...Cirdan
 
Christine Swarbrick discusses a pathology imaging system from a user perspective
Christine Swarbrick discusses a pathology imaging system from a user perspectiveChristine Swarbrick discusses a pathology imaging system from a user perspective
Christine Swarbrick discusses a pathology imaging system from a user perspectiveCirdan
 
Manuel Salto-Tellez on Personalised medicine and the future of tissue pathology
Manuel Salto-Tellez on Personalised medicine and the future of tissue pathologyManuel Salto-Tellez on Personalised medicine and the future of tissue pathology
Manuel Salto-Tellez on Personalised medicine and the future of tissue pathologyCirdan
 
Colin Truesdale on Bringing everyone together for efficient, better healthcare
Colin Truesdale on Bringing everyone together for efficient, better healthcareColin Truesdale on Bringing everyone together for efficient, better healthcare
Colin Truesdale on Bringing everyone together for efficient, better healthcareCirdan
 
Peter O'Halloran on Interfacing, automation and the internet of things – the ...
Peter O'Halloran on Interfacing, automation and the internet of things – the ...Peter O'Halloran on Interfacing, automation and the internet of things – the ...
Peter O'Halloran on Interfacing, automation and the internet of things – the ...Cirdan
 

More from Cirdan (20)

Big Data Provides Opportunities, Challenges and a Better Future in Health and...
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Big Data Provides Opportunities, Challenges and a Better Future in Health and...
Big Data Provides Opportunities, Challenges and a Better Future in Health and...
 
LIMS in Modern Molecular Pathology by Dr. Perry Maxwell
LIMS in Modern Molecular Pathology by Dr. Perry MaxwellLIMS in Modern Molecular Pathology by Dr. Perry Maxwell
LIMS in Modern Molecular Pathology by Dr. Perry Maxwell
 
Morphologomics - Challenges for Surgical Pathology in the Genomic Age by Dr. ...
Morphologomics - Challenges for Surgical Pathology in the Genomic Age by Dr. ...Morphologomics - Challenges for Surgical Pathology in the Genomic Age by Dr. ...
Morphologomics - Challenges for Surgical Pathology in the Genomic Age by Dr. ...
 
The Practical Utility of Social Media Platforms in Pathology and Laboratory M...
The Practical Utility of Social Media Platforms in Pathology and Laboratory M...The Practical Utility of Social Media Platforms in Pathology and Laboratory M...
The Practical Utility of Social Media Platforms in Pathology and Laboratory M...
 
Computer Aided Diagnosis in Pathology: Pros & Cons by Dr. Liron Pantanowitz
Computer Aided Diagnosis in Pathology: Pros & Cons by Dr. Liron PantanowitzComputer Aided Diagnosis in Pathology: Pros & Cons by Dr. Liron Pantanowitz
Computer Aided Diagnosis in Pathology: Pros & Cons by Dr. Liron Pantanowitz
 
The impact of international pathology guidance on the management of patients ...
The impact of international pathology guidance on the management of patients ...The impact of international pathology guidance on the management of patients ...
The impact of international pathology guidance on the management of patients ...
 
Dealing with change: Taking you on the journey by Judy Fitzgerald
Dealing with change: Taking you on the journey by Judy FitzgeraldDealing with change: Taking you on the journey by Judy Fitzgerald
Dealing with change: Taking you on the journey by Judy Fitzgerald
 
Spectral analysis for tumour diagnosis and classification in surgical patholo...
Spectral analysis for tumour diagnosis and classification in surgical patholo...Spectral analysis for tumour diagnosis and classification in surgical patholo...
Spectral analysis for tumour diagnosis and classification in surgical patholo...
 
Detection and Analysis of Long Non-Coding RNAs (IncRNAs) in Anopheles funestu...
Detection and Analysis of Long Non-Coding RNAs (IncRNAs) in Anopheles funestu...Detection and Analysis of Long Non-Coding RNAs (IncRNAs) in Anopheles funestu...
Detection and Analysis of Long Non-Coding RNAs (IncRNAs) in Anopheles funestu...
 
Integrative Genomics of Non-Small Cell Lung Cancer by Peter McLoughlin
Integrative Genomics of Non-Small Cell Lung Cancer by Peter McLoughlinIntegrative Genomics of Non-Small Cell Lung Cancer by Peter McLoughlin
Integrative Genomics of Non-Small Cell Lung Cancer by Peter McLoughlin
 
Anthony Gill on Lessons learnt for pathologists from the International Cancer...
Anthony Gill on Lessons learnt for pathologists from the International Cancer...Anthony Gill on Lessons learnt for pathologists from the International Cancer...
Anthony Gill on Lessons learnt for pathologists from the International Cancer...
 
Ronan Herlihy on Engaging Clinicians with data on their ordering practices
Ronan Herlihy on Engaging Clinicians with data on their ordering practicesRonan Herlihy on Engaging Clinicians with data on their ordering practices
Ronan Herlihy on Engaging Clinicians with data on their ordering practices
 
Damian Fogarty on Pathology in the era of connected health: Linking patients,...
Damian Fogarty on Pathology in the era of connected health: Linking patients,...Damian Fogarty on Pathology in the era of connected health: Linking patients,...
Damian Fogarty on Pathology in the era of connected health: Linking patients,...
 
Malcolm Pradhan on Pathology in Clincial Decision Support and the role of Dee...
Malcolm Pradhan on Pathology in Clincial Decision Support and the role of Dee...Malcolm Pradhan on Pathology in Clincial Decision Support and the role of Dee...
Malcolm Pradhan on Pathology in Clincial Decision Support and the role of Dee...
 
Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...
Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...
Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...
 
David Snead on The use of digital pathology in the primary diagnosis of histo...
David Snead on The use of digital pathology in the primary diagnosis of histo...David Snead on The use of digital pathology in the primary diagnosis of histo...
David Snead on The use of digital pathology in the primary diagnosis of histo...
 
Christine Swarbrick discusses a pathology imaging system from a user perspective
Christine Swarbrick discusses a pathology imaging system from a user perspectiveChristine Swarbrick discusses a pathology imaging system from a user perspective
Christine Swarbrick discusses a pathology imaging system from a user perspective
 
Manuel Salto-Tellez on Personalised medicine and the future of tissue pathology
Manuel Salto-Tellez on Personalised medicine and the future of tissue pathologyManuel Salto-Tellez on Personalised medicine and the future of tissue pathology
Manuel Salto-Tellez on Personalised medicine and the future of tissue pathology
 
Colin Truesdale on Bringing everyone together for efficient, better healthcare
Colin Truesdale on Bringing everyone together for efficient, better healthcareColin Truesdale on Bringing everyone together for efficient, better healthcare
Colin Truesdale on Bringing everyone together for efficient, better healthcare
 
Peter O'Halloran on Interfacing, automation and the internet of things – the ...
Peter O'Halloran on Interfacing, automation and the internet of things – the ...Peter O'Halloran on Interfacing, automation and the internet of things – the ...
Peter O'Halloran on Interfacing, automation and the internet of things – the ...
 

Recently uploaded

❤️Call girls in Jalandhar ☎️9876848877☎️ Call Girl service in Jalandhar☎️ Jal...
❤️Call girls in Jalandhar ☎️9876848877☎️ Call Girl service in Jalandhar☎️ Jal...❤️Call girls in Jalandhar ☎️9876848877☎️ Call Girl service in Jalandhar☎️ Jal...
❤️Call girls in Jalandhar ☎️9876848877☎️ Call Girl service in Jalandhar☎️ Jal...chandigarhentertainm
 
💚😋Chandigarh Escort Service Call Girls, ₹5000 To 25K With AC💚😋
💚😋Chandigarh Escort Service Call Girls, ₹5000 To 25K With AC💚😋💚😋Chandigarh Escort Service Call Girls, ₹5000 To 25K With AC💚😋
💚😋Chandigarh Escort Service Call Girls, ₹5000 To 25K With AC💚😋Sheetaleventcompany
 
Bangalore call girl 👯‍♀️@ Simran Independent Call Girls in Bangalore GIUXUZ...
Bangalore call girl  👯‍♀️@ Simran Independent Call Girls in Bangalore  GIUXUZ...Bangalore call girl  👯‍♀️@ Simran Independent Call Girls in Bangalore  GIUXUZ...
Bangalore call girl 👯‍♀️@ Simran Independent Call Girls in Bangalore GIUXUZ...Gfnyt
 
Call Girls Thane Just Call 9907093804 Top Class Call Girl Service Available
Call Girls Thane Just Call 9907093804 Top Class Call Girl Service AvailableCall Girls Thane Just Call 9907093804 Top Class Call Girl Service Available
Call Girls Thane Just Call 9907093804 Top Class Call Girl Service AvailableDipal Arora
 
Nepali Escort Girl * 9999965857 Naughty Call Girls Service in Faridabad
Nepali Escort Girl * 9999965857 Naughty Call Girls Service in FaridabadNepali Escort Girl * 9999965857 Naughty Call Girls Service in Faridabad
Nepali Escort Girl * 9999965857 Naughty Call Girls Service in Faridabadgragteena
 
❤️♀️@ Jaipur Call Girl Agency ❤️♀️@ Manjeet Russian Call Girls Service in Jai...
❤️♀️@ Jaipur Call Girl Agency ❤️♀️@ Manjeet Russian Call Girls Service in Jai...❤️♀️@ Jaipur Call Girl Agency ❤️♀️@ Manjeet Russian Call Girls Service in Jai...
❤️♀️@ Jaipur Call Girl Agency ❤️♀️@ Manjeet Russian Call Girls Service in Jai...Gfnyt.com
 
Call Girls Amritsar 💯Call Us 🔝 8725944379 🔝 💃 Independent Escort Service Amri...
Call Girls Amritsar 💯Call Us 🔝 8725944379 🔝 💃 Independent Escort Service Amri...Call Girls Amritsar 💯Call Us 🔝 8725944379 🔝 💃 Independent Escort Service Amri...
Call Girls Amritsar 💯Call Us 🔝 8725944379 🔝 💃 Independent Escort Service Amri...Niamh verma
 
Dehradun Call Girls Service ❤️🍑 8854095900 👄🫦Independent Escort Service Dehradun
Dehradun Call Girls Service ❤️🍑 8854095900 👄🫦Independent Escort Service DehradunDehradun Call Girls Service ❤️🍑 8854095900 👄🫦Independent Escort Service Dehradun
Dehradun Call Girls Service ❤️🍑 8854095900 👄🫦Independent Escort Service DehradunNiamh verma
 
VIP Call Girl Sector 88 Gurgaon Delhi Just Call Me 9899900591
VIP Call Girl Sector 88 Gurgaon Delhi Just Call Me 9899900591VIP Call Girl Sector 88 Gurgaon Delhi Just Call Me 9899900591
VIP Call Girl Sector 88 Gurgaon Delhi Just Call Me 9899900591adityaroy0215
 
Hot Call Girl In Chandigarh 👅🥵 9053'900678 Call Girls Service In Chandigarh
Hot  Call Girl In Chandigarh 👅🥵 9053'900678 Call Girls Service In ChandigarhHot  Call Girl In Chandigarh 👅🥵 9053'900678 Call Girls Service In Chandigarh
Hot Call Girl In Chandigarh 👅🥵 9053'900678 Call Girls Service In ChandigarhVip call girls In Chandigarh
 
Call Girls Service Chandigarh Gori WhatsApp ❤7710465962 VIP Call Girls Chandi...
Call Girls Service Chandigarh Gori WhatsApp ❤7710465962 VIP Call Girls Chandi...Call Girls Service Chandigarh Gori WhatsApp ❤7710465962 VIP Call Girls Chandi...
Call Girls Service Chandigarh Gori WhatsApp ❤7710465962 VIP Call Girls Chandi...Niamh verma
 
Nanded Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real Meet
Nanded Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real MeetNanded Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real Meet
Nanded Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real MeetCall Girls Service
 
Hot Call Girl In Ludhiana 👅🥵 9053'900678 Call Girls Service In Ludhiana
Hot  Call Girl In Ludhiana 👅🥵 9053'900678 Call Girls Service In LudhianaHot  Call Girl In Ludhiana 👅🥵 9053'900678 Call Girls Service In Ludhiana
Hot Call Girl In Ludhiana 👅🥵 9053'900678 Call Girls Service In LudhianaRussian Call Girls in Ludhiana
 
VIP Call Girls Sector 67 Gurgaon Just Call Me 9711199012
VIP Call Girls Sector 67 Gurgaon Just Call Me 9711199012VIP Call Girls Sector 67 Gurgaon Just Call Me 9711199012
VIP Call Girls Sector 67 Gurgaon Just Call Me 9711199012Call Girls Service Gurgaon
 
VIP Call Girls Noida Sia 9711199171 High Class Call Girl Near Me
VIP Call Girls Noida Sia 9711199171 High Class Call Girl Near MeVIP Call Girls Noida Sia 9711199171 High Class Call Girl Near Me
VIP Call Girls Noida Sia 9711199171 High Class Call Girl Near Memriyagarg453
 
Dehradun Call Girls Service 08854095900 Real Russian Girls Looking Models
Dehradun Call Girls Service 08854095900 Real Russian Girls Looking ModelsDehradun Call Girls Service 08854095900 Real Russian Girls Looking Models
Dehradun Call Girls Service 08854095900 Real Russian Girls Looking Modelsindiancallgirl4rent
 
Call Girl Price Amritsar ❤️🍑 9053900678 Call Girls in Amritsar Suman
Call Girl Price Amritsar ❤️🍑 9053900678 Call Girls in Amritsar SumanCall Girl Price Amritsar ❤️🍑 9053900678 Call Girls in Amritsar Suman
Call Girl Price Amritsar ❤️🍑 9053900678 Call Girls in Amritsar SumanCall Girls Service Chandigarh Ayushi
 
VIP Call Girl Sector 32 Noida Just Book Me 9711199171
VIP Call Girl Sector 32 Noida Just Book Me 9711199171VIP Call Girl Sector 32 Noida Just Book Me 9711199171
VIP Call Girl Sector 32 Noida Just Book Me 9711199171Call Girls Service Gurgaon
 
❤️♀️@ Jaipur Call Girls ❤️♀️@ Jaispreet Call Girl Services in Jaipur QRYPCF ...
❤️♀️@ Jaipur Call Girls ❤️♀️@ Jaispreet Call Girl Services in Jaipur QRYPCF  ...❤️♀️@ Jaipur Call Girls ❤️♀️@ Jaispreet Call Girl Services in Jaipur QRYPCF  ...
❤️♀️@ Jaipur Call Girls ❤️♀️@ Jaispreet Call Girl Services in Jaipur QRYPCF ...Gfnyt.com
 
Ozhukarai Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real Meet
Ozhukarai Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real MeetOzhukarai Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real Meet
Ozhukarai Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real MeetCall Girls Service
 

Recently uploaded (20)

❤️Call girls in Jalandhar ☎️9876848877☎️ Call Girl service in Jalandhar☎️ Jal...
❤️Call girls in Jalandhar ☎️9876848877☎️ Call Girl service in Jalandhar☎️ Jal...❤️Call girls in Jalandhar ☎️9876848877☎️ Call Girl service in Jalandhar☎️ Jal...
❤️Call girls in Jalandhar ☎️9876848877☎️ Call Girl service in Jalandhar☎️ Jal...
 
💚😋Chandigarh Escort Service Call Girls, ₹5000 To 25K With AC💚😋
💚😋Chandigarh Escort Service Call Girls, ₹5000 To 25K With AC💚😋💚😋Chandigarh Escort Service Call Girls, ₹5000 To 25K With AC💚😋
💚😋Chandigarh Escort Service Call Girls, ₹5000 To 25K With AC💚😋
 
Bangalore call girl 👯‍♀️@ Simran Independent Call Girls in Bangalore GIUXUZ...
Bangalore call girl  👯‍♀️@ Simran Independent Call Girls in Bangalore  GIUXUZ...Bangalore call girl  👯‍♀️@ Simran Independent Call Girls in Bangalore  GIUXUZ...
Bangalore call girl 👯‍♀️@ Simran Independent Call Girls in Bangalore GIUXUZ...
 
Call Girls Thane Just Call 9907093804 Top Class Call Girl Service Available
Call Girls Thane Just Call 9907093804 Top Class Call Girl Service AvailableCall Girls Thane Just Call 9907093804 Top Class Call Girl Service Available
Call Girls Thane Just Call 9907093804 Top Class Call Girl Service Available
 
Nepali Escort Girl * 9999965857 Naughty Call Girls Service in Faridabad
Nepali Escort Girl * 9999965857 Naughty Call Girls Service in FaridabadNepali Escort Girl * 9999965857 Naughty Call Girls Service in Faridabad
Nepali Escort Girl * 9999965857 Naughty Call Girls Service in Faridabad
 
❤️♀️@ Jaipur Call Girl Agency ❤️♀️@ Manjeet Russian Call Girls Service in Jai...
❤️♀️@ Jaipur Call Girl Agency ❤️♀️@ Manjeet Russian Call Girls Service in Jai...❤️♀️@ Jaipur Call Girl Agency ❤️♀️@ Manjeet Russian Call Girls Service in Jai...
❤️♀️@ Jaipur Call Girl Agency ❤️♀️@ Manjeet Russian Call Girls Service in Jai...
 
Call Girls Amritsar 💯Call Us 🔝 8725944379 🔝 💃 Independent Escort Service Amri...
Call Girls Amritsar 💯Call Us 🔝 8725944379 🔝 💃 Independent Escort Service Amri...Call Girls Amritsar 💯Call Us 🔝 8725944379 🔝 💃 Independent Escort Service Amri...
Call Girls Amritsar 💯Call Us 🔝 8725944379 🔝 💃 Independent Escort Service Amri...
 
Dehradun Call Girls Service ❤️🍑 8854095900 👄🫦Independent Escort Service Dehradun
Dehradun Call Girls Service ❤️🍑 8854095900 👄🫦Independent Escort Service DehradunDehradun Call Girls Service ❤️🍑 8854095900 👄🫦Independent Escort Service Dehradun
Dehradun Call Girls Service ❤️🍑 8854095900 👄🫦Independent Escort Service Dehradun
 
VIP Call Girl Sector 88 Gurgaon Delhi Just Call Me 9899900591
VIP Call Girl Sector 88 Gurgaon Delhi Just Call Me 9899900591VIP Call Girl Sector 88 Gurgaon Delhi Just Call Me 9899900591
VIP Call Girl Sector 88 Gurgaon Delhi Just Call Me 9899900591
 
Hot Call Girl In Chandigarh 👅🥵 9053'900678 Call Girls Service In Chandigarh
Hot  Call Girl In Chandigarh 👅🥵 9053'900678 Call Girls Service In ChandigarhHot  Call Girl In Chandigarh 👅🥵 9053'900678 Call Girls Service In Chandigarh
Hot Call Girl In Chandigarh 👅🥵 9053'900678 Call Girls Service In Chandigarh
 
Call Girls Service Chandigarh Gori WhatsApp ❤7710465962 VIP Call Girls Chandi...
Call Girls Service Chandigarh Gori WhatsApp ❤7710465962 VIP Call Girls Chandi...Call Girls Service Chandigarh Gori WhatsApp ❤7710465962 VIP Call Girls Chandi...
Call Girls Service Chandigarh Gori WhatsApp ❤7710465962 VIP Call Girls Chandi...
 
Nanded Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real Meet
Nanded Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real MeetNanded Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real Meet
Nanded Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real Meet
 
Hot Call Girl In Ludhiana 👅🥵 9053'900678 Call Girls Service In Ludhiana
Hot  Call Girl In Ludhiana 👅🥵 9053'900678 Call Girls Service In LudhianaHot  Call Girl In Ludhiana 👅🥵 9053'900678 Call Girls Service In Ludhiana
Hot Call Girl In Ludhiana 👅🥵 9053'900678 Call Girls Service In Ludhiana
 
VIP Call Girls Sector 67 Gurgaon Just Call Me 9711199012
VIP Call Girls Sector 67 Gurgaon Just Call Me 9711199012VIP Call Girls Sector 67 Gurgaon Just Call Me 9711199012
VIP Call Girls Sector 67 Gurgaon Just Call Me 9711199012
 
VIP Call Girls Noida Sia 9711199171 High Class Call Girl Near Me
VIP Call Girls Noida Sia 9711199171 High Class Call Girl Near MeVIP Call Girls Noida Sia 9711199171 High Class Call Girl Near Me
VIP Call Girls Noida Sia 9711199171 High Class Call Girl Near Me
 
Dehradun Call Girls Service 08854095900 Real Russian Girls Looking Models
Dehradun Call Girls Service 08854095900 Real Russian Girls Looking ModelsDehradun Call Girls Service 08854095900 Real Russian Girls Looking Models
Dehradun Call Girls Service 08854095900 Real Russian Girls Looking Models
 
Call Girl Price Amritsar ❤️🍑 9053900678 Call Girls in Amritsar Suman
Call Girl Price Amritsar ❤️🍑 9053900678 Call Girls in Amritsar SumanCall Girl Price Amritsar ❤️🍑 9053900678 Call Girls in Amritsar Suman
Call Girl Price Amritsar ❤️🍑 9053900678 Call Girls in Amritsar Suman
 
VIP Call Girl Sector 32 Noida Just Book Me 9711199171
VIP Call Girl Sector 32 Noida Just Book Me 9711199171VIP Call Girl Sector 32 Noida Just Book Me 9711199171
VIP Call Girl Sector 32 Noida Just Book Me 9711199171
 
❤️♀️@ Jaipur Call Girls ❤️♀️@ Jaispreet Call Girl Services in Jaipur QRYPCF ...
❤️♀️@ Jaipur Call Girls ❤️♀️@ Jaispreet Call Girl Services in Jaipur QRYPCF  ...❤️♀️@ Jaipur Call Girls ❤️♀️@ Jaispreet Call Girl Services in Jaipur QRYPCF  ...
❤️♀️@ Jaipur Call Girls ❤️♀️@ Jaispreet Call Girl Services in Jaipur QRYPCF ...
 
Ozhukarai Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real Meet
Ozhukarai Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real MeetOzhukarai Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real Meet
Ozhukarai Call Girls 👙 6297143586 👙 Genuine WhatsApp Number for Real Meet
 

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)
  • 4. Runciman et al MJA 197 (2) · 16 July 2012
  • 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
  • 17. Eugenio H. Zabaleta, Ph.D. MedCentral Health System, OH
  • 18.
  • 19.
  • 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
  • 24. Free text analysis in clinical decision support systems
  • 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

  1. 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.