24th february 20094th health it summer showcase1 4 th summer health it showcase -2009 health...

24th February 2009 4th Health IT Summer Showcase 1

4th Summer Health IT Showcase -2009

Health Information Technologies Research Laboratory

School of IT

University of Sydney

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HITRL Objectives• Research in Natural Language processing

for medical content

• Research into Clinical Information Systems

• Research into the use of terminologies -SNOMED CT, Apache IV, NIC, NOC, etc.

• We are learning how to build such functionality

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Current Enhancement Technologies

• Clinical Data Analytics Language (CliniDAL)

• Generative Clinical Information Management Systems (GCIMS)

• Bolt-on to existing systems

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1st Session

• Pathology Research and Clinical Data Analytics Language - Cleverer interfaces for research on clinical databases

• Visual Annotator - getting at the correct language - clinical notes, synoptic reports - automating annotation

• Generating Interoperable Clinical Information Systems - Multidisciplinary & Nursing CIS, Trauma CIS

• Intelligent Notes System - Automatic text correction with information retrieval on the ward rounds

Health Information Technologies Research Laboratory

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2nd Session• Identifying SNOMED CT codes in clinical

notes

• Identifying medical concepts in published papers

• Recognising co-morbidities in clinical notes of Obesity patients

• Unpacking clinical notes - recognising words and non-words

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Other Projects in 2008• Semester 1

– CLINIDAL installed on SWAPS AP data warehouse– Pathology Classification on SWAPS AP database– Handovers generating system for the ICU

• Semester 2– Graphical viewer for SNOMED CT on Term Server– Workflow on General Medical Wards - BMDH– Software testing for CliniDAL– CLINIDAL installed in CareVue data warehouse– Intelligent Notes for ICU

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Structured Reporting for Pathology Results

• Melanoma - supported by QUPP in collaboration with Dr Richard Scolyer and Dr Raj Murali at the RPAH

• Breast Cancer - supported by the BCI Westmead with Dr John Boyages and Dr Nehmat Houssami

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Melanoma-Executive Summary • Project objectives achieved• 477 histopathology reports annotated for 22

concepts of data by four annotators• Linguists missed 6.0% on average of

pathologists labels• 19 fields appear to be reliably computable• Gold-standard set of reports assembled• By-products: advice and training materials on

presentation of reports.

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Ultimate Goals of ProjectA. provide feedback to pathologists re the content of their

reports - are they including all the key information that:i. determines the patients prognosis andii. directs their managementThe key features that determine i & ii are:

a. Breslow thicknessb. mitotic rate

c. Clark level

d. ulceratione. margins (all of them) 

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Ultimate GoalsB. data extraction for

i. cancer registriesii. research 

C. automated generation of synoptic reports from text reportsi. could be done when the pathologist has constructed the narrative report and included in the final report that is sent to the requesting clinician  ii. performed at a later date

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BACKGROUND TO THE STUDY • Materials

– 477 histopathology reports– Photocopied– Scanned– OCRed– Spell checked/ proof read– Anonymised– Stored and maintained in a revision repository– Annotated by pathologist and linguists

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Annotation Discrepancies Involving Language

• Noun Phrase vs Verb phase usage– Arising in a dysplastic naevus (useful verb)– Patchy regression was seen (not useful)

• Interpretive Annotations– Early, Intermediate, Late changed to– TILS, Fibrosis, Loss of Rete Ridges

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Concept SetAssociated naevus (type) Neurotropism

Breslow thickness (mm) Other Pattern(s)

Clark level Predominant cell type(s)

Classification/Main Pattern Regression

Dermal mitoses (per mm2) Rete Ridges

Desmoplasia (% of dermal invasive tumour) Satellites

Diagnosis Site

Distance from tumour to deep margin (mm) Solar elastosis

Fibrosis TILS

Nearest lateral margin to dermal invasive component (mm)

Ulceration (mm)

Nearest lateral margin to in-situ component (mm) Vascular/lymphatic invasion

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Results of the Annotations • Most important Concepts accurately

identified• Linguists had better agreement than the

pathologists• Three codes were not reliable

– TILS, Fibrosis, Rete Ridges

• Reliable Computation of important elements achievable

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Comparative Inter-annotator Agreement Second Annotation Round

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Diagnosis

Site

Clark level Lateral MarginBreslow Thickness

Classification

MitosesUlceration

Associated naevusVascular invasion

TILs

RegressionCell TypeDeep MarginNeurotropismIn-situ Margin

Fibrosis

Solar ElastosisDesmoplasia

Satellites

Other PatternsRete RidgesEarly (TILs)Intermediate

Late (Fibrosis)

Concept Tag

Inter-annotator Agreement

P1 & L1

P1 & L2

P2 & L1

P2 & L2

Figure 3 - Comparative second round inter-annotator agreement, scaled by number of annotations.

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Tags annotated by Pathologists missed by Linguists

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Comparison of Gold Standard and Linguists

Concept Linguist 1 Linguist 2

Breslow Thickness

.97 .97

Mitotic Rate .95 .94

Clark level .97 .94

Ulceration .94 .98

Margins .92/.92/.81 .98/.93/.94

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Observations about the corpus contents

• Occasional inconsistencies between report body and conclusions, e.g. size = .1mm vs 1mm

• Highly variable standard of contents

• Lateral margins not well reported

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Results of simple extraction for a Structured Report

• Breslow Thickness simple classifier

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Summary of the Project Results • Reliable annotations can be made for all the important

concepts in melanoma pathology reports.• The prospect of building very reliable computational

aides for automatically generating structured reports are high.

• The most uncertain aspect of the study is to understand the smallest training set that is needed to build an effective structured report computational populator.

• This approach can be used ot identify what is needed in any structured report -

“the text tells more than the experts”

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Breast Cancer Synoptic Reports• 33 categories

• 120+ reports

• Information extraction

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Projects for 20091. Snomed CT subset for ICU

2. Snomed CT subset for ED

3. Multiple CIS for Aged Care demonstrated

4. Trauma CIS verified and tested

5. First version of IC Realtime Audit IS (ICRAIS)

6. Automatic post co-ordination of clinical notes

7. Lexical and morphological disambiguation of clinical notes

8. Automatic computation of structured reports for melanoma and breast cancer

9. Proven use of CLINIDAL for pathology & ICU CIS

10. Mapping SNOMED CT to ICD 10 AM for ICU notes

11. Design of Information Model for ICD 11 (WHO)

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Partners• Breast Cancer Institute• RPAH - ICU, ED, AP• SWAPS• Blacktown-Mt Druitt Hospital - Nursing &

Midwifery• QUPC• SEALS

• Concorde - ED• NEHTA