1. 24th February 20094th Health IT Summer Showcase1 4 th Summer Health IT Showcase -2009 Health Information Technologies Research Laboratory School of IT University of Sydney

2. 24th February 20094th Health IT Summer Showcase2 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

3. 24th February 20094th Health IT Summer Showcase3 Current Enhancement Technologies Clinical Data Analytics Language (CliniDAL) Generative Clinical Information Management Systems (GCIMS) Bolt-on to existing systems

4. 24th February 20094th Health IT Summer Showcase4 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

5. 24th February 20094th Health IT Summer Showcase5 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

6. 24th February 20094th Health IT Summer Showcase6 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

7. 24th February 20094th Health IT Summer Showcase7 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

8. 24th February 20094th Health IT Summer Showcase8 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.

9. 24th February 20094th Health IT Summer Showcase9 Ultimate Goals of Project A. provide feedback to pathologists re the content of their reports - are they including all the key information that: i. determines the patients prognosis and ii. directs their management The key features that determine i & ii are: a. Breslow thickness b. mitotic rate c. Clark level d. ulceration e. margins (all of them)

10. 24th February 20094th Health IT Summer Showcase10 Ultimate Goals B. data extraction for i. cancer registries ii. research C. automated generation of synoptic reports from text reports i. 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

11. 24th February 20094th Health IT Summer Showcase11 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

12. 24th February 20094th Health IT Summer Showcase12 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

13. 24th February 20094th Health IT Summer Showcase13 Concept Set Associated naevus (type)Neurotropism Breslow thickness (mm)Other Pattern(s) Clark levelPredominant cell type(s) Classification/Main PatternRegression Dermal mitoses (per mm2)Rete Ridges Desmoplasia (% of dermal invasive tumour)Satellites DiagnosisSite Distance from tumour to deep margin (mm)Solar elastosis FibrosisTILS Nearest lateral margin to dermal invasive component (mm) Ulceration (mm) Nearest lateral margin to in-situ component (mm)Vascular/lymphatic invasion

14. 24th February 20094th Health IT Summer Showcase14 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

15. 24th February 20094th Health IT Summer Showcase15 Figure 3 - Comparative second round inter-annotator agreement, scaled by number of annotations.

16. 24th February 20094th Health IT Summer Showcase16 Tags annotated by Pathologists missed by Linguists

17. 24th February 20094th Health IT Summer Showcase17 Comparison of Gold Standard and Linguists ConceptLinguist 1Linguist 2 Breslow Thickness.97 Mitotic Rate.95.94 Clark level.97.94 Ulceration.94.98 Margins.92/.92/.81.98/.93/.94

18. 24th February 20094th Health IT Summer Showcase18 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

19. 24th February 20094th Health IT Summer Showcase19 Results of simple extraction for a Structured Report Breslow Thickness simple classifier

20. 24th February 20094th Health IT Summer Showcase20 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”

21. 24th February 20094th Health IT Summer Showcase21 Breast Cancer Synoptic Reports 33 categories 120+ reports Information extraction

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24. 24th February 20094th Health IT Summer Showcase24 Projects for 2009 1.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)

25. 24th February 20094th Health IT Summer Showcase25 Partners Breast Cancer Institute RPAH - ICU, ED, AP SWAPS Blacktown-Mt Druitt Hospital - Nursing & Midwifery QUPC SEALS Concorde - ED NEHTA