Download Narelle Grayson

Automated data collection
mechanisms for cancer registries
Data Governance 2012
Narelle Grayson
Cancer Institute NSW
Contents
1. Background – why automate?
2. Projects to achieve automation
Background – why automate?
The number of new cases of cancer is increasing…
Actual and Projected new cancer cases and cancer deaths 1972-2021
Number of new cases or deaths
60000
50000
40000
New cases
Deaths
30000
20000
10000
0
1972
1976
1981
1986
1991
1996
2001
2006
Year of diagnosis or death
2011
2016
CINSW 2011. Cancer incidence and mortality: projections 2011 to 2021. Cancer Institute NSW, Sydney: May 2011
2021
Why Automate?
Increased
Requirements
- Timeliness
Chemotherapy
Radiotherapy
data items
Surgery
data items
data items
data items
New data
sources
Pathology
data
items
Increased
number of
notifications
Scarce
resources
Expanding
Data
Collection
Scope
Imaging
data
items
REGISTRY
Health Care System
Projects to achieve automation
• Natural Language Processing
(NLP)
• Enhanced chemotherapy reporting
NATURAL LANGUAGE
PROCESSING
Two projects…
1. Medical free text analysis:
Cancer Information processing
and reporting
2. Capturing cancer stage
and recurrence for
population-based registries
Funded by Cancer Australia
Project 1
Objectives
1. Develop automated process to transform scanned
pathology reports to electronic free text.
2. Develop natural language processing platform for
the extraction of information from medical free text
3. Evaluate through a controlled clinical trial the effort
required for a fully-manual versus semi-automated
abstraction processes.
Cancer notification variables
•
•
•
•
Primary site
Histological type
Laterality
Histological grade
NSW CCR processing of pathology
notifications
Paper pathology
reports
Scan to create TIFF
images
Data entry and
coding
Process
Cancer Institute
NSW
AEHRC
AEHRC
Cancer Institute
NSW
Randomly select eligible
patients from CCR
Extract TIFF image of
pathology report
Run TIFF image through
OCR engine
Create de-identified file
Post-processing
Process text file using
MEDTEXT
Create synoptic report
Extract and code cancer
data items
Clinical trial
De-identification
Number of files
Dr Name
Patient Name
Date
Facility
500
15
5
5
1
Percent
3%
1%
1%
0.2%
Reasons included:
“Dr” did not proceed:
Reported by, Requested by, Authorised by
Dates:
“11” converted to II by OCR e.g. 15/05/II
Year merged with another number in report e.g. 29/06/201111
Facility:
Found within body of text – ‘slides received courtesy of “Facility x”’
Patient Name:
“Patient name xxxxxx xxxxxx xxxxxx”
Other benefits…
• Research projects
• Automatically de-identify pathology reports
• Automatically identify and select reports for
eligible patients
• Automatically abstract relevant data items
Structured pathology reporting
• HL7 Messaging and Archetypes
• National E-Health Transition Authority (NEHTA)
Project 2
• Imaging reports – PET, CT, MRI, Bone Scans
• Augment cancer registry data with:
• clinical stage at diagnosis for lung and pancreatic cases
• metastases at diagnosis for all cancers
• recurrence for all cancers
• Inform:
• Survival by stage at diagnosis including trends over time
• Time from diagnosis to recurrence
• Patterns of disease including incidence and mortality by
stage at diagnosis
Pilot sites and contractors
Pilot sites
• Cancer Council Victoria
• 2 Pilot sites
• Lake Imaging, Ballarat
• Peter MacCallum Cancer Centre – Cancer Imaging
• Cancer Institute NSW
• 1 Pilot site – Westmead Radiology Department
Contractor
• Health Language Laboratories
• TumourTExtract
Diagnostic imaging reports –
Westmead Radiology Department
Imaging report type
CT
Number of reports
% expected cancer
22,000
25
PET
2,200
90
MRI
7,800
50
800
30-60
Bone Scan
Data capture process
TumourTExtract Development
Preliminary results – TumourTExtract
development
• Comparison of the TumourTExtract classifier module
(Version 0.1) output and manual review by a clinical
coder:
• 99.8% reports (399/400) accurately classified as eligible or
not-eligible for transfer to the population-based registry
• 98.4% reports for cancer (126/128) the primary tumour
stream is accurately identified
• 98.9% reports (89/90) accurately identified as referrals for
diagnostic staging (including investigation of metastases) or
investigation for possible recurrence or relapse.
Evaluation
• Assessment of the:
• feasibility of NLP for efficient, effective and sustainable data
capture from diagnostic imaging reports; and
• completeness and accuracy of the data collected for
population-based registry reporting.
• Final report to Cancer Australia on project outcomes
including a recommendation for the national
collection of cancer stage, metastases and
recurrence by population-based registries.
Next steps
2011
2013
• TumourTExtract development completed
• TumourTExtract implemented at 3 pilot sites
• Imaging data linked to population-based registries –
NSW & Victoria
• AJCC TNM stage and first recurrence collected
• Evaluation data collected
• Evaluation data analysed
• Final recommendation to Cancer Australia
ENHANCED REPORTING OF
CHEMOTHERAPY DATA
Treatment modalities
•
Evaluate patterns of care, the effectiveness of different treatment
modalities, and treatment by patient outcome.
•
Surgery, Radiotherapy, Chemotherapy
•
? % cancer patients receive chemotherapy
• Clinical Cancer Registry – Public sector only
• 29% Medical oncology services – Private sector
•
Under-notification from medical oncology departments
•
Aim: enhance reporting from medical oncology departments
0.0%
Cumulative Percent
Parkes District Hospital
Macquarie Hospital
Mudgee Health Service
Ballina District Hospital
Strathfield private hospital
Lingard Private Hospital
Moree District Health Service
Cooma Oncology
Baulkham Hills Private Hospital, Cancer Therapy Suite
Manly Waters Private Hospital
Muswellbrook Chemotherapy
Broken Hill Health Service
Milton Ulladulla Hospital
Cowra District Hospital
Royal Hospital for Women
North Gosford Private Hospital Oncology Unit
Westmead Private Hospital - Chemotherapy Unit
Bathurst Base Hospital (Daffodil cottage)
Prince of Wales Hospital - PR
Orange Base Hospital…
Griffith Oncology
St Vincent's Private Hospital, Lismore
Grafton Base Hospital
Southern Highlands Private Hospital Bowral
Northern Cancer Institute Frenchs Forest
Manning Rural Referral Hospital Taree
Armidale Rural Referral Hospital
Bourke St Oncology - PR
Bega Valley Oncology/Haematology
Young Oncology
Dubbo Base Hospital
Tamworth Rural Referral Hospital
Murray Valley Private Wodonga
Eurobodalla Oncology (Moruya)
Southern Medical Day Centre Wollongong - PR
Shoalhaven Oncology Day Care…
Albury Base Day Oncology Unit
Wyong
The Mater Misericordiae Hospital, Crows Nest Sydney
Macarthur Cancer Centre Therapy Centre (Campbelltown)
John Hunter's Children's Hospital
Bankstown Oncology Unit
North Coast Cancer Institute Lismore
100.0%
Prince of Wales Hospital
Blacktown Oncology
Gosford Hospital
Newcastle Private
North Coast Cancer Institute Port Macquarie
The Sutherland Hospital Day Unit - PR
Liverpool Cancer Therapy Centre
Northern Cancer Institute Sydney - PR
Riverina Cancer Care Centre - PR
North Coast Cancer Institute Coffs Harbour
Illawarra Cancer Centre…
St George Cancer Care Centre Kogarah
Calvary Mater Newcastle
Sydney Adventist Hospital
Sydney Children's Hospital
Nepean Cancer Care Centre
St Vincent's Hospital, HOAC Darlinghurst
Westmead Children's Hospital - Oncology Treatment Centre
The Tweed Hospital Medical Oncology Unit
Royal Prince Alfred Hospital
Royal North Shore Hospital
Westmead Cancer Centre
St George Private Hospital
20.0%
Concord Repatriation General Hospital
Estimated throughput by Medical
Oncology site
Percent of cancer visits in chemotherapy outpatient departments in NSW
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
25
10.0%
Medical Oncology departments by system and
throughput
Medical oncology system
Number of sites
% throughput
Paper
31
26.4
Electronic1
37
74.6
Total
68
100.0
1. Includes sites where an OMIS is planned. Includes MOSAIQ, Cerner, CHARM, ARIA, Polymedics, LANTAS and Isoft
Enhanced reporting of chemotherapy
data project
Aims:
• Contribute to a sustainable automated data collection process
• Provide the ability to measure and monitor:
• aspects of treatment e.g. toxicity and safety
• adherence to best evidence based practice
• Progress against benchmarks
• Enhanced reporting capability
Objectives:
• Develop a reporting extract compliant with the medical oncology extract
specification and data dictionary
• Implement the extract in another cancer centre using the same Oncology
Management Information System (OMIS).
Pilot sites
Local Health
District
System
South Eastern
Sydney
MOSAIQ
Illawarra Cancer
Care Centre
Prince of Wales
Cancer Care
ARIA
St George Hospital
Cancer Care Centre
Newcastle Mater
Hospital Cancer Care
Network
Macarthur Cancer
Therapy Centre
Liverpool and Bowral
Cancer Therapy
Centre
South Western MOSAIQ
Sydney
Worksite
Implementation site
Preliminary results
Proof of concept
• Extracts can be developed at each worksite
• Extracts can be implemented at each implementation site
Main findings:
• Different sites utilise the same system differently
• Workflow
• Custom fields/screens
• Modification of extract based on site
• Work toward standard utilisation of systems
Conclusion
Project 1:
• Developed automated process to transform scanned pathology reports to
electronic free text.
• Extract items of interest
• Unexpected benefits – facilitate use of pathology reports for research
• Structured pathology reporting – HL7 messaging and archetypes will
enhance automation
Project 2:
• Vic pilot site 1 in progress
• Identified NSW pilot site
• Challenges – define metastases, recurrence
Project 3:
• Extracts successfully developed and implemented in pilot sites
• Other systems to follow
• Challenges:
• non-standard implementation of systems
• Approx. 50% paper-based
Acknowledgements
Project 1: Medical free text analysis: Cancer Information processing and reporting
Project team: Anthony Nguyen, Guido Zuccon, Sandra Wickman, Anton
Bergheim, Narelle Grayson, Sanchia Aranda
Project 2: Capturing cancer stage and recurrence for population-based registries
Principal Investigators: Helen Farrugia, Graham Giles, Narelle Grayson
Victorian Pilot Sites: Lake Imaging, Ballarat and Peter MacCallum Cancer
Centre – Cancer Imaging
NSW Pilot site: Westmead Radiology Department
Project team: Georgina Marr, Meg Callander, Jon Patrick, Sandra Wickman,
Anton Bergheim
Project 3: Enhanced reporting of chemotherapy data
Project team: Shelley Rushton, Sandra Wickman, Anton Bergheim, Narelle
Grayson, Sanchia Aranda