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eHealth and Statistics
Sally Goodenough and Miriam Bluhdorn
HIMAA Symposium 26th September 2008
AIHW & national statistics
• National statistics– Health
– Community services
– Housing
• Statistics based on data from the health sector form the evidence base
eHealthEHR / IEHR
POLICYWhat should be done?
METADATADefinitions
FormatRepresentationCollection and
useContext
STATISTICSCounts
PercentagesAverages
RatiosOther data sources
NMDSSurveys
ABS Census
DATANumbers
CodesDates
$PLANNINGHow and where?
RESEARCH / EPIDEMIOLOGY
What evidence?
PROVISIONWhat happened?
PERFORMANCEDid it work?
Statistics value chain
eHealth means BIG changes
• What will the impact be for statistics?
–Can continuity be assured?
–Opportunities for new or better statistics?
• AIHW-NEHTA project to explore the possibilities
Data re-use
• Data moves from the clinical to the statistical realm
• Data is re-purposed, then re-used
• Statistics produced then benefit the health sector
Moving data between systems
STATISTICAL SYSTEMS
HEALTH SYSTEMS
Process data
ProcessObtain
data Analyse
Data requests Data supply
Collect data
Key roles
• Health consumer• User (& specifier)• Requirements coordinator & approver• ICT change manager • Data collector• Trusted intermediary• Knowledge builder
Key processes
STATISTICAL SYSTEMS
ProcessObtain
data
Analyse &
publish
Interview patient &
record diagnosis
Increased BP
Code (I10), aggregate data (hospital admitted
patient statistics), transform ICD code to AR-DRG F67B, apply
cost weight
National strategy to reduce hypertension in adults over 45
Release report on
increases in NCDs
HEALTH SYSTEMS
Process data
Collect data
Calculate population morbidity
rates
Process step is complex
ProcessCollect Analyse
Capture, assemble, data entry, quality control
Transform and aggregate (data about groups)
Run extraction or integration algorithms (filters, linkage, joins)
Apply release/access/& use rules
Cleanse, validate, edit
Transform instance data into different forms, code, classify, derive
(data about individuals)FeedbackFeedback
ArchetypesCasemix
NEHTA
Data supply chains
SNOMED CT
HL7 v3Data Dictionaries
ICD-10-AM
Harmonization Shared Electronic Health Record
Extraction
Fitness for purpose
WHO
Relationship modelling
Trusted Intermediary
Consumers
Ontologies
Secondary use
ISO
PrivacyUnique identifiers
Changes to environment
• Standardised & interconnected systems
• Need explicit business rules for automation
• The consumer – the “person in the centre”
• Privacy & auditing constraints
• New stakeholders
• Unknown pace of change
Impact on HIM’s
• Be patient
• Leverage your skill set
• Need for rapid learning and development
The future
• We don’t know all the answers
• But we have a way to think about the questions!
• Next project is to test impacts on existing statistical data sets
Thank you!