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!