Presented by Koray Atalag National Institute for Health Innovation, University of Auckland

1. A Stocktake of New Zealand’s
Healthcare Datasets
Koray Atalag MD, PhD, FACHI
k.atalag@nihi.auckland.ac.nz

2. 2
Average Health Care Spending per Capita, 1980–2010
Adjusted for Differences in Cost of Living
Dollars ($US)
9000
US ($8,233)
NOR ($5,388)
8000
SWIZ ($5,270)
7000
NETH ($5,056)
DEN ($4,464)
6000
CAN ($4,445)
5000
GER ($4,338)
FR ($3,974)
4000
SWE ($3,758)
AUS ($3,670)*
3000
UK ($3,433)
2000
JPN ($3,035)*
NZ ($3,022)
1000
0
1980
1984
* 2009
Source: OECD Health Data 2012.
1988
1992
1996
2000
2004
2008
THE
COMMONWEALTH
FUND

3. Doctors’ Use of Electronic Medical Records
in Their Practice, 2009 and 2012
Percent
100
99 98 97 98 97 97
96 97 95
92 94
Series5
Series1
88
82
80
72
69 68 67
56
60
46
37
40
41
20
0
1
2
3
4
5
6
7
8
Source: 2009 and 2012 Commonwealth Fund International Health Policy Survey of Primary Care Physicians.
9
10
11
3

4. Doctor Can Electronically Exchange Patient Summaries
and Test Results with Doctors Outside their Practice
Percent
100
80
60
55
52
49
49
45
39
40
38
31
27
22
20
14
0
1
2
3
4
5
6
7
Source: 2012 Commonwealth Fund International Health Policy Survey of Primary Care Physicians.
8
9
10
11
4

5. The Study
• Commissioned by
• Motivation:
(Feb-Jul 2013)
– NZ has excellent intl. profile: good healthcare, little cost
– health IT is one of the best; NHI in use >20yrs; rich and linkable data
collections
• Aim: is there $value in our datasets, can we overcome barriers to
create extra funding for healthcare?
• Scope includes:
– Stocktake (NIHI)
– Demand side analysis (US – Harvard collaborator)
– Value assessment & barriers
• First phase: full stocktake, lite versions of rest

6. Terminology & Scope
Health data
Healthcare datasets
Health information assets
• Clinical and administrative data collected or derived mainly
for research and planning purposes (aka secondary
use), including:
– national collections and surveys
– research databases and clinical registries
– transactional data coming from healthcare delivery (Clinical
Data Repositories or individual HIS/EHR)
– Others: ACC, DHBs, Quality/Performance datasets
– Sector innovation projects (Shared Care, NZePS etc.)

7. Our Agenda
• To look at the full value (not just $$) of these datasets within
an appropriate legal, ethics and privacy framework.
• Particularly interested in the value of having a comprehensive
understanding of what datasets are being held, how they
might appropriately be linked and how they could be better
used to inform clinical practice & public health policy
• A deeper look at barriers; e.g. not just privacy issues, but also
the unnecessary time/effort for legitimate researchers
• Take responsibility and start a public debate to establish a
comprehensive ethics and privacy framework

8. Methods
• Literature review and an environmental scan
• Initial categories & sources list (mostly own
knowledge and the obvious)
• Further refinement and expansion
• Interview questions & stocktake template
• Correspondence (email & interviews)
• Data quality checks and further editing
• Analysis & results

9. Categories
•
•
•
•
•
•
•
•
•
District Health Boards (DHB)
Administrative
Public health and research
Surveys
Registries & Screening databases
National collections
Primary/Community Care Providers
Integrated Care initiatives (e.g. Shared Care)
Labs and Imaging centres

10. Correspondence
• Study context info and questionnaire sent via email
• Key roles in organisations contacted:
–
–
–
–
clinical directors,
chief information officers,
decision support teams
enterprise architects
• Many attempts and follow-up necessary due to very busy
time of the year
• Several organisations could not provide detailed
information due to other priorities
– for these cases, it was decided to include only a brief
title/description of the datasets (with publicly available
information) with a prompt to follow up in Phase Two

11. Study Context Information
NZ Healthcare Dataset – a stocktake study
This is the first of a series of studies that aims to identify existing healthcare datasets
(both administrative and clinical data) collected as part of routine clinical practice
or collated from other sources. In this part of the study, we are collecting
information regarding a description of the data held in each ‘database,’ the nature
of its data, the volume of data, the quality of data and the process and governance
related to the data collection and management. A follow-up study will look at what
potential additional value these datasets could have to the country over and above
their direct role in improving healthcare. The study will also review the ethical and
privacy related barriers to the use of the datasets for international research
purposes.
Notwithstanding whether or not this potential value is seen as being able to be
realised from a practical perspective, the study will provide a thorough
understanding of what datasets exist in the country and it will increase our
understanding of the opportunities and risks associated with these datasets, for
example reviewing whether routinely used de-identification mechanisms are
adequate to ensure patient privacy.

12. Questionnaire

14. Iterative Process
• We also looked at:
– Organisations’ websites,
– (annual) reports, media releases and other publications
– Other documents that the participants shared with the project
team
to better understand these datasets and the organisational
data governance policies
• Correspondences also suggested a few more relevant
characteristics;
– for example whether a dataset is longitudinal, presence of data
access protocols and when data can be made accessible etc.
• This feedback was built into the study questionnaire for
later iterations of data collection

15. Organisation of Findings
• Stocktake spreadsheet (xls) organised by dataset
and custodian organisations in natural order
• Stocktake information can be filtered and sorted
according to:
– Categories
– Scope: whether coverage is at a national or regional
level or provider specific or contains a certain cohort.
– Whether individually identifiable and linkable

16. Results
Dataset categories
# of datasets
District Health Boards (DHB)
Administrative
Public health and research
Surveys
Registries & Screening databases
National collections
Primary/ Community Care Providers
Integrated Care
Labs and Imaging centres
Total:
76
73
60
32
17
14
8
3
3
286
% of total by
number
27%
26%
21%
11%
6%
5%
3%
1%
1%

17. Graphical Look
By category
By scope

18. Data Custodian Organisations
# of datasets
MoH
105
The University of Auckland
52
ADHB
29
Canterbury and West Coast DHB
26
WDHB
19
Cancer networks - Southern
8
Nurse Maude
5
ESR (Institute of Environmental Science and Research Ltd)
3
Diabetes Projects Trust
3
CMDHB
3
Health Sponsorship Council (HSC)
3
Plunket
2
ASH
2
Pharmac
2
Northern DHB Support Agency
1
Southern DHB
1
NZ national genetic services
1
CBG Health Research
1
Dr Info.
1
GUiNZ / UoA
1
Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS)
1
NZSSDS
1
Waikato DHB
1
Enigma/ UoA
1
Flinders University
1
Princess Alexandra Hospital, Brisbane
1
Cancer Network - Midland
1
Diagnostic MedLab
1
ACC
1
BRC (now Research NZ, http:/ / www.researchnz.com/ ), ALAC
1
LabTests
1
healthAlliance
1
Each renal service has its own local data collection, e.g. The Royal Adelaide Hospital,
South Australia
1
HealthStat
1
individual GP Practices
1
HSA Global
1
individual PHOs
1
Medibank Health Solutions in association with St John
1
Total:
286

19. Interpretation
• >80% of datasets belong to a small number of custodian
organisations  easier for F/U
– (Ministry of Health: 105, UoA: 52, DHBs:80
• Half (141 or 49%) of datasets are individually identifiable
via NHI or encrypted versions of it.
• A substantial amount of the custodian organisations have
also provided information regarding the data
volume, format, quality, governance and data access
protocols
• Responses were sufficient - Except for ADHB, CMDHB and
Southern DHB
• Caution!: numbers can be misleading! Not all datasets are
of same significance, numbers can be misleading
– E.g. National collections vs. small research cohorts

20. The Data
• Data quality very variable (!) but not too bad
• Historical data may not be validated or
conform to current data dictionaries but still
valuable (e.g. Longitudinal view)
• Volume of data is enormous
• Structuring is good 
• Mostly in relational databases
• Mostly updated frequently

21. Data Access
• Most custodian organisations require ethics
approval and data access processes
• Linkages can increase risk of potential
harm, hence a bit more tricky
• Many of these custodian organisations, mainly
the Ministry of Health holding the majority of
datasets, DHBs, and Universities, have well
established data access protocols and guidelines
• Data collected in certain research studies cannot
be made accessible outside the research team
without explicit patient consent

22. Full Report & Next Steps
Atalag K, Adler R, White C, Lovelock K, Gauld R, Gu Y, Pollock M. Value of New Zealand Health Data (VNZHD) Project Phase 1: Final
Report. Wellington: New Zealand Trade and Enterprise, 2013.
• Not as huge overseas interest as people think! Health
IT has picked up a lot in the US recently
• Still interest from Research/Epidemiology type
organisations
• 5 valuation methods identified
• A good first stab at barriers to commercialisation
• It’d be good to:
– Have second phase commissioned
– Make stocktake a ‘living’ project; e.g. On the
web, interactive and perhaps self submission/validation
– Take advantage of the ‘big data’ hype!

23. Bottom line
• There’s certainly value!
• Elicited more datasets than we hoped for 
• Except for a couple many participants shared our
enthusiasm and were positive about ‘value’ and
‘reuse’
• There’s much more out there!
• Barriers do exist and we need a participatory and
comprehensive framework
• For some datasets further reuse might not be
possible at all

24. Outlook & Opportunities
• Awareness around ‘getting information right’
• Responsibility for us as researchers to learn and
educate the Sector about importance of common
data definitions & processes
• Useful steps:
–
–
–
–
–
NHITB Interoperability Reference Architecture
Sector and Vendor Architects Forums
Emerging HISO (10041.x) ‘content’ standards
CDA test harness by Patients First
NIHI’s work on health information modelling (create
Archetypes for reuse)

26. Usage of the Content Model

27. Exploiting Content Model for Secondary Use
Single Content Model
Automated Transforms
PAYLOAD
CDA
System A
Map
To
Content
Model
FHIR
HL7 v2/3
EHR Extract
System B
Map
To
Content
Model
UML
XSD/XMI
PDF
Mindmap
Data Source A
Data Source B
No Mapping
Secondary Use
Native openEHR Repository

28. Questions?
k.atalag@auckland.ac.nz