This is the prezo I presented at HINZ 2014 conference. Gestational diabetes has implications for both mother and child with risk of complications during pregnancy, and type 2 diabetes later in life. This paper presents the initial lessons learned from the development of a clinical registry. The aims of the Registry are: 1) 100% successful diabetes screening within 3 months of delivery; 2) Annual type 2 diabetes screening; 3) Early warning in subsequent pregnancies. We have employed the openEHR standard which underpins our national interoperability reference architecture to represent the dataset and also to build the web-based registry system. Use of this rigorous methodology to tackle health information is expected to ensure semantic consistency of Registry data and maximise interoperability with other Sector projects. The development work has been facilitated by the ability to transform the dataset automatically into software code – ensuring clinical requirements accurately translated into technical terms. Dataset has been finalised, registry system has been developed and deployed for pilot implementation. Data entry is underway for participants after consenting. This registry is expected to increase the screening of women leading to earlier detection of diabetes. It should provide a valuable picture of the condition and is intended for extension and wider roll-out after evaluation.

1. A Standards-based Approach to Development
of Clinical Registries -
Initial Lessons Learnt from the Gestational Diabetes Registry
Dr. Koray Atalag MD, PhD, FACHI (National Institute for Health Innovation)
Aleksandar Zivaljevic, PhD candidate (Univ. Of Auckland)
Dr. Carl Eagleton MBChB, FRACP (Counties Manukau District Health Board)
Karen Pickering (Diabetes Projects Trust)

2. Registry defined
An organised system that
uses observational study methods
to collect uniform data(clinical and other)
to evaluate specified outcomes for a population
defined by a particular disease, condition, or exposure,
and that serves a predetermined scientific, clinical or
policy purpose(s).
GliklichR, Dreyer Ne. Registries for Evaluating Patient Outcomes: A User's Guide Prepared by Outcome DEcIDECenter[Outcome Science, Inc.
dbaOutcome] under Contract No. HHSA290200500351TO1). Rockville, MD: Agency for Healthcare Research and Quality, 2007; Publication No.
07-EHC001-

3. Clinical Registries
 Register / Registry
 Clinical (+quality) / disease / patient / incidence / screening etc.
Repository of individuals with certain conditions/characteristics
Ease of access to important info
Track clinical processes & (risk adjusted) outcomes
Longitudinal history of correspondences & interventions
Prompt / feedback to participants and providers
Data linkages & advanced analytics & reporting
 Supporting clinical practice
◦ Screening, risk prediction, intervention/recall, safety monitoring
 Clinical quality improvement
◦ Organisations, clinicians, policy makers
 Research & education

4. Why do we need them?
 Because we don’t have the mighty EHR!
 Registries are a ‘quick fix’ to some ‘can’t wait’ type
problems / for ‘quick wins’; capturing
◦ observations, diagnoses, procedures, clinical processes and
most importantly outcomes
 Provide an infrastructure on which intervention studies
can be established with relative ease.
 Who get’s a registry?
◦ Those with funding of course!
 Clinical significance / popularity (eg. CVD, diabetes)
 Well established network/specialised (e.g. Spina Bifida)
 national/intl policies (MoH / WHO – cancer etc.)
 leadership / persistence / charisma / luck (GDM?)

5. Around the world & NZ
 A lot of them!
 Overarching principles / regulations /
minimal standards
 Shared resources (hosted by dedicated
organisations / infrastructure)
 A growing number of them
 All go own ways – (under privacy rules)
 Hosted/curated by source groups with limited
technical/data management resources
 Some hosted offshore (e.g. Oz)

6. GDM Registry
* A recently deployed pilot project to test the
feasibility of a registry to support targeted
interventions. Led and supported by CMH & DPT.
NIHI has undertaken health informatics research
and the technical development.
AIMS:
 100% successful screening of women for type 2 diabetes
(T2DM) within 3 months after a pregnancy with GDM
 Annual screening of all women for new onset T2DM
 Early warning to healthcare providers (GPs, Maori/Pacific
Health, others) about GDM history in subsequent
pregnancies

7. Motivation for the GDM Registry
 Long term consequences can be prevented by regular
screening for early detection of T2DM or high CVD risk
◦ CMDHB found 20% of women with a history of GDM were not
follow-up tested in a 4 year period; (37% for 2 year period)
◦ Sending out reminders improve adherence / better compliance
with screening recommendations
 Risk of developing T2DM can be substantially reduced
by early identification of women at high risk + targeted
lifestyle & pharmacological interventions
 Registry can also be used to drive clinical quality
improvement and enhance patient safety
◦ by identifying variations in processes and clinical outcomes.

8. GDM Registry Pathway
Entry
• Referral from primary care with a diagnosis of GDM
Education
• Attendance at Group Session
• Registry information supplied
Consent
• Attendance at DiP Clinic
• Consent obtained and entry into the registry
Postpartum
• 6 week OGTT request or 3 month HbA1c
• GP & Patient advised of results
Annual
• Annual HbA1c with copy to primary care
• GP & Patient advised of results
Next time
• Positive pregnancy test detected in Testsafe
• Requesting healthcare provider advised of Diabetes history by the Registry
RegistryDirected

9. Golden principle: Minimal data entry, Maximal reuse!
Health Informatics @ Work
 Used an international (and HISO) standard:
◦ Consistent dataset
◦ Interoperability / integration
◦ Manage change over time
 Used a Web-based data set development tool to
review & finalise
 Automatically converted dataset into “software
code” [domain objects]
 Built on NIHI’s data management framework

10. If the Banks Can Do It,
Why Can’t Health?
Clinical data is wicked:
◦ Size (breadth, depth) and complexity
◦ >300,000 concepts, 1.4m relationships in SNOMED
◦ Variability of practice
◦ Diversity in concepts and language
◦ Conflicting evidence
◦ Longevity
◦ Links to others (e.g. family)
◦ Peculiarities in privacy and security
◦ Medico-legal issues
It IS critical…

11.  Open source specs & software for representing
health information and person-centric records
◦ Based on 18+ years of international implementation experience
including Good European Health Record Project
◦ Superset of ISO/CEN 13606 EHR standard
◦ Underpins HISO Interop Reference Architecture standard (NZ)
 Not-for-profit organisation - established in 2001
www.openEHR.org
 Extensively used in research
 Separation of clinical
and technical worlds
 Big international community

12. The Dataset

13. Online dataset development - CKM

16. Formal
Domain
Model

17. Automatic technical conversion – C# Class

19. EHR Providing a Canonical Representation
so we know what kind of info goes into which bucket!
Demographics
ClinicalEncounter
VitalSigns
Medications
Diagnoses
DiagnosticTests
Interventions
FamilyHistory
PastHistory
PhysicalExam
Genetics
LifeStyle
etc.etc.etc.
Subject A
Subject B
Person-Centric Record Organisation
NZ Address
Ethicity1,2.
Whanau
USAddress
State
Next of kin
GP visit
Flu-like
PHO enrolm.
Hospital adm.
Diabetes
Priv insurance
BP 130/90
HR 90
T: 38.5 C
BP 120/70
(24 hour avg)
HR 70
T: 37 C
Rx A
Dispense
Administer
Rx B
Dispense
Administer
Dx 1
Dx 2
etc.
Diabetes Dx
-Type
-Severity
-Course etc.
Routine Blood
Urine
X-Ray
Specific blood test
Urine culture
Genomic assay
Retinography
Rx
Fluid Tx
Insuline inj
Infection Tx
Psychologic
N/A
Pedigree
N/A
Chronic
Routine
Detailed
Foot and
eyes
N/A N/A
DNA
Seq.
Assays
Low
sugar
Exercise
Shared Archetypes
Each finding usually depends on other – clinical context matters!

20. Benefits of Approach Taken
We may not have EHR now....but
by using openEHR to represent our clinical information
we are leveraging some of the benefits of EHR today,
including
◦ Expressivity, clinical context, meta-data support
◦ Interoperability
◦ Semantic querying (easy + fast)
◦ Tooling support and international content
◦ Standards compliance
and future-proofing registry data!
Atalag K, Yang HY, Tempero E, Warren JR. Evaluation of software maintainability with
openEHR – a comparison of architectures. International Journal of Medical Informatics.
2014 Nov;83(11):849–59.

21. Conclusions
 No need for Regional Ethics Approval if ‘part of
clinical service’
 Model based Dataset development
◦ Very effective and easy to engage clinicians but require
tooling and editorial effort & skills
 Fully-fledged EHR underpinning Registry
◦ Standards based, scientific rigour in data representation
 Getting ‘information right’ is crucial!
◦ Invest in defining dataset properly, change is costly
◦ Alignment is hard and there’s no formal guidance  There
is no single organisation or mechanism to ensure the
Sector’s datasets are to be aligned 

22. What’s Next
 Obtain funding for next stage
 Further Enhancements
 Prepare for scaling up & further testing of the Software
 New data points & features (e.g. Smart phone App for women for bi-
directional support)
 Integration with key systems (e.g. PAS, Maternity System)
 Deployment in CMH catchment area
◦ Attain enough numbers to for meaningful formal evaluation
 Seek wider Sector support & funding
◦ National Diabetes Registry?
 NIHI has implemented other Registries (NZ Cardiac registry)
and providing stewardship to research databases (Growing
Up in NZ, SPARX + 100s of own trials). Current
infrastructure and expertise will be leveraged.

23. Improved
Health
Outcomes
Education
Research
Reduce
Disparities
Collaboration
Koray Atalag MD, PhD, FACHI
k.atalag@auckland.ac.nz
Vice Chair HL7 New Zealand
openEHR Localisation Program Leader
Health Information Standards Organisation (HISO) Committee Member
NHITB Sector Architects Group Member