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Gestational Diabetes Registry
Development
in CMDHB
Dr. Koray Atalag MD, PhD, FACHI (National Institute for Health Innovation)
Dr. Carl Eagleton MBChB, FRACP (Counties Manukau District Health Board)
Karen Pickering (Diabetes Projects Trust)
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
Gestational Diabetes Mellitus (GDM)
GDM is characterised by glucose intolerance with
onset or first recognition during pregnancy & is
identified by an oral glucose tolerance test (OGTT)
A repeat OGTT performed 6 weeks post-partum
checks for resolution
◦ If normal, an annual fasting glucose or glycosylated
haemoglobin (HbA1c) screening test is recommended for
T2DM, according to New Zealand (NZ) guidelines.
The number of deliveries at CMDHB for women with GDM almost
doubled in the last six years to 408 in 2011/12
Maori and Pacific women represent the largest ethnic cohort in
CMDHB service and Maori women have more high risk pregnancies
than non-Maori
Recent unpublished data from CMDHB show a 6% prevalence with
an 85% screening rate for GDM.
GDM has long-term, serious consequences. A study in NZ found
19% of 110 women with GDM developed T2DM after a mean follow
up of only 2.4 years. Women with a history of GDM have an
increased prevalence of CVD risk factors such as hypertension,
dyslipidaemia, and microalbuminuria, and of developing CVD.
Furthermore these women are highly likely to develop GDM in a
subsequent pregnancy.
Children born to women with GDM have been found to have
increased rates of obesity and hypertension as adolescents.
Children of women with unrecognised type 2 diabetes have
increased risk for foetal malformation.
Opportunities & Motivation for the 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.
Clinical Registries & Uses
 Register / Registry
 Clinical (+quality) / disease / patient registry etc.
Repository of individuals with a certain condition/characteristic
Ease of access to important info
Track outcomes & processes
Longitudinal history of correspondences & interventions
Prompt / feedback to participants and providers
Supporting clinical practice
◦ Screening, risk prediction, intervention/recall, safety monitoring
Clinical quality improvement
◦ Organisations, clinicians, policy makers
Research & education
Main Considerations
Privacy / Confidentiality
◦ Privacy Act 1993 and Health Information Privacy Code 1994 (“HIPC”)
◦ Recent changes to offshore hosting
◦ Connected Health secure network
Security / Recovery / Availability
◦ Univ. of Auckland’s secure IT infrastructure
IT standards & components
◦ W3C, Microsoft Net, SQL Server, Angular JS
◦ HISO Interoperability Reference Architecture
◦ openEHR
Existing systems
◦ CMDHB: Maternity CIS & others
◦ Regional/National: MoH datamart? VDR, PMS, Shared Care etc.
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
Golden principle: Minimal data entry, Maximal reuse!
Technical Development
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 generic clinical data management
framework
The Dataset
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using openEHR
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using openEHR
Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using openEHR
Conclusions
Currently in pilot deployment & evaluation phase
No need for Regional Ethics Approval if ‘part of clinical service’
◦ Still issues around data feeds (e.g. TestSafe data in DHB systems)
Model based Dataset development
◦ Very effective and easy to engage clinicians but require tooling and
editorial effort & skills (=cost)
Fully-fledged EHR underpinning Registry
◦ Standards based, scientific rigour in data representation but hard!
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 
Improved
Health
Outcomes
Education
Research
Reduce
Disparities
Collaboration
Koray Atalag
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

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Development of the Gestational Diabetes Registry at CMDHB (New Zealand) using openEHR

  • 1. Gestational Diabetes Registry Development in CMDHB Dr. Koray Atalag MD, PhD, FACHI (National Institute for Health Innovation) Dr. Carl Eagleton MBChB, FRACP (Counties Manukau District Health Board) Karen Pickering (Diabetes Projects Trust)
  • 2. 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
  • 3. Gestational Diabetes Mellitus (GDM) GDM is characterised by glucose intolerance with onset or first recognition during pregnancy & is identified by an oral glucose tolerance test (OGTT) A repeat OGTT performed 6 weeks post-partum checks for resolution ◦ If normal, an annual fasting glucose or glycosylated haemoglobin (HbA1c) screening test is recommended for T2DM, according to New Zealand (NZ) guidelines.
  • 4. The number of deliveries at CMDHB for women with GDM almost doubled in the last six years to 408 in 2011/12 Maori and Pacific women represent the largest ethnic cohort in CMDHB service and Maori women have more high risk pregnancies than non-Maori Recent unpublished data from CMDHB show a 6% prevalence with an 85% screening rate for GDM. GDM has long-term, serious consequences. A study in NZ found 19% of 110 women with GDM developed T2DM after a mean follow up of only 2.4 years. Women with a history of GDM have an increased prevalence of CVD risk factors such as hypertension, dyslipidaemia, and microalbuminuria, and of developing CVD. Furthermore these women are highly likely to develop GDM in a subsequent pregnancy. Children born to women with GDM have been found to have increased rates of obesity and hypertension as adolescents. Children of women with unrecognised type 2 diabetes have increased risk for foetal malformation.
  • 5. Opportunities & Motivation for the 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.
  • 6. Clinical Registries & Uses  Register / Registry  Clinical (+quality) / disease / patient registry etc. Repository of individuals with a certain condition/characteristic Ease of access to important info Track outcomes & processes Longitudinal history of correspondences & interventions Prompt / feedback to participants and providers Supporting clinical practice ◦ Screening, risk prediction, intervention/recall, safety monitoring Clinical quality improvement ◦ Organisations, clinicians, policy makers Research & education
  • 7. Main Considerations Privacy / Confidentiality ◦ Privacy Act 1993 and Health Information Privacy Code 1994 (“HIPC”) ◦ Recent changes to offshore hosting ◦ Connected Health secure network Security / Recovery / Availability ◦ Univ. of Auckland’s secure IT infrastructure IT standards & components ◦ W3C, Microsoft Net, SQL Server, Angular JS ◦ HISO Interoperability Reference Architecture ◦ openEHR Existing systems ◦ CMDHB: Maternity CIS & others ◦ Regional/National: MoH datamart? VDR, PMS, Shared Care etc.
  • 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! Technical Development 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 generic clinical data management framework
  • 14. Conclusions Currently in pilot deployment & evaluation phase No need for Regional Ethics Approval if ‘part of clinical service’ ◦ Still issues around data feeds (e.g. TestSafe data in DHB systems) Model based Dataset development ◦ Very effective and easy to engage clinicians but require tooling and editorial effort & skills (=cost) Fully-fledged EHR underpinning Registry ◦ Standards based, scientific rigour in data representation but hard! 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 
  • 15. Improved Health Outcomes Education Research Reduce Disparities Collaboration Koray Atalag 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