a standards-based approach to development of clinical registries - initial lessons learnt from the...
DESCRIPTION
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.TRANSCRIPT
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)
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-
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
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?)
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)
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
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.
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
Reg
istr
y D
irec
ted
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
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…
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
The Dataset
Online dataset development - CKM
Formal Domain Model
Automatic technical conversion – C# Class
EHR Providing a Canonical Representationso we know what kind of info goes into which bucket!
De
mo
grap
hic
s
Clin
ical
En
cou
nte
r
Vit
al S
ign
s
Me
dic
atio
ns
Dia
gno
ses
Dia
gno
stic
Te
sts
Inte
rve
nti
on
s
Fam
ily H
isto
ry
Pas
t H
isto
ry
Ph
ysic
al E
xam
Ge
net
ics
Life
Sty
le
etc.
etc
. etc
.
Subject A
Subject B
Person-Centric Record Organisation
NZ AddressEthicity1,2.Whanau
USAddressStateNext of kin
GP visitFlu-likePHO enrolm.
Hospital adm.DiabetesPriv insurance
BP 130/90HR 90T: 38.5 C
BP 120/70 (24 hour avg)HR 70T: 37 C
Rx ADispenseAdminister
Rx BDispenseAdminister
Dx 1Dx 2etc.
Diabetes Dx-Type-Severity-Course etc.
Routine BloodUrineX-Ray
Specific blood testUrine cultureGenomic assayRetinography
Rx
Fluid TxInsuline injInfection TxPsychologic
N/A
Pedigree
N/A
Chronic
Routine
DetailedFoot and eyes
N/A N/A
DNA Seq.Assays
Low sugarExercise
Shared Archetypes
Each finding usually depends on other – clinical context matters!
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.
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
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.
Improved Health
Outcomes
Education
ResearchReduce
Disparities
Collaboration
Koray Atalag MD, PhD, FACHI
Vice Chair HL7 New ZealandopenEHR Localisation Program Leader
Health Information Standards Organisation (HISO) Committee MemberNHITB Sector Architects Group Member