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A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
A Stocktake of New Zealand’s Healthcare Datasets
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A Stocktake of New Zealand’s Healthcare Datasets

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Presented by Koray Atalag …

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

Published in: Health & Medicine, Business
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  • 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
  • 13. 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
  • 14. 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
  • 15. 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%
  • 16. Graphical Look By category By scope
  • 17. 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
  • 18. 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
  • 19. 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
  • 20. 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
  • 21. 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!
  • 22. 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
  • 23. 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)
  • 24. Usage of the Content Model
  • 25. 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
  • 26. Questions? k.atalag@auckland.ac.nz

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