the aggregated health information project: building october 27, 2006

1. The Aggregated Health Information Project:Building   October 27, 2006

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3. 3 Vision A provincial health information management framework that Shifts to patient-centred analysis, sensitive to the user, task, responsibility and location Moves beyond information to knowledge management and development Proactively supports health system with new information resources and techniques

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7. 7 Health System Measurement and Management Information

8. 8 AHIP Key Objectives Enable a sector-wide view of the knowledge and information required to understand the whole context of health and related service delivery in BC Bring data, information and knowledge into this view on an incremental, priority driven basis and integrate all relevant data and information over time Provide tactical value along with strategic deliverables

9. 9 AHIP Key Objectives (cont�d) Concurrently and incrementally extend support for data, methodology, and tooling needs of information consumers Build in all necessary business controls and governance processes (e.g. data quality assurance; access control) Use metadata to drive and support the development and delivery processes

10. 10 Our Challenges: the Ongoing Use of the Data Information is typically produced in response to an issue rather as an ongoing resource for improving corporate knowledge Lack of a proactive, comprehensive means to undertake the surveillance of trends in health status, need for service, efficiency of the system and health outcomes

11. 11 Health System Knowledge Model Objective: To provide a formal, descriptive, and extensible outline of the uses of aggregate information in supporting the healthcare system, in order to � Contextualize specific business issues, concerns and questions Promote increasingly well-informed, effective and consistent analysis and reporting across the scope of information use in the system Support system users in identifying, describing, finding, retrieving and/or building packages of information appropriate to their needs.

12. 12 Uses of Aggregated Health Information

13. 13 Knowledge Model Principles Focus first on the business context and questions, then on furnishing information Business questions can be described in terms of categories of enquiry that share common approaches and methodologies, (e.g. drug utilization, lab utilization, physician specialist utilization �), Business contexts are highly interdependent Consistent methodology and a consistent (dimensional) approach to data structure can provide a great deal of leverage for consistent business understanding and analysis.

14. 14 Engaging with Ministry Information Users Lead with executive engagement Understanding strategic directions Communicate vision and potential Examine current practices Identify new requirements and opportunities Business wins and high value products Capacity to use information and tools Information delivery processes Note under resourcing of this area of the projectNote under resourcing of this area of the project

15. 15 MOH Program area Requirements

16. 16 Developing the Framework

17. 17 Developing the Framework

18. 18 Developing the Framework

19. 19 The Information Catalogue: Metadata

20. 20 The Information Catalogue: Metadata

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23. 23 New reporting capabilities with integrated data and dimensional model Layered groups of identifiers and classifiers E.g. � patient, provider, time periods, geography, service types, morbidity / health status, age bands Used for Validating fixed fields in data Consistently reporting and linking across sources Rolling up / drilling down results Filtering levels of detail for protection of privacy Providing a common frame of reference for defining complex measures, cohorts, etc. Adds analysis leverage to data and tools Changes the way the work is done!

24. 24 Reference Data Dimensional Clusters

25. 25 Support for Data Linkage Extend the base dimension � e.g. with patient or provider demographics, ICD code description Co-ordinate views of aggregate info for same reference data set � e.g. costs by service type for same set of patients Co-relate event data / reporting across sources (e.g. DAD-MSP) Build analytic reference data sets by applying consistent criteria across multiple data sources (e.g. cohorts, episodes, �burden of care� indicators), for use in broader analysis and aggregation.

26. 26 Health Information & GIS The traditional two broad types of GIS applications are: health outcomes and epidemiology applications, and healthcare delivery applications. Most pertinent for us - the interface (overlap) between epidemiological and healthcare delivery applications For the MOHS, geographic information analysis, and the evolution of the data and methodological insight required for this analysis to be effective, are integral parts of our information delivery strategies and plans.

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28. 28 AHIP GIS Strategy Apply geographic co-ordinates for residence, point of service, etc. to all relevant event data, based on geo-location of addresses. Establish interoperability with the BC Government�s Land Resource Data Warehouse (LRDW) for physical geography, land use and infrastructure data. Pursue interoperability between dimensional and spatial analysis tools and views of data. Provide simplified GIS analysis for specific purposes such as disease surveillance and facility planning. Ensure robust controls on data access for protection of privacy with respect to exact locations.

29. 29 Access to Data Security of data Access Authorization & Control Role based access Audit logs Privacy Protection: Identifiable data on a need to know basis only Data quality assurance and matching Medical intervention Audit

30. 30 Strategies for managing data linkage for analysis (near term) Separate views and responsibilities for MATCHING from views and responsibilities for ANALYSIS. Keep audit logs of all access to personally identifiable data by individual users. Use metadata, dimensional roll-ups, and data base views to manage data access and extract, balancing need to know birth dates, addresses etc., with identification risk (some manual work initially). Don�t use ID encryption if you can easily use a non-reproducible surrogate ID. Ensure the underlying system security infrastructure is robust, in both technical and human terms. Define and apply formal criteria for masking small result set data.

31. 31 Personal information types Explicit Business Identifiers (e.g. PHN) Common use identifiers (names) Contact information (address, telephone) �Tombstone� demographic characteristics (birth date, gender) Service / assessment information

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33. 33 Strategies for managing data linkage for analysis (longer term) Develop flexible utilities and capabilities for delivering fast, iterative, aggregate views of data (including statistical and geographic aggregates) accurately on demand from detailed data, without revealing the detailed data to the analyst. In extracting detailed, de-identified data for external analysis, encrypt surrogate identifiers differently for each authorized purpose. Establish and apply a code of ethics for data access request reviews; automate to the extent possible.

34. 34 Data linkage across organizations (primarily government and healthcare) Shared Client Access to health services and health outcomes are highly influenced by other government services - community, social, educational, and economic factors and� These services in turn are impacted by the health status of their clients and workers Data Privacy, Security issues: personal information highly sensitive mechanisms, policies and legislation in development

35. 35 How can broader linkage work? Assumptions Each Ministry (or other organization) maintains responsibility for their own data, in terms of collection, stewardship and governance In sharing among / linking across organizations, ensure the same minimum set of info privacy controls and policies exist for analytic access to linked and unlinked data, in all participating organizations. The Trust Box A secured auditable environment to allow the linkage of anonymized aggregated data for decision support. Provides enhanced functions for segments of the data reference / integration layer that are common across multiple organizations, and highly sensitive This would require data from multiple sources to be linkable based on common reference data and common data models. Technically feasible Requires legislative and policy enablement Enables a virtual warehouse of warehouses

36. 36 AHIP principles underlying the data access approach Explicit, effective linkage with explicit, effective governance will support better analysis and more complete and auditable privacy than are possible with restrictive, ad hoc linkage. Data linkage, data quality, and the practical basis for dimensional analysis are all closely related, and should be managed together. Metadata makes this entirely practical.

37. 37 Over-All Strategy Summary Consistent management and technical framework Apply to all Ministry data analysis and information delivery Open to integration and analysis across organizational boundaries Grow the scope incrementally, based on a strategic view of data requirements Focus first on patient- and provider-based information Initially with smaller data sources (e.g. DAD) Make key cross-data source analysis a priority Import useful derived information (e.g. Chronic Disease cohorts) even if intending to generate them in the longer term Focus on strategic data analysis and new business value, supporting operational reporting / data analysis in the same framework as much as is practical

38. 38 Emerging opportunities Enhanced ad hoc analysis of detailed data using new tools such as Oracle Discoverer Packaging and rapid delivery of aggregate measures (similar to PURRFECT, web-based) Support for more complex analysis using anonymized linked data, cohort formation, episode analysis, geographic data integration and mapping, �

39. 39 Thank you Questions or comments are welcome or forward to the KID/AHIP team:terry.tuk@gov.bc.caian.caesar@gov.bc.ca kelly.barnard@gov.bc.cacharles.douglas@gov.bc.ca