Establishing a Data and Information Governance Program at the Cancer Institute NSW. Data Governance 2012 Narelle Grayson

Transcription

1 Establishing a Data and Information Governance Program at the Cancer Institute NSW Data Governance 2012 Narelle Grayson

2 Contents 1. Key drivers 2. Developing the Program Case study NSW Cancer Registries (NSWCRs) System

3 Key drivers 2011 NSW Cancer Plan CINSW Cancer Information Strategy Two major divisions merged Cancer Information and Registries Cancer Services and Education CIO appointed Major information system development: BreastScreen Information System Amalgamation of NSW Central Cancer Registry and Clinical Cancer Registry NSW Ministry of Health

4 Developing a Data and Information Governance Program Strategy Structure Executive support Data governance audit - maturity Polices, procedures and standards Amalgamation of CCR and ClinCR

5 Strategy - CINSW Data holdings survey CCR AMR HCR ClinCR

6 Strategic alignment

7 Strategic Alignment Cancer Information Strategy Maximise efficiency and currency of cancer data collections Maximise the quality and completeness of cancer data collections Maximise opportunities for linkage to cancer data to identify trends and address key cancer control questions Maximise timely access to relevant and accurate cancer data for quality improvement and research Optimise use of available resources for cancer data and information activities Ensure currency in approaches to cancer data and information activities

8 Data Quality To ensure the quality of data held by CINSW, ensuring consistent quality assurance and control processes are applied across the data holdings Data and Information Standards To improve the comparability, consistency, reusability, accessibility, and quality of data across the CINSW Services to support the Program IT Vision Clinical and other Consultation Data and Information Governance To ensure a co-ordinated, collaborative and strategic approach to data and information governance Information Management To ensure standardised and robust information management policies and practices for CINSW data holdings and information systems Compliance Data Access To ensure CINSW s data holdings are accessible and available to stakeholders to inform improvements in cancer services and outcomes for cancer patients Analysis, Reporting and Publications To ensure consistent, high quality analysis, reporting and publication of CINSW data that meets future needs Business Unit Scope of this program All data holdings and information systems managed by CINSW

9 Draft structure Data and Information Governance Committee Registries and Data Collection Advisory Committee Registries and Data Collection Operations Committee Data and Information Governance Office Reporting Operations Committee Quality Working Group Standards Working Group Information Management Working Group Analysis, Reporting & Publications Working Group Data Access Working Group Quality Indicators/ audits Quality Control/ assurance Quality statements/ caveats Policies Data Standards Process Indicator process Review, prioritise and approve projects Build capacity Promote use of standards/indicators Data Security and access policy Change management Review proposals for new systems Retention and disposal policy Compliance with policy Analysis, reporting and publication strategy Standard analysis methods Reporting templates Capacity building Compliance with legislation and policies Common approach Data access and pricing policy Supporting documentation Review Requests

10 Executive support Sign-off from the Leadership team

11 Data Governance Maturity Model: Levels of maturity (IBM Data Governance Council, 2007) Level Name Practice Quality and results predictability 1 Initial The organization lacks the necessary processes for sustaining data management practices. Data management is characterized as ad hoc or chaotic. 2 Repeatable The organization might know where data management expertise exists internally and has some ability to duplicate good practices and successes. 3 Defined The organization uses a set of defined processes, which are published for recommended use. 4 Managed The organization statistically forecasts and directs data management, based on defined processes, selected cost, schedule, and customer satisfaction levels. The use of defined data management processes within the organization is required and monitored. 5 Optimizing The organization analyses existing data management processes to determine whether they can be improved, makes changes in a controlled fashion, and reduces operating costs by improving current process performance or by introducing innovative services to maintain their competitive edge. The organization depends on entirely on individuals, with little or no corporate visibility into cost or performance, or even awareness of data management practices. There is variable quality, low results predictability, and little to no repeatability. The organization exhibits variable quality with some predictability. The best individuals are assigned to critical projects to reduce risk and improve results. Good quality results within expected tolerances most of the time. The poorest individual performers improve toward the best performers, and the best performers achieve more leverage. Reliability and predictability of results, such as the ability to determine progress or six sigma versus three sigma measurability, is significantly improved. The organization achieves high levels of results certainty.

12 Case study: NSW Cancer Registries

13 Scope and coverage Proposed: NSW Cancer Registries system Patients diagnosed or treated with cancer in NSW and Residents of NSW diagnosed or treated with cancer in other States and Territories Public Sector Private Sector Existing: ClinCR Patients diagnosed or treated in Public facilities in 13 of 17 LHDs Public Sector only (limited to facilities in 13 LHDs) Existing: CCR (Public Health Act) Residents of NSW diagnosed or treated in NSW and in other States and Territories Public Sector Private Sector Identifiers/ demographics Facilities Clinicians Diagnosis Death Stage Treatment - Surgery Treatment - Radiotherapy Treatment Chemotherapy Quality of care/access Dataset Extensions

14 CCR

15 ClinCR

16 NSWCRs

17 Data Governance issues Compliance with legislation Different legislation ensure public health information can be separately identified Privacy Impact Assessment Ethical approval Consent Policies Ownership Data custodianship Authority for disclosure Legal advice

18 Data Standards To improve the comparability, consistency, reusability, accessibility, and quality of data across the CINSW Working group Common data items CCR & ClinCR Expansion strategy

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20 Common data items defined differently - CCR & ClinCR Examples include: Laterality Most valid basis of diagnosis Date of diagnosis Extent of disease

21 Expansion strategy Nine tumour-specific DSEs were developed in NSW in Four in ClinCR: NSWOG Breast Cancer Specific Data Items for Clinical Cancer Registration NSWOG Colorectal Minimum Data Set Extension v1.0 and Guidelines Gynaecological Oncology Extension Dataset FIGO Extension Dataset Collection of Breast and Colorectal by two ClinCRs Differences

22 Expansion Strategy At least 3 other national Breast data sets Breast Cancer Specific Data Items for Clinical Cancer Registration (2009) Breast cancer (Cancer registries) Data Set Specification (DSS) National Breast Cancer Audit (RACS MDS) Collected by one ClinCR

23 Expansion Strategy Guiding principle Use national standards where possible Analysis of items to: Identify which items are useful for population-level (state-wide) reporting Identify which items should go in the MDS Identify a set of items to implement in a Breast DSE that would be feasible to collect statewide Develop a change management plan for implementation of these items Identify which items are desirable but require further work and development NSWCRs User Defined Fields Pilot

24 Information management To ensure standardised and robust information management policies and practices for CINSW data holdings and information systems Compliance with ITD standards, including: Security and access Retention and disposal Disaster recovery Enterprise software Microsoft application Business objects - reporting TRIM image repository Record linkage Business Intelligence strategy Processes Change management

25 Data Access To ensure CINSW data holdings are accessible and available to stakeholders to inform improvements in cancer services and outcomes for cancer patients Inconsistent data access policies and procedures leads to: Confusion for stakeholders Lack of transparency Inconsistent decision-making Inconsistent documentation Poor customer service Increased costs/resources

26 Data Access Streamlined process Single point of contact Documented process Roles and responsibilities Online data request forms Data Access System (KPIs, reporting) Metadata repository Started with CCR and expanding to whole of organisation Data Access Working Group Policies and procedures Decisions log

27 Conclusion Success factors (Janaka Dissanayake) Started small expanding to other Divisions Executive support early Commitment from staff Expert advice Challenges Business resources Volume of work initially Timeframe

28 Acknowledgements Sanchia Aranda Sarajane Hansen Jane Walker Elizabeth Tracey