A new opening for transparency and transformation - the benefits of the community dataset
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The document discusses the development of the Community Information Dataset (CIDS) which aims to provide accurate monitoring of community health services spending and outcomes. It highlights benefits like improved service planning and demonstrates reporting capabilities through example data. Full implementation will require approval and adoption by organizations. The dataset design and initial usage examples are presented.
A new opening for transparency and transformation - the benefits of the community dataset
- 1. A New Opening for Transparency and Transformation – The Benefits Of The Community Dataset
- 2. Today’s Presentation To update you on progress with the development of the Community Information Data Set (CIDS) To help you focus on your plans for the national implementation of the CIDS To highlight the benefits that CIDS will bring To give some examples of reporting capability of CIDS To give you the opportunity to ask questions
- 3. Community Information Data Set Key Driver for Development / Business Requirement NHS currently spends more than £10bn every year on community services BUT Currently no way of accurately monitoring the quantity, the cost or the quality of these services; no national definitions or processes exist to collect and report common activity/ outcome data for local and/or national comparison HOWEVER Community services are strategically vital in enabling the shift of care from acute settings to deliver the government’s vision for world-class health outcomes and quality health services
- 4. Timeline for Implementation Phase 1 from April 2011 Local collection and use to meet requirement to report AHP RTT and support transformation of local services Phase 2 from 7 months following approval of funding Commence implementation of national collection and flow via Secondary User Services Outstanding Issues to be Addressed for Phase 2 ISB Approval – ROCR / NIGB approval being sought Decision on affordability
- 5. Even Without A Funding Decision Planning to publish Community Dataset for April 2011 prior to ISN Resource pack will be available on Information Centre Website to support implementation Implementation Plan Template State of Readiness Assessment Tool FAQ’s Product Sheet Demand and market from new community service providers will increase as community services are opened up with choice and competition Early Demonstration sites (First of Type)
- 6. Even Without A Funding Decision Implications For Community Service Organisations Part of Monitor Compliance Framework Q3 2011/12 QIPP – more of the same is not an option NHS contract monitoring Meeting of technical requirements of Operating Framework 2011/12 NHS Outcomes Framework Improved Commissioning Part of the Guidance Framework for Any Willing Provider Strategic Priority for Official Statistics on Health and Social Care
- 8. Patient level secondary uses dataset Includes patients in contact with Community Services (NHS Standard Community Contract) Covers entire community patient pathway i.e. referral to discharge Developed in conjunction with Expert Reference Group Aligns to existing NHS Data Dictionary and other national standards wherever possible Based on information routinely captured for primary use or local administration purposes Principles
- 9. CIDS dataset Output Dataset Mandatory/Required/Optional Unique Identifiers e.g. Service Referral/Care Contact etc Pilot Items It’s the start and not the finish of what needs to be collected, understood and acted upon
- 10. Dataset Content Person details/demographics Service Referral Referral to Treatment Care Contact Activities Assessments Activities Outcomes Group Sessions
- 11. Reporting Potential Systems are key – ease of input and extract Consistency – do once and share! Identify relevant data – organisation and content Value mapping – do not need to collect only national values Need to correctly associate data items in system and extract
- 12. Numbers of Activity by Duration and Staff Group Data Source: Community Dataset Proof of Concept data, 2010 Extreme caution should be taken when interpreting the data. This is proof of concept data and is not representative of all community services and providers. This data should not be used for management and performance purposes.
- 13. Number of Activities by Duration and Activity Group Data Source: Community Dataset Proof of Concept data, 2010 Extreme caution should be taken when interpreting the data. This is proof of concept data and is not representative of all community services and providers. This data should not be used for management and performance purposes.
- 14. Number of Activities by the Age of Patient for Selected Staff Groups Data Source: Community Dataset Proof of Concept data, 2010 Extreme caution should be taken when interpreting the data. This is proof of concept data and is not representative of all community services and providers. This data should not be used for management and performance purposes.
- 15. Number of Initial Activities by Duration Recorded for District Nurses on Two Sites Data Source: Community Dataset Proof of Concept data, 2010 Extreme caution should be taken when interpreting the data. This is proof of concept data and is not representative of all community services and providers. This data should not be used for management and performance purposes.
- 16. Number of Follow up Visits by Activity Duration Recorded for District Nurses for 2 Sites Data Source: Community Dataset Proof of Concept data, 2010 Extreme caution should be taken when interpreting the data. This is proof of concept data and is not representative of all community services and providers. This data should not be used for management and performance purposes.
- 17. Number of Referrals by Primary Reason where Source of Referral is a Hospital Data Source: Community Dataset Proof of Concept data, 2010 Extreme caution should be taken when interpreting the data. This is proof of concept data and is not representative of all community services and providers. This data should not be used for management and performance purposes.
- 18. Number of Cancelled Activities by Activity Group and Cancellation Reason Data Source: Community Dataset Proof of Concept data, 2010 Extreme caution should be taken when interpreting the data. This is proof of concept data and is not representative of all community services and providers. This data should not be used for management and performance purposes.
- 19. Referral to Treatment Times Driving Service Improvement Bathing Assessment Team Northumberland Care Trust reduced RTT from 26 weeks to 1 week (96% improvement) Children’s Occupational Therapy Coventry Community Health Services reduced waits from a average of 9-17 months depending on clinical priority to 2 weeks for all referrals (94% - 96% improvement) Adolescent Chronic Fatigue Service, University College Hospital reduced waits from14 weeks to 4 weeks (71% improvement)
- 20. Conclusion There is potential to obtain and report accurate data which provides information that will drive, support and demonstrate the potential and actual quality and efficiency gains in community services. It will not be easy to undertake this development but it is essential to the future of these services Time and effort must be made to support clinicians to confidently and competently utilise clinical applications and deploy mobile solutions to enable data collection and completeness
Editor's Notes
- #9: Information Requirements Expert Reference Group Data Discovery Proof of Concept Formal Approvals Aligns to existing NHS Data Dictionary wherever possible Mostly based on information routinely captured for primary use or local administration purposes
- #10: Output Dataset (not an exhaustive list of what needs to be collected) Derived data items Mapping value lists Data Item status Mandatory Required Optional (not for suppliers though!) Contains Repeating Groups Unique Identifiers e.g. Service Referral/Care Contact etc Includes Pilot Items (not approved) Onward Referrals Indirect Patient Activities
- #12: System suppliers should ensure that electronic systems are easy to use, and the locations of various data collection items are clear and easy to find, as part of routine information recording pathway. System suppliers should ensure that their customers can get access to and can extract all data (where appropriate) for loading into their data warehouses for local and national reporting, using robust scheduled processes to provide the extracts they require. System suppliers should support sites by giving a facility to extract CIDS related content where the content has been identified; this should be shared with other sites that use the same application. Local System configuration should have options in order to prevent barriers to collection, ensuring that data can be captured as part of the routine work flow e.g. by switching off certain unwanted local capture, which the workflow mandates be captured before the required local entry. Where ‘Data Sharing’ is used in systems between organisations, extraction and reporting should be able to filter this content out where applicable. Local System configuration should allow some control of mandatory and optional items in terms of capture to better support local processes. Sufficient training should be given to all staff using electronic systems to record data. The data set specification and Guidance documentation should be available for all those who are entering data to ensure accurate understanding of all data items. System suppliers need to ensure that data is clearly mapped to national collection “behind the scenes” where this is more appropriate. Although many clinicians prefer to have free text entry in systems, this entry needs to have a simply fixed category (drop down list) to give context as to what the free text statement relates to. Reports (patient lists) could be developed using these categories to show the benefits of recording data this way in supporting clinicians delivering patient care. Some local configuration would be needed to support this. Suppliers should consider using Global IDs in their systems, to correctly associate data items