1. Data Quality; Data Standards & Information Context – Nick Armitage, NHS Information Centre

2. Introduction: Data Quality –Background –ESR and Data Quality –Data Quality and iView Data Standards –The National Workforce Dataset (NWD) version 2.3 –The NHS Occupation Code Manual version 9 –The Healthcare Scientists Workforce Information Pilot

3. “We can only be sure to improve what we can actually measure” Lord Darzi, High Quality Care for All, June 2008 “...our greatest mistake would be to forget that data is used for serious decisions in the very real world, and bad information causes suffering and death.” – pg 46 Bad Science, Ben Goldacre “Collect once, use many times”

4. Data Quality Overview Background - What is Data Quality? - How to Measure Data Quality - How to Improve Data Quality ESR and Data Quality - Using ESR Data and DQ Consequences Data Quality and iView - Highlighting DQ Issues - Benchmarking Conclusion

5. What is data quality? May mean different things to different people Relevance, timeliness, completeness, validity, accuracy and comparability Complex and developing area – many different people are looking at this as a matter of priority, including the NHS IC Data Quality Programme and the ONS Quality Centre Importance of knowing your data and understanding your requirements Improving data quality tends to rely upon collaborative working – you cannot achieve it alone! Data must be available – the most validated and accurate data is not quality data if it is not made available to those that need it – regular publications; iView!

6. Measuring improvements in data quality Against what do you measure improvements in data quality? Previous standards of data quality? Some idealised ‘Gold Standard’? Between different data suppliers? Utilise available resources – ESR DQ reports Consult with the users and providers – develop your own means of assessing quality Different aspects of data quality need to be measured in different ways – validity versus accuracy Data quality issues easier to measure than good quality data? A nice idea, but…..?

7. Improving data quality Clear Purpose Classification / Standardisation Education Validation Feedback Visible and useful results Involve the service – ownership of the data & standards Work with systems suppliers, common data standards & greater validation at source Positive and negative feedback on data quality Data available and useful to frontline NHS staff (iView!)

8. The ESR and Data Quality There are many ways in which issues with NHS data and the information derived from it can have a significant impact, for example: patient care and safety depend upon good quality data poor quality data can damage the reputations of organisations and individuals poor quality data can lead to flawed clinical, administrative and planning decisions Our experience in England is that there is a great deal of variation in data quality within the Electronic Staff Record In general data relating to getting staff paid tends to be of much higher quality than what might be considered ‘ancillary’ information such as occupation coding, details of sickness absence, equality monitoring etc.

9. Ulitising ESR Data This information is put to far more uses than may be realised at it’s source Examples of how ESR data is being used in England: Premium setting by NHS Litigation Authority Reporting to NHS Management Board (QIPP) Monitoring targets (e.g. Health Visitors) Equality and Diversity Reporting to NHS Staff Council Provisional monthly workforce publication Automated 2010 census – and beyond Are there similar uses in Wales? With the drive to reduce the burden of direct information collections the list (and potential impacts) is likely to increase

10. Data Quality in ESR – Important Messages: Ensure Occupation Code is correct Correctly identify Locum Doctors Equality monitoring and other details Area of Work and Job Role? If you host staff, ensure you record them correctly within ESR –Position Workplace Org –Assignment Hosted Use the data quality tools in ESR – NHS IC ‘WOVEN’ ( Workforce Verification Engine )

11. ESR Data Quality/Validation 3 levels Operational ESR –Trust driven – to take responsibility for their own data quality –Run at trust discretion ESR Data Warehouse –Dashboard reports (not validation) –Covering key areas –For interest at Trust level and DW users –Run at trust or DW User discretion IC –To feed official publications and management information –Demonstrate increased confidence in ESR data –Provide evidence of increasing quality over time –Run monthly DQ reports for each Trust All 3 developed in partnership (ESR, IC, NHS)

12. ESR data – the IC data quality cycle Outline of the situation in England – parallels in Wales? A summary and detailed monthly report for each Trust Provide a useful assessment of data quality for key annual NHS Workforce Census fields Identify good performers – reduce their burden to complete the annual census return Identify poor performers – target help Identify systemic issues that may be addressed by changes in ESR iView – additional DQ measure (numbers versus quality) Objective measure of when official statistics can be published direct from ESR

13. ESR data and PQs/AMQs Parliamentary or Assembly Questions can highlight the ‘margin of error’ between ESR data and local knowledge NHS Workforce Census bulletin caveat “The HCHS non-medical workforce census is a large statistical exercise collecting over one million records from over 400 organisations. It is not, and is not intended to be, carried out to exact accounting standards.“ Example: –PQ asked for number of school nurses in a PCT. –2008 Census return stated 1, the PCT said 20. –PCT indicated to DH it had made some coding errors and wanted to correct its Census figures. –IC policy is that unless the impact is significant at national level figures are not changed, post publication. Poor quality data can damage the reputations of organisations and individuals

14. ESR data, impact on Finance/Planning Examples from England – the same or similar in Wales? NHS Litigation Authority premiums –2007 Census fed the 09/10 premium –2008 Census has fed the 10/11 premiums which DoF received in January to sign off –Poor data quality of doctors now has a financial cost attached to individual trusts Planning/Targets –Commitment to increase the number of Health Visitors –Reduction in management costs –NHS Pharmacy Education and Development Committee – survey of staff numbers and vacancies in departments Commissioning Frameworks for Clinical areas – e.g. Diabetes – creation of Minimum Data Set using ESR as a potential feed

15. ESR data - iView Content –Annual Census – greater granularity –Monthly – Staff in post, Earnings, Sickness Absence, Turnover Benefits –More immediate than the Census –More accessible –More flexible –More fields –More potential Benchmarking between similar organisations Highlights DQ issues for further investigation

16. Data Quality and iView Benchmarking is only as good as the data that is input at source –key improving data quality of non-core payment data ESR data linked with iView can help to highlight data quality issues that have not previously been investigated Example of miscoding of Managers and Senior managers distorting figures –Provided area of focus for data quality messages –Lead to improvements in the guidance provided in the NHS Occupation Code Manual that are to be applied in other areas –Manager / Senior Manager coding now more reliable

17. DQ Example – Managers and Senior managers by Agenda for Change Band (Sept 2008) ManagerSenior ManagerTotal AfC Band 1 55 13,620 AfC Band 245550 AfC Band 31055110 AfC Band 426530295 AfC Band 51,110401,150 AfC Band 63,7952204,015 AfC Band 77,3056907,995 AfC Band 8a6,2351,6207,855 27,205 AfC Band 8b4,6602,0306,690 AfC Band 8c2,4102,1554,560 AfC Band 8d1,0851,4352,520 AfC Band 9195415615 Non AfC Grades1,5703,4104,965 Total28,71012,05040,720 Sept 08 Census Managers & Senior Managers39,913

18. Next Steps 1 – for the IC

19. Conclusion Improving data quality relies upon collaborative working – you cannot achieve it alone! Good data quality in administrative systems means we can stop burdensome collections on the service – saving time and money which can be better applied elsewhere. Aim is for data to be accepted as authorative by highly engaged users. Ultimately quality data will save money for the NHS by improving decisions (taken locally and nationally), improving commissioning and reducing the burden of additional data collections required where direct use of administrative data was not previously believed to fit the bill.

20. Data Standards

21. Data Standards Overview The National Workforce Dataset (NWD) version 2.3 - Area of Work / Job Role updates - New Sickness Absence Reasons - Additional Reasons for leaving The NHS Occupation Code Manual version 9 - New Nursing Codes - Changes to Medical Occupation Codes - Improved guidance notes The Healthcare Scientists Workforce Information Pilot - The Pilot, Why? How? When? - Example of the Healthcare Scientists

22. NWD Overview The National Workforce Dataset (NWD) is a reference dataset comprising standardised definitions to facilitate the capture of nationally consistent information relating to the NHS workforce. NWD data items and definitions under pin the ESR and support a variety of workforce based collections including the annual NHS Workforce Census. The NWD is primarily used in NHS organisations, mainly within HR and Workforce Planning functions to support planning and delivery for: –Services: the services required to meet the patients' needs and how they are planned to change –Workforce inputs: the workforce inputs required to deliver specific services –Requirements: How workforce inputs map onto skills, roles and numbers –Options: Options for changing the workforce demand through new service models or ways of working The NWD is reviewed on a continuous basis to ensure that it remains fit for purpose and is updated to reflect any changes to workforce policies and practices. NHS Occupation Codes are not part of the NWD but are referenced in the NWD and are updated and approved as part of the same process for consistency

23. NWD Version 2.3 - Updates Version 2.3 approved by ISB on 24 th November ISN to follow and update to details on the NHS IC website Area of Work updates –2 minor to correct the names of medical specialties New Sickness Absence Reasons –Implementation of more detailed list based on the Institute of Occupational Medicine Sickness Absence Recording Tool (SART) values(SART) Additional Reasons for leaving –3 new reasons for leaving to cover Mutually Agreed Resignation Schemes (MARS) To be implemented in ESR by 01/04/2011? Updates to Ethnic Categories in version 2.4? –Awaiting confirmation on optional detailed codes

24. The NHS Occupation Code Manual version 9 - updates New Nursing Codes - Nursing Assistant Practitioners (N*F) - Neonatal Nurses (including Special Care Baby Units) (N*L) - Removal of SCBU from Maternity Services (N*C) Changes to Medical Occupation Codes - Acute Internal Medicine as a separate specialty - 035 - Sport and Exercise Medicine – name corrected Improved guidance notes - References to Nursing and Midwifery Council updated - Scientific and Therapeutic and Healthcare Science Assistant Practitioners

25. Keep for mock up of N-matrix with new code added

26. Keep for mock up of M-matrix with new code added

27. Healthcare Scientists Workforce Information Pilot Why do we need the Pilot? Occupation codes are approaching 20 years old, trying to do two jobs, limited granularity especially for non-clinical roles Workforce data standards are out of line with general data model Difficult to relate workforce to activity and outcomes Big issues including productivity, patient safety etc. Also added pressures of significant changes to the structure of the NHS including movement out of the core NHS – and away from ESR? Revalidation – will go wider than GPs / Medics – how will the data support this?

28. Where are we now? Dated coding/classification schemes –Inconsistencies in recording –Guidance not always robust –Aimed at national collections, not mgt. information Non-clinical roles not well represented Poor levels of granularity Specialist surveys used to fill gaps –Public health –Informatics Inability to link with activity/outcomes/finance

29. Where would we like to be? Separation of –Role/profession/function, from –Patient-client group/specialty/work area, from –Setting/site/context Better coverage of non-clinical roles Links to activity and outcomes (i.e. tied into national data model) Robust guidance for HR depts. Clear/unambiguous validation rules

30. What do we want to know about the NHS Workforce? Aspirational list: –Registration / Profession –Qualified (yes/no or level?) –Clinical (yes/no) –Staff grouping / Occupation –Area of Work –Provider / Commissioner –Job Role (level?) –Care Group –Subjective code (and dependant codes?) What can realistically be achieved / expected to be accurately captured? Overarching need to tie in with Activity / Outcomes measures?

31. How do we get there? Pilots develop (where possible) –Occupation Codes –Areas of Work –Job Roles –Guidance (linking above items) –Validation rules (including algorithms) –Links to activity/outcomes –Settings Test classifications/guidance in the field Test generic application (i.e. to other staff groups) Follow ISB/ISN process Plan for first output in first quarter of 2011 – more fundamental changes will take longer Set up ongoing maintenance arrangements

32. Develop & Test revised standards Submit proposal to WIRG WIRG approval Submit to ISB Resolve any ISB issues ISB approval Draft and issue ISN ESR update software New ESR version released Employers update employee records

33. Healthcare Science: Current Situation & Developments Over 51 different HCS professions Confusing for those coding Not all roles are regulated Codes don ’ t match current roles in the service New roles and new ways of working Modernising Scientific Careers Simplifying access to the professions Simplifying career progression through the professional levels Increasing workforce flexibility Identifying 6 broad job levels across all professions

35. Healthcare Science: Difficulties Inconsistent approach to coding HCS – biggest problem Unable to obtain accurate picture of whole HCS workforce at local or national level via data warehouse – the census thought to under count by about 20,000 in England (coded elsewhere) Difficult to workforce plan without accurate data Difficult to identify the contribution made to the service by HCS Need to link coding to new MSC developments Mapping existing workforce to new career framework – example of an early win Persuading employers to recode – scope for mass update in ESR following consultation with employers? Identifying regulated professionals