1. Identifying the Digital Citizen A case study from Mid-Yorkshire NHS Hospital Trust and i+ IT Ltd

2. Mid Yorks DQ challenges Using I+IT I+IT methodology Patient Identification Maturity Model Solution overview Benefits

3. DQ Challenges 15+ years old PAS Not spine compliant Numbers –Circa 1,400,000 ids on our MPI –950,000 with verified NHS # –537 new registrations a week –700 after GP order comms turned on –26 duplicates a week (4%)

4. DQ Challenges Data Cleansing team (2 wte) Process: 1. Print registration report 2. Check each sequentially in MPI for errors/duplicate 3. Make changes and record actions 4. Repeat 1-3 above 5. Inform / train – repeat offenders.

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6. Using I+IT Clinical Directors ‘mate’ Understood the problem CfH experience (not sure its a +) Ensemble experience Fixed price Very quick to deliver ->

7. Delivering the Solution We used our specialised methodology iMethod+ Series of ‘fixed-price’ steps –iDiscover+: Quick investigation of the problem, two people, 1 day then report. <10 days turnaround –iAssess+: Longer high level solution design piece – in this case merged with: –iConnect+: Deliver the solution.

8. Benefits of iMethod+ MY gets price certainty as risk is reduced. Customer can ‘pull’ projects that will be too expensive before money is spent i+ gets better understanding of problem space so can ‘hit the ground running’ at start of next phase Onus on us to become more efficient

9. Patient Identification: A complex problem Patient Identification is not a simple issue NHS Numbers –People do not know or carry them –Many Acute Trust systems do not store or use them A continuum of differing practices within and between Trusts These experiences suggested a Maturity Model (MM) approach to progress the problem Hence a Patient Identification MM or PIMM © i+ IT Ltd 2010

10. Maturity Models (MMs) Started with Capability Maturity Model – late 1980’s, early 1990s Applied to many other contexts since Continuum from some starting point to an idealised goal end state. Way of breaking down complex problems into manageable solution steps – In terms of costs – In terms of organisational change – In terms of process © i+ IT Ltd 2010

11. Advantages of MMs A place to start working to improve a process or system. Gain the benefit of a wider community’s prior experiences. Provides a common language and a shared vision. Provides a framework for prioritising actions to yield the best value return on investment. Is a way to define what improvement means for an organisation within a given ‘domain’. © i+ IT Ltd 2010

12. The Patient Identification MM (PIMM) 6 levels (for now) Initial state – each system creates a new record for each encounter Idealised end state: “One Patient, One Identifier, universally available to all health providers” Application of it advocates decision support systems over decision making systems © i+ IT Ltd 2010

13. Level 2 Local Characterised by poorly controlled patient identity, proliferation of duplicates and confusions. Reactive processes to reconcile. Characterised by institutionalised implementation of local Patient identification Processes and Policies. Characterised by robust workflows with Patient verification against national systems part of “business as usual”. Characterised by continuous, pro-active improvement in identification data quality. Characterised by insignificant number of duplicates and confusions, identity synchronised to national register using fully compliant PAS. Level 1 Initial Level 3 National Level 4 Pro-active Level 5 Managed Level 6 Optimised Characterised by comprehensive monitoring and reporting of duplicate and confusion cases. © i+ IT Ltd 2010

14. Other Inputs CfH’s “IQAP Standard for Duplicate Management on a Legacy PAS System” guidelines. IG Toolkit – Requirement 401, Attainment Levels ->

15. The Solution - ProjectBB Built on top of Ensemble 4/6 weeks to deliver Real time registrations (no more lists) Confidence matching (can target efforts) Record status and ‘lock’ record Less staff cleaner data

16. Part 1-Improving Data Quality Central Team view new registrations and check for Data Quality

17. Pt 1- Improving Data Quality (cont.) Choose OK if happy – or update on PAS if not

18. Part 2 - Check for Duplicates Recipe for success - start simply and improve/develop Took the first ‘DQ’ system we deployed and expanded. Included a ‘possible duplicates’ check Pull key data from the PAS to assist the DQ team without having to re-query PAS for DQ investigations.

19. Part 2 – Screens: New Registration

20. Pt 2 – Screens: Trace Result

21. Pt 2 – Screens: Trace Criteria

22. Pt 2 – Screens: Complete

23. What we gained Prioritise workload Quicker identification of DQ errors Reduced Duplicates NHS Numbers – do we have them? ‘Spread the love’ not tied to ‘the office’

24. What Next? Spine Compliance for MPI (?) Reduces going back to PAS Additional Reporting – feeds to spreadsheets

25. Questions ? Ours: will CfH make things any better? Yours…