Identifying the Digital Citizen

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

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

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’.

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

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

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 – Screens: New Registration

19. Pt 2 – Screens: Trace Result

20. Pt 2 – Screens: Complete

21. 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’

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

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