Identifying the Digital Citizen

A case study from Mid-Yorkshire NHS Hospital Trust and

i+ IT Ltd

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

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%)

DQ Challenges• Data Cleansing team (2 wte)• Process:

1. Print registration report2. Check each sequentially in MPI for

errors/duplicate3. Make changes and record actions4. Repeat 1-3 above5. Inform / train – repeat offenders.

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

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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.

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

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

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

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

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

Level 2Local

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 1Initial

Level 3National

Level 4Pro-active

Level 5Managed

Level 6Optimised

Characterised by comprehensive monitoring and reporting of duplicate and confusion cases.

© i+ IT Ltd 2010

Other Inputs• CfH’s “IQAP Standard for Duplicate

Management on a Legacy PAS System” guidelines.

• IG Toolkit – Requirement 401, Attainment Levels

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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

Part 1-Improving Data Quality

• Central Team view new registrations and check for Data Quality

Pt 1- Improving Data Quality (cont.)

• Choose OK if happy – or update on PAS if not

Part 2 – Screens: New Registration

Pt 2 – Screens: Trace Result

Pt 2 – Screens: Complete

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’

What Next?

• Spine Compliance for MPI (?)• Reduces going back to PAS• Additional Reporting – feeds to

spreadsheets

Questions

?Ours: will CfH make things any better?

Yours…