1. Data Warehousing and EMIS Web Dr Kambiz Boomla & Ryan Meikle
September 2012

2. Background
East London – diverse community, database of over 800,000 patients all bar two practices on streaming into EMIS Web, with go live dates in the diary for 95% of practices to be live by April 2013.

3. Background
3 CCGs, City & Hackney, Tower Hamlets, Newham with Waltham Forest to join cluster soon
Trust mergers
Homerton Foundation Trust in Hackney
Barts and the London, Newham University Hospital and Whipps Cross all merging to form Barts Health
Wider Commissioning Support Services and Cluster that includes outer east London, and North Central London – mirrors local configuration of National Commissioning Board

4. Learning Disabilities
SUS
Data
EMIS Web QUTE
EMIS Web Secondary Care
EMIS Web Primary Care
EMIS Web Community
PCS
Lablinks LV
Xray
A&E
Secondary care LV
Community Services
Care of the Elderly
District Nursing
Diabetes Centre
PBC
Health visiting
Stroke Service
Speech & Language
Urgent Care
Physiotherapy
A&E Front End
Wound Care
Learning Disabilities
Walk-in Centres
Specialist nurses
Diabetes
Heart Failure
Stroke
Respiratory
Occupational Therapy
GP Out of Hours x2
Prim Care Psychology
The Patient
School Nursing
Clinical Assessment
Service
Dermatology
Musculoskeletal
Urology
EMIS Access
Foot Health
Child Health
Social Services eSAP ?
Continence Service
Community matrons
Minor Surgery

5. Enhanced Services and Dashboards
CCGs need dashboards
To performance manage our enhanced services
Track integrate care pathways
Monitor secondary care
Dashboards need to contain both primary and secondary care metrics, and even social care
Creates complex information governance issues

6. Networks are the basis for Primary Care Investment Plan
Tower Hamlets commencing on ambitious primary care investment plan as part of being an Integrated Care Pilot.
£12m investment annually raising Tower Hamlets from near the bottom to the top for primary care spend
Similar programmes in Hackney and Newham
Integrated care with such an ambitious investment programme needs integrated IT
Mergers offer a unique opportunity to provide full integration between EMIS Web and Cerner

7. The 36 Tower Hamlets practices and the 8 LAP boundaries53
The 36 Tower Hamlets practices and the 8 LAP boundaries
LAP 5. Bow West, Bow East
LAP 1. Weavers, Bethnal Green North, Mile End and Globe Town 19
Shah 22
St. Stephen’s 20 23 1
Strouts Pl 5
Tredegar
Amin
Mission 23 21 2
Bethnal Green 6
Harley Grove
Globe Town
Pop: 25,549 3
Pollard Row 5 5
Pop: 38,529 4
Blithehale 20
LAP 6. Mile End East, Bromley by Bow 6 6 19 3 1 4 22 24
Stroudley Walk
Bromley by Bow 2 21 26
Rana 26 7’ 27 25
St Paul’s Way 27 24
Nischal
Pop: 33,948 7
LAP 2. Spitalfields and Banglatown, Bethnal Green South
Pop: 27,692 10 28
Limehouse 30
Chrisp St
LAP 7. Limehouse, East India Lansbury 29
Selvan 31
All Saints 32
Aberfeldy 8 9 14 25 8
Health E1 10
Albion 13 9 7 12
Spitalfields
XX place*
Pop: 23,868 30 32 11 15 29 16 31
LAP 3. Whitechapel, St. Duncan’s and Stepney Green 28
Pop: 36,433 11
Shah Jalal 13
Varma
Pop: 28,956 12
Tower 14
Stepney 17 18
LAP 8. Millwall, Blackwall and Cubitt town
LAP 4. St. Katharine’s and Wapping, Shadwell
Pop: 30,034 33
Barkantine 35
Island Health 15
East One 17
St Katherine’s
Dock 33 34 36 36
Docklands
Island Med Ctr 16
Jubilee St 18
Wapping 35 34
* Estimated registered population, calculated as ½ of Bromley-by-Bow and XX place combined list
Source:: Allocation practice to LAP as per Team Analysis (Aug 2008); Number of patients per practice based on LDP data (Jan 2009) *

11. Combining secondary and primary care in one dashboard
Two main purposes
To produce combined data source dashboards
To enable collection and exploitation of data to support the pro-active targeting of effective health interventions, partially through improved commissioning but also by being able to better identify and address individual needs
To provide clinical data from combined sources to directly support patient care
Providing timely and accurate info on which to base clinical decision making
Improving the co-ordination between different healthcare providers
Facilitate better patient care by sharing patient information between healthcare providers
These two main purposes require different information governance frameworks

12. Processor eg NCEL Commissioning Support Services
Data flows These are the organisations where data sharing/flow could result in patient benefit
Data Controller
Community Health
General Practice
Acute Hospital
Data
Processor eg NCEL Commissioning Support Services
Mental Health Data
Social Services Data

13. There will be three principle types of data flow, although the lawful basis for processing differs in the second between health and social care Data Controllers. These will be sequenced to minimise the data in each flow and from each provider, as shown below.
An explanation of these data flows is on the next slide

14. Data flows Scenario 1 – Risk Stratification
We first take hospital data from the SUS (Secondary Use Services) dataset. This dataset already has s251 allowing the common law duty of confidentiality to be set aside in specific circumstances. It will then be combined with pseudonymised GP data, and then analysis then performed on the pseudonymised combined dataset. Dashboards and risk scores and commissioning information can then be made available. If we need to get back to knowing who the patients really are, because we can offer them enhanced care, then only practices will unlock the pseudonyms and refer patients appropriately . EMIS to do work here!!!
Scenario 2 – Information sharing between health care providers
An obvious example of this is the virtual ward. Virtual ward staff including modern matrons work most efficiently with access to patient information from all those agencies involved in their care. Information sharing in this scenario would rely on explicit patient consent for GP data, and hospital provider data is already part of the commissioning contract requirements for secondary care, and only holding this and making this available for those patients being cared for in this scenario, and not all patients.
Scenario 3 – Similar to 2 above, but also involve social care providers.
An example of this, could be obtaining for elderly patients already receiving social care from social services, their long term condition diagnoses to record on social services information systems. Similarly the type of care packages they are on could be provided to General Practices. Explicit patient consent would be required for data flows in each direction here. Also if health and social care data were shared in a virtual ward, explicit patient consent will be required.

15. Information Governance
This project will adopt the highest standards of information governance to ensure that patient’s rights are respected and that the confidentiality, integrity and availability of their information is maintained at all times.
The approval of the National Information Governance Board for this has been obtained.

16. Data Warehousing – why do it?
Systematic management of large amounts of data optimised for:
Fast searches – pre-calculation of common queries
Visual Reporting – automated tables, charts, maps
Investigation – hypothesis testing, prediction
Common interface to explore data regardless of source system

17. Data Warehouse Architecture
4. User Interface
3. Solutions – dashboards, reports, risk prediction
2. Warehousing
1. Data Extraction

18. 1. Data Extraction No “one size fits all” solution
Extract once – but use for multiple purposes
Challenges:
Keeping volume of data manageable
Limited options for extraction
Automating where possible
Working with EMIS IQ to bulk extract data for dashboard reporting and patient care

19. Data Warehouse Architecture
4. User Interface
3. Solutions – dashboards, reports, risk prediction
2. Warehousing
1. Data Extraction

20. 2. Warehousing
Data processed into a common structure, regardless of source system
Data cleansing and standardisation – need to be able to compare “like for like”
Challenges:
Conflicting between systems
Data matching

21. Data Warehouse Architecture
4. User Interface
3. Solutions – dashboards, reports, risk prediction
2. Warehousing
1. Data Extraction

22. 3. Solutions
Need to know up front who will be the users of the system and what they will want to use it for
Different users will have different perspectives e.g. concept of PMI
Challenges:
Understanding what people expect from a data warehouse – joined up data? Better reporting?
Building the model to support future requests

23. Data Warehouse Architecture
4. User Interface
3. Solutions – dashboards, reports, risk prediction
2. Warehousing
1. Data Extraction

24. 4. User Interface The only part most people see (and judge)
Very large number of tools available
Need to decide what is most important:
Immediate solutions?
Ability to customise?
All-in-one warehouse and user interface?

25. Demonstration Using the warehouse to report SUS data
Using the warehouse to report EMIS data
Using the warehouse to explore combined GP and Acute data

26. Next Steps Use the warehouse to enhance existing clinical dashboards
Provision of risk scores to GPs
Pilot additional solutions based on data forecasting and prediction

27. Appendix: Screenshots

28. I. Using the warehouse to report SUS data

34. II. Using the warehouse to report EMIS data

38. III. Using the warehouse to explore combined GP and Acute data