Data Warehousing and EMIS Web

Dr Kambiz Boomla & Ryan MeikleSeptember 2012

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

SUSData

District Nursing

Health visiting

Speech & Language

Physiotherapy

Learning Disabilities

Occupational Therapy

Prim Care Psychology

School Nursing

Foot Health

Child Health

Continence Service

Wound Care

Specialist nurses•Diabetes

•Heart Failure•Stroke

•Respiratory

Clinical Assessment

Service•Dermatology

•Musculoskeletal•Urology

The Patient

PBC

Community matrons

A&E Front End

GP Out of Hours x2

Diabetes Centre

Care of the Elderly

Minor Surgery

Lablinks

Xray

Social Services eSAP ?

Walk-in Centres

A&E

Urgent Care

Secondary care

Community Services

Stroke Service

EMIS Access

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

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

6

5

The 36 Tower Hamlets practices and the 8 LAP boundaries

*

123

4

5

6

8 Health E1

9

10 Albion

LAP 2. Spitalfields and Banglatown, Bethnal Green South 8

9

10

7

LAP 1. Weavers, Bethnal Green North, Mile End and Globe Town

1 Strouts Pl

2 Bethnal Green

3 Pollard Row

4 Blithehale

7 XX place*

5 Mission

6 Globe Town

1112

15

13

16

14

11 Shah Jalal

12 Tower

13

LAP 3. Whitechapel, St. Duncan’s and Stepney Green

14

Spitalfields

Varma

Stepney 17

18

19 Shah 22 St. Stephen’s

LAP 5. Bow West, Bow East

20 Tredegar

21 Harley Grove

23 Amin

19

24

2122

20

23

24 Rana

25St Paul’sWay

26StroudleyWalk

LAP 6. Mile End East, Bromley by Bow

27 Nischal

25

2627

28

29

30

31

32

28 Limehouse 30 Chrisp St

LAP 7. Limehouse, East India Lansbury

29 Selvan 31All Saints

32 Aberfeldy

33 Barkantine 35 Island Health

LAP 8. Millwall, Blackwall and Cubitt town

34 Docklands 36 Island Med Ctr33

34

35

36

17St Katherine’s Dock

18 Wapping

LAP 4. St. Katharine’s and Wapping, Shadwell

16 Jubilee St

15 East One

Pop: 28,956

Pop: 23,868

Pop: 38,529

Pop: 33,948

Pop: 27,692

Pop: 25,549

Pop: 36,433

Pop: 30,034

7’Bromleyby Bow

* Estimated registered population, calculated as ½ of Bromley-by-Bow and XX place combined listSource::http://www.towerhamlets.gov.uk/data/in-your-ward; Allocation practice to LAP as per Team Analysis (Aug 2008); Number of patients per

practice based on LDP data (Jan 2009)

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

Data flowsThese are the organisations where data sharing/flow could result in patient

benefit

Data Controller

• Community Health

Data Controller

• General Practice

Data Controller

• Acute Hospital

Data Controller

Data Controller

Data Processor eg NCEL

Commissioning Support Services

• Mental Health Data

• Social Services Data

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

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.

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.

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

Data Warehouse Architecture

1. Data Extraction

2. Warehousing

3. Solutions – dashboards, reports, risk prediction

4. User Interface

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

Data Warehouse Architecture

1. Data Extraction

2. Warehousing

3. Solutions – dashboards, reports, risk prediction

4. User Interface

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

Data Warehouse Architecture

1. Data Extraction

2. Warehousing

3. Solutions – dashboards, reports, risk prediction

4. User Interface

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

Data Warehouse Architecture

1. Data Extraction

2. Warehousing

3. Solutions – dashboards, reports, risk prediction

4. User Interface

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?

Demonstration1. Using the warehouse to report SUS data

2. Using the warehouse to report EMIS data

3. Using the warehouse to explore combined GP and Acute data

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

Appendix: Screenshots

I. Using the warehouse to report SUS data

II. Using the warehouse to report EMIS data

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