Transcript Kambiz Boomla - EMIS NUG 2012_Kambiz_Ryan v3 1

Data Warehousing and EMIS Web
Dr Kambiz Boomla & Ryan Meikle
September 2012
Background
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3 CCGs, City & Hackney, Tower Hamlets,
Newham with Waltham Forest to join cluster
soon
Trust mergers
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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
EMIS Web QUTE
SUS
Data
EMIS Web Secondary Care
EMIS Web Primary Care
Lablinks
EMIS Web Community
Xray
A&E
Secondary care
Care of the Elderly
Community Services
Diabetes Centre
District Nursing
Health visiting
Stroke Service
Speech & Language
PBC
Physiotherapy
Urgent Care
A&E Front End
Learning Disabilities
Walk-in Centres
Occupational Therapy
GP Out of Hours x2
Prim Care Psychology
The Patient
School Nursing
EMIS Access
Specialist nurses
•Diabetes
•Heart Failure
•Stroke
•Respiratory
Continence Service
Clinical
Assessment
Service
•Dermatology
•Musculoskeletal
•Urology
Community matrons
Minor Surgery
Foot Health
Child Health
Social Services eSAP ?
Wound Care
Enhanced Services and
Dashboards
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CCGs need dashboards
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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
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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
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
1
Strouts Pl
5
Mission
2
Bethnal Green
6
Globe Town
3
Pollard Row
4
Blithehale
23
19 Shah
22 St. Stephen’s
20 Tredegar
23 Amin
21 Harley Grove
Pop: 25,549
5
Pop: 38,529
6
LAP 6. Mile End East, Bromley by Bow
19
3
4
2
1
20
21
22
26
24 Rana
27
25
24
Pop: 33,948
LAP 2. Spitalfields and
Banglatown, Bethnal Green
South
8 Health E1
9 Spitalfields
10 Albion
14 Stepney
Bromley
by Bow
St Paul’s
27 Nischal
Way
LAP 7. Limehouse, East India Lansbury
25
14
9
13
12
11
LAP 3. Whitechapel, St.
Duncan’s and Stepney Green
12 Tower
7’
Pop: 27,692
15
13 Varma
Stroudley
Walk
7
10
8
7 XX place*
11 Shah Jalal
26
30
Pop: 23,868
29
16
30 Chrisp St
29 Selvan
All
31
Saints
32 Aberfeldy
31
Pop: 36,433
28
17
32
28 Limehouse
Pop: 28,956
18
LAP 4. St. Katharine’s and
Wapping, Shadwell
St Katherine’s
17 Dock
15 East One
Pop: 30,034
33
16 Jubilee St
LAP 8. Millwall, Blackwall and Cubitt
town
36
18 Wapping
33 Barkantine
35 Island Health
34 Docklands
36 Island Med Ctr
35
34
* Estimated registered population, calculated as ½ of Bromley-by-Bow and XX place combined list
Source::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
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Two main purposes
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To produce combined data source dashboards
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To provide clinical data from combined sources to directly
support patient care
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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
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 flows
These are the organisations where data sharing/flow could result in patient
benefit
Data
Controller
• Community
Health
Data
Controller
Data
Processor eg NCEL
Commissioning
Support Services
Data
Controller
Data
Controller
Data
Controller
• General Practice
• Acute Hospital
• 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
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Scenario 1 – Risk Stratification
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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
4. User Interface
3. Solutions –
dashboards, reports,
risk prediction
2. Warehousing
1. Data Extraction
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
4. User Interface
3. Solutions –
dashboards, reports,
risk prediction
2. Warehousing
1. Data Extraction
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
4. User Interface
3. Solutions –
dashboards, reports,
risk prediction
2. Warehousing
1. Data Extraction
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:
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•
Understanding what people expect from a data
warehouse – joined up data? Better reporting?
Building the model to support future requests
Data Warehouse Architecture
4. User Interface
3. Solutions –
dashboards, reports,
risk prediction
2. Warehousing
1. Data Extraction
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?
Demonstration
1. 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