Kambiz Boomla - EMIS NUG 2012_Kambiz_Ryan v3 1
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Data Warehousing and EMIS Web Dr Kambiz Boomla & Ryan Meikle September 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
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
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
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
Two main purposes
To produce combined data source dashboards
To provide clinical data from combined sources to directly support patient care
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 •
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 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: •
•
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
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