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Data Warehousing and EMIS Web Dr Kambiz Boomla & Ryan Meikle

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Presentation on theme: "Data Warehousing and EMIS Web Dr Kambiz Boomla & Ryan Meikle"— Presentation transcript:

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 boundaries
53 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








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