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

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Presentation on theme: "Data Warehousing and EMIS Web Dr Kambiz Boomla & Ryan Meikle September 2012."— Presentation transcript:

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


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 EMIS Web QUTE EMIS Web Secondary Care EMIS Web Primary Care EMIS Web Community SUS Data 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

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 6 5 The 36 Tower Hamlets practices and the 8 LAP boundaries * Health E Albion LAP 2. Spitalfields and Banglatown, Bethnal Green South 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 Shah Jalal 12 Tower 13 LAP 3. Whitechapel, St. Duncan’s and Stepney Green 14 Spitalfields Varma Stepney Shah 22 St. Stephen’s LAP 5. Bow West, Bow East 20 Tredegar 21 Harley Grove 23 Amin Rana 25 St Paul’s Way 26 Stroudley Walk LAP 6. Mile End East, Bromley by Bow 27 Nischal Limehouse 30 Chrisp St LAP 7. Limehouse, East India Lansbury 29 Selvan 31 All Saints 32 Aberfeldy 33 Barkantine 35 Island Health LAP 8. Millwall, Blackwall and Cubitt town 34 Docklands 36 Island Med Ctr St 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’ Bromley by Bow * 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 Data flows These 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 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 1. Data Extraction 2. Warehousing 3. Solutions – dashboards, reports, risk prediction 4. User Interface

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

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

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

24 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 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

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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