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 BackgroundEast 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 Background3 CCGs, City & Hackney, Tower Hamlets, Newham with Waltham Forest to join cluster soonTrust mergersHomerton Foundation Trust in HackneyBarts and the London, Newham University Hospital and Whipps Cross all merging to form Barts HealthWider Commissioning Support Services and Cluster that includes outer east London, and North Central London – mirrors local configuration of National Commissioning Board
4 Learning Disabilities SUSDataEMIS Web QUTEEMIS Web Secondary CareEMIS Web Primary CareEMIS Web CommunityPCSLablinksLVXrayA&ESecondary careLVCommunity ServicesCare of the ElderlyDistrict NursingDiabetes CentrePBCHealth visitingStroke ServiceSpeech & LanguageUrgent CarePhysiotherapyA&E Front EndWound CareLearning DisabilitiesWalk-in CentresSpecialist nursesDiabetesHeart FailureStrokeRespiratoryOccupational TherapyGP Out of Hours x2Prim Care PsychologyThe PatientSchool NursingClinical AssessmentServiceDermatologyMusculoskeletalUrologyEMIS AccessFoot HealthChild HealthSocial Services eSAP ?Continence ServiceCommunity matronsMinor Surgery
5 Enhanced Services and Dashboards CCGs need dashboardsTo performance manage our enhanced servicesTrack integrate care pathwaysMonitor secondary careDashboards need to contain both primary and secondary care metrics, and even social careCreates 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 spendSimilar programmes in Hackney and NewhamIntegrated care with such an ambitious investment programme needs integrated ITMergers offer a unique opportunity to provide full integration between EMIS Web and Cerner
7 The 36 Tower Hamlets practices and the 8 LAP boundaries 53The 36 Tower Hamlets practices and the 8 LAP boundariesLAP 5. Bow West, Bow EastLAP 1. Weavers, Bethnal Green North, Mile End and Globe Town19Shah22St. Stephen’s20231Strouts Pl5TredegarAminMission23212Bethnal Green6Harley GroveGlobe TownPop: 25,5493Pollard Row55Pop: 38,5294Blithehale20LAP 6. Mile End East, Bromley by Bow66193142224Stroudley WalkBromley by Bow22126Rana267’2725St Paul’s Way2724NischalPop: 33,9487LAP 2. Spitalfields and Banglatown, Bethnal Green SouthPop: 27,6921028Limehouse30Chrisp StLAP 7. Limehouse, East India Lansbury29Selvan31All Saints32Aberfeldy8914258Health E110Albion139712SpitalfieldsXX place*Pop: 23,86830321115291631LAP 3. Whitechapel, St. Duncan’s and Stepney Green28Pop: 36,43311Shah Jalal13VarmaPop: 28,95612Tower14Stepney1718LAP 8. Millwall, Blackwall and Cubitt townLAP 4. St. Katharine’s and Wapping, ShadwellPop: 30,03433Barkantine35Island Health15East One17St Katherine’sDock33343636DocklandsIsland Med Ctr16Jubilee St18Wapping3534* 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)*
11 Combining secondary and primary care in one dashboard Two main purposesTo produce combined data source dashboardsTo 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 needsTo provide clinical data from combined sources to directly support patient careProviding timely and accurate info on which to base clinical decision makingImproving the co-ordination between different healthcare providersFacilitate better patient care by sharing patient information between healthcare providersThese 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 benefitData ControllerCommunity HealthGeneral PracticeAcute HospitalDataProcessor eg NCEL Commissioning Support ServicesMental Health DataSocial 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 providersAn 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 queriesVisual Reporting – automated tables, charts, mapsInvestigation – hypothesis testing, predictionCommon interface to explore data regardless of source system
17 Data Warehouse Architecture 4. User Interface3. Solutions – dashboards, reports, risk prediction2. Warehousing1. Data Extraction
18 1. Data Extraction No “one size fits all” solution Extract once – but use for multiple purposesChallenges:Keeping volume of data manageableLimited options for extractionAutomating where possibleWorking with EMIS IQ to bulk extract data for dashboard reporting and patient care
19 Data Warehouse Architecture 4. User Interface3. Solutions – dashboards, reports, risk prediction2. Warehousing1. Data Extraction
20 2. WarehousingData processed into a common structure, regardless of source systemData cleansing and standardisation – need to be able to compare “like for like”Challenges:Conflicting between systemsData matching
21 Data Warehouse Architecture 4. User Interface3. Solutions – dashboards, reports, risk prediction2. Warehousing1. Data Extraction
22 3. SolutionsNeed to know up front who will be the users of the system and what they will want to use it forDifferent users will have different perspectives e.g. concept of PMIChallenges: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 Interface3. Solutions – dashboards, reports, risk prediction2. Warehousing1. Data Extraction
24 4. User Interface The only part most people see (and judge) Very large number of tools availableNeed 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 dataUsing 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 GPsPilot additional solutions based on data forecasting and prediction