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The need for Business Intelligence in Healthcare – 2011 and beyond Jeremy Nettle Director, Global Client Advisor.

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Presentation on theme: "The need for Business Intelligence in Healthcare – 2011 and beyond Jeremy Nettle Director, Global Client Advisor."— Presentation transcript:

1 The need for Business Intelligence in Healthcare – 2011 and beyond Jeremy Nettle Director, Global Client Advisor


3 The key issues in the Information Revolution away from information belonging to the system, to information enabling patients and service users to be in clear control of their care; away from patients and service users merely receiving care, to patients and service users being active participants in their care; away from information based on administrative and technical needs, to information which is based on the patient or service user consultation and on good clinical and professional practice; away from top-down information collection, to a focus on meeting the needs of individuals and local communities; away from a culture in which information has been held close and recorded in forms that are difficult to compare, to one characterised by openness, transparency and comparability; away from the Government being the main provider of information about the quality of services to a range of organisations being able to offer service information to a variety of audiences; and in relation to digital technologies, away from an approach where we expect every organisation to use the same system, to one where we connect and join up systems.

4 “You can have data without information, but you cannot have information without data” Daniel K. Moran said

5 Health trends Educated and more demanding population – Able and willing to be empowered in managing their disease – Looking for more transparency Ageing population – Use Healthcare services for a longer time – More complicated to treat (e.g. multiple illness / comorbidities) Medical advances – More effective drugs if patients are compliant – Technology allows effective mobile monitoring – Predictive analysis is making progress Funding is under pressure – A decreasing working/non-working population ratio – Average OECD country Healthcare budget > 20% of GDP in 2050 if nothing changes – Governments are commonly looking and revising the role of payer and provider – Providers are increasing being placed in a competitive environment

6 England’s 5 Academic Health Science – ‘Super Trusts’ They are: Cambridge University Health Partners: Cambridge University; Cambridge University Hospitals, Papworth Hospital, and Cambridgeshire and Peterborough foundation trusts Imperial AHSC: Imperial College London and Imperial College Healthcare trust King's Health Partners: King's College London; Guy's and St Thomas', King's College Hospital, and South London and Maudsley foundation trusts Manchester AHSC: Manchester University; Central Manchester University Hospitals, Christie Hospital, Salford Royal, and University Hospital of South Manchester foundation trusts; Manchester Mental Health and Social Care trust; Salford PCT UCL Partners: University College London; University College London Hospitals and Moorfields Eye Hospital foundation trusts; Great Ormond Street Hospital for Children and Royal Free Hampstead trusts

7 Issues/Opportunities for Integrated Care Trusts (ICTS) Interoperability - All have multiple data sources and clinical systems that do not integrate. Business Intelligence – All are looking to pool their clinical information to a common Data-warehouse for BI forecasting, budgeting and demand generation. Patient relationship management – all want to manage their customer patient experience. Bench to Bedside – All want to minimise cost of research An opportunity for Oracle and the ICTs to work together

8 The Spectrum of BI Technologies Among business intelligence disciplines, prediction provides the most business value but is also the most complex. Each discipline builds on the one below it—these are additive, not exclusive, in practice.

9 Predictive modelling in Healthcare? Predictive modelling aims to identify individuals who are at risk of incurring significant care costs at some future time; for example, the risk of unplanned hospital readmission over the next year. Such models exploit linked, individual-level datasets to establish the relationships between characteristics of the individual (predictor variables such as measures of health need and diagnoses) and a subsequent event (outcome variables such as hospital readmission). Predictive models find a variety of uses in healthcare. In recent years they have been introduced in case finding tools in the NHS where the aim is to target preventive interventions aimed at promoting health and independence so as to avoid later hospitalisation.

10 U.S. HOSPITAL BED SUPPLY SHRINKS WHILE HOSPITAL WORKFORCE GROWS. April 2, 2009: The supply of hospital beds and doctors varies widely from region to region across the United States, and the variations have nothing to do with the level of care patients want or need, according to a new report from the Dartmouth Atlas Project. "Hospital and Physician Capacity Update," analyzes current hospital and physician capacity as well as trends over a 10-year period, and reveals irrational distribution of these valuable and expensive health care resources. The report finds similar wide and persistent variations in capacity in 2006 to those that existed in 1996, generally in the same places."Hospital and Physician Capacity Update,"

11 From Evidence to Decision but who’s? If a patient does not have sufficient information is it a question of Doctor knows best

12 Differences in male life-expectancy within a small area of London : travelling east from Westminster, every two Tube stops represent over one year of life-expectancy lost (data revised to 2004–2008) Source: Analysis by London Health Observatory of ONS and GLA data for 2004–2008. Adapted from a diagram produced by Department of Health

13 Interactive Dashboards Geospatial Visualization Office Integration Mobile Reports Data Analytics Oracle Business Intelligence Complete. Consistent. Accurate. Applications & Portals Search Ad-hoc Queries Collaboration Scorecards

14 Magic Quadrant for Business Intelligence Platforms “Oracle’s Crystal Ball can run 10,000 different Monte Carlo simulations within two or three minutes and give business managers the probabilities of the success of different scenarios for maximizing profits or customer service. Companies can pick the scenario that represents the least risk and the biggest payoff." Source: Gartner (January 2010)

15 Oracle and the NHS

16 16 The connected HC/LS ecosystem Patients Life Sciences Healthcare Pharmaceutical Medical Eqpt Integrated Networks Hospitals Primary Care Outpatient Other Biotech Distributor/ Wholesaler Regulatory Agency Physicians Payer Employer Retailer PayerProvider

17 National and Large Scale Procurements – Ways We Engage Electronic Staff Record – Product: Oracle HCM – Partner: McKesson Consortium – Scope: For 1.5M employees – Status: 700,000+ employees across 370 organisations Technology Enterprise Wide Agreement – Product:Oracle Technology stack as of May 2004 price list – Partner:All NPfIT primes – Scope:Unlimited Deployment Licence for all NHS organisations – Status:Spine, SUS, Choose & Book, Cerner, INPS, etc. NHS Shared Business Services – Product:Oracle Financials & Procurement – Partner:Dept. of Health & Steria – Scope:Site licence for all NHS organisations – Status:120 sites (total Oracle sites 220) North East Patches – Shared System Product: Oracle Financials & Procurement Partner: Direct Scope: Perpetual licence for 50 organisations Status: 60 organisations plus EPM and CRM

18 Oracle’s Strategy Deliver a complete, open, integrated stack of hardware, infrastructure, database, middleware, and business applications – Integrate components of Oracle’s software stack to provide unique value to customers – Innovate in each and every component so that is the best in its category Exploit processor, systems, storage, and networking trends to deliver breakthrough innovations by combining Oracle software with Sun hardware

19 In summary – What are the challenges Source: Department of Health The Information Revolution 2010

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