How To Predict Hospital Admission

Transcription

1 LA FRAGILITA : DAI MODELLI TEORICI ALLA VALUTAZIONE DELLE ESPERIENZE IBKOST: OD TEORETICNIH MODELOV DO OCENJEVANJA IZKU ENJ Predictive Models for Health and Social Care Progetto strategico "E-health /Strate kega projekta "E-heath" Dr Martin Bardsley Ravenna, 12 ottobre 2012

2 The Nuffield Trust Promote independent analysis and informed debate on healthcare policy across the UK Charitable organization founded in 1940 Formerly a grant-giving organization Since 2008 we have been conducting in-house research and policy analysis Significant interest in uses of predictive risk techniques William Morris 1st Viscount Nuffield ( )

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4 NHS (UK) Populations: NHS England: 51.2m; NHS Scotland: 5m; NHS Wales: 2.9m; NHS N Ireland: 1.7m England: 10 strategic health authorities From: Predictive Models for Health and Social Care: A Feasibility Study

5 Background Comprehensive benefits Free at the point of use (copayments minimal) UK health care spending ( ) 9.4 % GDP (1% private, 8.4% public) UK NHS ( ) billion (approx 1730 per capita ) From: Predictive Models for Health and Social Care: A Feasibility Study

6 New funding arrangements

7 UK health spending as share of GDP

8 Are emergency admissions rising? Number of emergency admissions in England , with period investigated marked in red Possible reasons Ageing population Public expectations Care of frail older people Defensive medicine Central targets / payment by results Changes in other linked services Over reliance on A&E for urgent care Reproduced from Trends in emergency admissions in England : is greater efficiency breeding inefficiency

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10 Uses of predictive risk techniques Predictive modelling aims to identify people at risk of future event

11 Introduction of predictive modelling to UK BMJ in paper in 2002 showed Kaiser Permanente in California seemed to provide higher quality healthcare than the NHS at lower cost. Kaiser identify high risk people in their population and manage them intensively to avoid admissions Follow up paper noted chief executive of a managed care organisation commented: Without case management, we are sunk in the marketplace. Getting more for their dollar: a comparison of the NHS with California's Kaiser Permanente BMJ 2002;324: Can the NHS learn from US managed care organisations? BMJ 2004;328:

12 Predictive modelling is only as effective as the intervention it is used to trigger Top 0.5% % 6-20% % Case Management Intensive Disease Management Less Intensive Disease Management Wellness Programmes Providers need to know potential costs of the outcome to build business case for intervention

13 To prevent, we need to know who will be an intensive user in the future Predictive models try to identify people here It s not the people who are current intensive users

14 From: Predictive Models for Health and Social Care: A Feasibility Study

15 Why use predictive risk to find cases? New research last year showed that neither doctors, nurses nor case managers were able to predict which patients were at highest risk of readmission to hospital. An alternative approach to clinicians predicting hospital admissions is to use statistical models instead. (Allaudeen et al, Journal of General Internal Medicine, 2011)

16 Population level screening for high risk individivuals At the start of the year, no one knows who s who A predictive risk model tries to sort it out

17 Introduction of predictive modelling to UK Patterns in routine data identify highrisk people next year Relies on exploiting existing information: +ve: systematic; not costly data collections; fit into existing systems -ve: information collected may not be predictive Use pseudonymous, person-level data In health sector a number of predictive models are available e.g. PARR++ and the combined model.

18 Developing a predictive risk model

19 Protecting individuals identities

20 Developing a predictive risk model

21 A predictive risk tool: PARR In 2006, the Department of Health (DH) invested in two predictive models (or risk stratification tools ) for the NHS in England. Hospital provides SUS PARR widely used by NHS (because software was free and SUS data only) PCT runs PARR++ Predicts readmission in next year PPV 65% Designed to be run by PCTs periodically, requires up-to-date diagnostic codes Patients selected for intervention (via GP)

22 Key metrics for performance of a model (PPV and sensitivity) Positive predictive value Sensitivity Trade-off between PPV and sensitivity sometimes summarised as the c-statistic Not very intuitive, and reflects average performance across all risk levels

23 Typical accuracy models currently used to predict hospital admission Model Risk threshold PPV (%) Sensitivity (%) PARR (England) SPARRA (Scotland) S Care model (Pooled 1K) From: Predictive Models for Health and Social Care: A Feasibility Study

24 Emerging market in England August 2011, the Department of Health announced that it had no plans to commission national updates of the latest Patients at Risk of Rehospitalisation tool (PARR++) or the Combined Predictive Model Range of new/established commercial organisation developing risk tools Creation of new commissioning groups and new markets Increasing ease of accessing GP data Continuing financial pressures and the search for ways to reduce emergency hospital care. SPARRA PARR (++) SPARRA MD PRISM AHI Risk adjuster ACGs (Johns Hopkins) DxCGs (Verisk) SCOPE LACE Combined Predictive Model PEONY LACE MARA (Milliman Advanced Risk Adjuster) Dr Foster Intelligence QResearch models eg QD score RISC Variants on basic admission/readmission predictions: Short term readmissions Social care Condition specific tools costs

25 Examples of some models used in UK Readmission to hospital within 1 year eg PARR, SPARRA Readmission within 30 days eg LACE, PARR30 Admission to hospital eg Combined Predictive Model, ACGs,PRISM Move into intensive social care eg Nuffield Model Likelihood of chronic disease eg QRISK models diabetes Hospital costs in the coming year eg PBRA

26 Authors: Bardsley M, Billings J, Dixon J, Georghiou T, Lewis GH, Steventon A (2011) Predicting who will use intensive social care: case finding tools based on linked health and social care data, Age and Ageing 40(2): October 2012 From: Predictive Models for Health and Social Care: A Feasibility Study

27 Information flows From: Predictive Models for Health and Social Care: A Feasibility Study

28 Data linkage Social & secondary care interface From: Predictive Models for Health and Social Care: A Feasibility Study

29 Which variables are important in pooled 1k model? From: Predictive Models for Health and Social Care: A Feasibility Study

30 Models using lower 1k thresholds From: Predictive Models for Health and Social Care: A Feasibility Study

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32 From models to tools A predictive risk tool has three parts: The model The software The data

33 From models to tools A predictive risk tool has three parts: The model Range of models is growing all the time from academic groups and from proprietary information tools Many models available for free Some PCTs developed their own The software Range of commercial companies offering complete tools + technical support in marshalling data. Models can be applied using a standard database package (Business Objects, SQL Server even MS Access) The data Some business intelligence packages now come with predictive risk modelling built-in Many PCTs have already created data-warehouses

34 Relative size of data sets collected For one WSD area Accident and emergency 350,000 records Outpatients 1,680,000 records Inpatients 360,000 records Social care 240,000 records Community matrons 20,000 records GPs 60 practices 48.5 million records March 2011

35 Using the data available

36 Testing for gaps in care

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38 Design and implementation Ref. Prof John Billings Model development limitations Predict risks of expensive things you think you can do something about Make sure your data base has most of the key risk factors Recognize the trade-offs between model accuracy and sensitivity Intervention design flaws Design the intervention after the risk model has been developed Use data from model development to help design the intervention Recognize you are probably going to need more information Intervention implementation flaws Roll it out in at least quasi-experimental mode Track dosage levels (who does what to whom and how) Avoid enrollment criteria leakage Evaluate impact of the intervention as rigorously as possible

39 Community matrons Very high risk High risk Moderate risk Low risk Top 0.5% 0.5% - 5% 5% - 20% 20% - 100% General population March 2011 Progetto e-health

40 GP Practice 1 GP Practice 2 GP Practice 3 GP Practice 4 GP Practice 5 GP Practice 6 GP Practice 7 GP Practice 8 Virtual Ward A Community Matron Nursing complement Health Visitor Ward Clerk Pharmacist Social Worker Physiotherapist Occupational Therapist Mental Health Link Voluntary Sector Link Virtual Ward B Community Matron Nursing complement Health Visitor Ward Clerk Pharmacist Social Worker Physiotherapist Occupational Therapist Mental Health Link Voluntary Sector Link Specialist Staff Specialist nurses Asthma Continence Heart Failure Palliative care team Alcohol service Dietician Progetto e-health

41 Telehealth and telecare Progetto e-health Images are the copyright of Tunstall Group Ltd

42 Distribution of Combined Model risk scores Importance of risk adjustment Very high risk High risk Moderate risk Low risk Top 0.5% 0.5% - 5% 5% - 20% Top 10% 10% - 45% 20% - 100% 45% - 85% 85% - 100% General population Telehealth trial participants March 2011 Progetto e-health

43 When can I expect to see a return on investment? (How accurate is the model? How effective is the intervention? How much does it cost?) Savings are linked to cost of intervention and its effectiveness Example: Average costs of readmission for high risk patients are ~ 1000 Intervention reduces readmission by 10% Then intervention has to cost less than 100 to save money Progetto e-health

44 Summary Range of predictive modelling tools have been developed Focus is on prioritising cases for preventive care with the expected benefits of reducing the increasing demand for emergency hospital care Models can span both health and social care Technical details of model performance is important the way the model is implemented Significant challenges in organising data offer additional benefits Range of applications not fully tested but so how is but linked data sets can From: Predictive Models for Health and Social Care: A Feasibility Study Progetto e-health

45 Sign-up for our newsletter Follow us on Twitter: Twitter.com/NuffieldTrust Insert presenter s address here Progetto e-health 16 October 2012

46 LA FRAGILITA : DAI MODELLI TEORICI ALLA VALUTAZIONE DELLE ESPERIENZE Ravenna, 12 ottobre 2012 Grazie per l attenzione! Hvala za va o pozornost! Dr Martin Bardsley Head of Research Nuffield Trust,London in collaborazione con /v sodelovanju z Progetto finanziato nell'ambito del Programma per la Cooperazione Transfrontaliera Italia-Slovenia , dal Fondo europeo di sviluppo regionale e dai fondi nazionali Projekt sofinanciran v okviru Programa ezmejnega sodelovanja Slovenija-Italija iz sredstev Evropskega sklada za regionalni razvoj in nacionalnih sredstev Ministero dell'economia e delle Finanze