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© Nuffield Trust 15 March 2012 Analysis of Virtual Wards: a multidisciplinary form of case management that integrates social and health care Geraint Lewis FRCP FFPH Senior Fellow Nuffield Trust Co-authors: Martin Bardsley, Jennifer Dixon, John Billings and Rhema Vaithianathan
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© Nuffield Trust 15 March 2012

Analysis of Virtual Wards: a multidisciplinary

form of case management that integrates social

and health care

Geraint Lewis FRCP FFPH

Senior Fellow

Nuffield Trust

Co-authors: Martin Bardsley, Jennifer Dixon, John Billings and Rhema Vaithianathan

© Nuffield Trust 15 March 2012 © Nuffield Trust

Background

© Nuffield Trust

Why research this topic?

Challenge

Ageing population

Rising prevalence of chronic disease

Financial challenge

Opportunity

5% of patients account for 49% of emergency bed days

Expensive

Undesirable

Potentially avoidable

Virtual Wards = Predictive Model

+

Hospital-at-Home

© Nuffield Trust

Historical Context

BMJ in paper* in 2002 showed Kaiser Permanente in California seemed to provide higher quality healthcare than the NHS at a lower cost

*Getting more for their dollar: a comparison of the NHS with California's Kaiser Permanente BMJ 2002;324:135-143

Kaiser identify high risk people in their population and manage them intensively to avoid admissions

Inaccurate Approaches:

Clinician referrals

Threshold approach (e.g. all patients aged >65 with 2+ admissions)

© Nuffield Trust

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- 5 - 4 - 3 - 2 - 1 Intense

year + 1 + 2 + 3 + 4

© Nuffield Trust

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e year + 1 + 2 + 3 + 4

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Kaiser Pyramid

Pyramid

represents the

distribution of

risk across the

population

Small numbers of

people at very

high risk

Large

numbers of

people at

low risk

© Nuffield Trust

Inpatient

data

A&E data GP

Practice

data

Outpatient

data PARR Combined

Model

Census

data

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£500

£1,000

£1,500

£2,000

£2,500

£3,000

£3,500

£4,000

£4,500

0 10 20 30 40 50 60 70 80 90

Predicted Risk (centile rank)

Actu

al

Avera

ge c

os

t p

er

pa

tien

t

Predictive accuracy of the Combined Model

© Nuffield Trust

Virtual Ward B Community Matron

Nursing complement

Health Visitor

Ward Clerk

Pharmacist

Social Worker

Physiotherapist

Occupational Therapist

Mental Health Link

Voluntary Sector Link

Virtual Ward A 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

GP Practice 1

GP Practice 2

GP Practice 3

GP Practice 5

GP Practice 4

GP Practice 6

GP Practice 7

GP Practice 8

© Nuffield Trust

100 patients per virtual ward

“Weekly”

35

Patients

“Monthly”

60 Patients

5 (35 5) (60 20)

= 5 + 7 + 3

= 15 patients for discussion each day

“Daily”

5 Patients

© Nuffield Trust

Admission

• Predictive model only

• Consent

• Electronic notes

© Nuffield Trust

Initial assessment at home

© Nuffield Trust

Ward rounds

20 minutes

PCT offices or GP practice meeting rooms

Tele-conferencing facility

© Nuffield Trust

Discharge

NIHR Management Fellow project

Options:

• Never discharge

• Fixed duration

• Clinical opinion

• Fixed goals

• Predictive score

• Front burner/back burner

© Nuffield Trust

Adaptations and Evaluations

Site Feature Evaluation

Croydon Nurse-led

Wandsworth VWGPs

Devon Practice-based

New York Homeless

Toronto Discharge Virtual Ward

North Somerset Clinical referrals

© Nuffield Trust

Adaptations and Evaluations

Site Feature Evaluation

Croydon Nurse-led Current study

Wandsworth VWGPs Current study

Devon Practice-based Current study

New York Homeless RCT

Toronto Discharge Virtual Ward RCT

North Somerset Clinical referrals Formative study

© Nuffield Trust

Evaluation Methods

Unreliable Reliable

Pre/Post comparison .

Area-based analyses Regression discontinuity

analysis

Clinical opinion .

© Nuffield Trust

Evaluation Methods

Unreliable Reliable

Pre/Post comparison Prognostic score matching

Area-based analyses Regression discontinuity

analysis

Clinical opinion Randomised controlled trial

© Nuffield Trust 15 March 2012 © Nuffield Trust

Pre/Post Analysis

© Nuffield Trust

Evaluation of POPPs

5

© Nuffield Trust 15 March 2012 © Nuffield Trust

Propensity Score Matching

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Intervention

Start of intervention

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Intervention

Start of intervention

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Intervention

Start of intervention

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Month

Control Intervention

Start of intervention

© Nuffield Trust 15 March 2012 © Nuffield Trust

Preliminary Descriptive

Findings

© Nuffield Trust

Research Project Overview

Start date: May 2010

Publication date: May 2012 (estimated)

Project Components

1. Permissions and data collection

2. Propensity Score Matching

Local

Matched areas

3. Economic evaluation

4. Synthesis

© Nuffield Trust

Research Protocol

Lewis et al. (2011)

International Journal of

Integrated Care, 11(2)

© Nuffield Trust

© Nuffield Trust

Performance of HES data linkage

March 2011

1727

122 228

1725

122 222

1656

114 219

Croydon Devon Wandsworth

Records received

Records linked to HES

Unique patients linked to HES

© Nuffield Trust

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Jan 2006

Jul 2006

Jan 2007

Jul 2007

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Jul 2009

Jan 2010

Jul 2010

Croydon

Devon

Wandsworth

Admission dates to Virtual Wards (analysis based on patients linked to HES)

March 2011

Are start dates accurate?

Did the definition change?

© Nuffield Trust

Age of virtual ward patients (analysis based on patients linked to HES)

March 2011

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5%

10%

15%

20%

25%

30%

35%

40%

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Under 20

20 - 29 30 - 39 40 - 49 50 - 59 60 - 69 70 - 79 80 - 89 90 - 99 Over 100

Pro

po

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f p

art

icip

an

ts

Croydon Devon Wandsworth

© Nuffield Trust

Diagnoses recorded in inpatient data sets (analysis based on patients linked to HES)

March 2011

0%

10%

20%

30%

40%

50% Croydon (N=1656) Devon (N=114) Wandsworth (N=219)

© Nuffield Trust

Emergency hospital admissions per person per quarter (Note: no controls yet)

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Croydon (N=1394) Devon (N=72) Wandsworth (N=71)

Date of admission to VW

Analysis based on patients linked to HES admitted before Sep 09

© Nuffield Trust

Elective hospital admissions per person per quarter (Note: no controls yet)

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Croydon (N=1394) Devon (N=72) Wandsworth (N=71)

Date of admission to VW

Analysis based on patients linked to HES admitted before Sep 09

© Nuffield Trust

Outpatient attendances per person per quarter (Note: no controls yet)

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Croydon (N=1394) Devon (N=72) Wandsworth (N=71)

Date of admission to VW

Analysis based on patients linked to HES admitted before Sep 09

© Nuffield Trust

Economic Analysis

Aims:

Calculate the direct costs of the VWs in each site

Perspectives of the NHS and the local authority.

If the intervention is effective, calculate the cost per averted hospital

admission

© Nuffield Trust

Economic Analysis: Step 1

Establish the cost of routine care

• Use data on the utilization of health and social care services,

including GP visits, hospital costs (inpatient, outpatient and A&E) and

council-funded social care.

• Calculate unit costs as the actual costs to the NHS (hospital tariff, GP

hourly rates etc.) and social care (PSSRU unit costs)

© Nuffield Trust

Economic Analysis: Step 2

Establish the cost of the intervention

• Financial data

• Validated with staff interviews, questionnaires and work diaries

Costs considered:

Staff costs

Travel costs

Land, computers and fixed capital costs

Management costs

Ignore:

Pharmaceutical

Laboratory costs

© Nuffield Trust 15 March 2012 © Nuffield Trust

What next?

© Nuffield Trust

Prognostic controls

Local Controls

• Hospital data (inpatient, outpatient, A&E)

• GP data

• Community health services data

• Social care data

National Controls (matched areas)

• Hospital data (HES)

© Nuffield Trust

What next?

Planned matched control areas VW site Comparison areas

Croydon Enfield Waltham Forest Greenwich

Teaching

Redbridge

Devon* Somerset Cornwall & Isles

of Scilly

Shropshire

County

Herefordshire

Wandsworth Hammersmith &

Fulham

Camden Islington Westminster

* Dorset PCT and North Yorkshire &York PCT were excluded because they had virtual

ward schemes or equivalent during the comparison period

© Nuffield Trust 15 March 2012 © Nuffield Trust

Improving Efficiency

© Nuffield Trust

Predictive

Risk Model Impactibility

Model

Whole Population

People at Risk

People at

Risk who

will benefit

© Nuffield Trust

Approaches to Impactibility Modelling

Approach to Impactibility

Modelling

Efficiency Equity

Prioritise patients with ACS

conditions

Prioritise patients with high

“gap scores”

Exclude “difficult” patients

© Nuffield Trust

Improving efficiency of the intervention

• Configuration software

• Lean methods

© Nuffield Trust 15 March 2012 © Nuffield Trust

Final Word: Roemer

© Nuffield Trust

Acknowledgements

This work was funded by the National Institute for Health Research (NIHR) Service Delivery and Organisation (SDO) programme. Project number 09/1816/1021.

The views and opinions expressed here are those of the authors and do not necessarily reflect those of the NIHR SDO programme or the Department of Health.

We are grateful to the support and guidance of staff in our three study sites, and in particular our site representatives:

• Paul Lovell (Devon)

• David Osborne (Croydon)

• Seth Rankin (Wandsworth)

© Nuffield Trust 15 March 2012

www.nuffieldtrust.org.uk

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© Nuffield Trust

[email protected]

© Nuffield Trust 15 March 2012 © Nuffield Trust

Appendices

© Nuffield Trust

Regression discontinuity analysis

90 Predictive risk score

Un

pla

nn

ed

ad

mis

sio

n r

ate

© Nuffield Trust

Outline

Background

Predictive modelling

Virtual wards

Research methods

Propensity score matching

Economic analysis

Preliminary findings

Next Steps

© Nuffield Trust

Why do this twice?

Local controls (SUS/GP/Social Care/Community services):

Can match on characteristics from all data sets

Can examine impact on use of wider range of services

Consistent coding from providers of care

But:

If intervention widespread, scope to find controls may be limited

We might not link every participant to routine data

May be spill over effects from intervention

© Nuffield Trust

Why do this twice?

Controls from matched areas (HES):

More plentiful source of controls

Comparison with other benchmarks of “usual care”

Less susceptible to incomplete data about participants

But:

Restricted in focus to hospital use (for matching and metrics)

Unexplained variation in hospital use around England (use of matched control areas helps to an extent)

Variation in coding practices by providers

© Nuffield Trust

What next?

Building local predictive models for each month

0

50

100

150

200

250

300

Jan 2006

Jul 2006

Jan 2007

Jul 2007

Jan 2008

Jul 2008

Jan 2009

Jul 2009

Jan 2010

Jul 2010

Croydon

Devon

Wandsworth

... To predict 12

months ahead

Predictor variables taken from two previous

years....

© Nuffield Trust

What next?

Selecting controls Should be balanced on:

• Age, sex, ethnicity, area-level deprivation score

• Recorded diagnoses of health conditions (social care needs?)

• Prior use (inpatient, outpatient, A&E, primary care, social care)

• Predictive risk score

Problem: this could be very numerically intensive

• Number of participants = ~1,800

• Number of potential controls = 3 million (matched control areas)

• Number of distinct categories of individuals =

(10 age bands) x (2 sexes) x (5 ethnic categories) x (15 diagnosis

categories) x ..... = lots!

© Nuffield Trust

What next?

Selecting controls Matching techniques :

• Propensity score matching: Rosenbaum and Rubin (1983)

• Model probability that an individual receives treatment (p-score)

• Match on this single score rather than on individual variables

• Prognostic (“predictive risk”) score matching: Hansen (2008)

• Model probability of experiencing outcome (predictive risk score)

• Inverse probability weighting: Hernán et al (2000)

• Use inverse probability of treatment in weighted regression

• Genetic matching: Diamond and Sekhon (2005)

• Match on individual variables using numerical optimisation routine


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