This document summarizes a presentation on virtual wards, which are a form of case management that integrates health and social care. It describes predictive modeling to identify high-risk patients for virtual ward enrollment and the multidisciplinary staffing of virtual wards. Evaluations of virtual ward programs in different locations found they enrolled complex, high-cost patients but did not consistently reduce emergency admissions or costs in the short-term. Implementing large-scale service changes takes time and hospital use is not the only impact measure of these programs.

1. © Nuffield Trust01 May 2014
Analysis of Virtual Wards: a multidisciplinary
form of case management that integrates social
and health care
Martin Bardsley
Director of Research
Nuffield Trust
BGS Meeting. Acute care of Older people
Manchester Conference Centre. April 14 2014

2. © Nuffield Trust01 May 2014 © Nuffield Trust
Background

3. © Nuffield Trust
National Institute for Health Research (NIHR) report

4. © Nuffield Trust
10-year trend in emergency admissions (46 million admits)
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
2001/02Q1
2001/02Q2
2001/02Q3
2001/02Q4
2002/03Q1
2002/03Q2
2002/03Q3
2002/03Q4
2003/04Q1
2003/04Q2
2003/04Q3
2003/04Q4
2004/05Q1
2004/05Q2
2004/05Q3
2004/05Q4
2005/06Q1
2005/06Q2
2005/06Q3
2005/06Q4
2006/07Q1
2006/07Q2
2006/07Q3
2006/07Q4
2007/08Q1
2007/08Q2
2007/08Q3
2007/08Q4
2008/09Q1
2008/09Q2
2008/09Q3
2008/09Q4
2009/10Q1
2009/10Q2
2009/10Q3
2009/10Q4
2010/11Q1
2010/11Q2
2010/11Q3
2010/11Q4
Numberofemergencyadmissions
(millions)
No ACS diagnosis ACS primary diagnosis ACS secondary diagnosis
+35% (40%)
+34%

5. © Nuffield Trust
Interventions to reduce avoidable admissions
Primary Care ED Depts Hospital Transition
Practice features Assess/obs wards Structured
Discharge
Transition care
management
Medication review GPs in A&E Medication
Review
Rehabilitation
Case
management
Senior Clinician
Review
Specialist Clinics Self management
and education
Telemedicine Coordination end
of life care
Hospital at home
Virtual Wards
From Sarah Purdy 2013

6. Rationale for the virtual ward
Need to respond to growing needs of people with chronic health problems.
Emergency admissions have been rising for some time – undesirable for
patients and costly in terms of acute hospital care. No one explanation for
rise in emergency admissions – part patients factors, part health
systems.
Aim to develop approaches that are preventive before crises emerge.
Needed to identify patients at risk of future admissions.
Needed a linked process for managing high risk patients in community
settings.
Not clear what works see Purdy et al (2012) Interventions to Reduce Unplanned Hospital Admission:
A series of systematic reviews. Bristol University Final Report)

7. © Nuffield Trust
To prevent, we need to predict who will high costs in who in
the future
Predictive
models try to
identify
people here
Averagenumberof
emergencybeddays
…not the people
who are current
intensive users

8. © Nuffield Trust
Predictive modelling in the UK
Patterns in routine data identify high-
risk people next year.
Use pseudonymous, person-level
data.
Relies on exploiting existing
information:
+ve: systematic; not costly data
collections; fit into existing systems;
applied at population level
-ve: information collected may not be
predictive; data lags

9. © Nuffield Trust
Describing a model’s performance
A predictive risk
model tries to
sort it out
At the start of the year, no
one knows who’s who

10. © Nuffield Trust
Predictive modelling is only as effective as the intervention it
is used to trigger
Case Management
Intensive Disease Management
Less Intensive Disease
Management
Wellness Programmes
Top 0.5%
0.5 – 5.0%
6 - 20%
21 –
100%
Need to link the risk strata to the treatment/management options

11. © Nuffield Trust
Virtual Wards = Predictive Model
+
Hospital-at-Home

12. 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
Original Croydon
Model for Virtual
Wards

13. © Nuffield Trust
Lewis* described the following model of care known as 'virtual wards‘
(1 of 2)
• Each virtual ward is linked to a specific group of GP practices (so pop
c.30,000)
• A patient is offered "admission" to a virtual ward if a risk prediction tool
identifies him or her as being at high risk of a future emergency hospital
admission.
• Patients remain in the community and receive multidisciplinary in person at the
patient's home, by telephone and/or at a local clinic.
• Each virtual ward has a capacity for 100 patients, i.e. 100 “virtual beds” per
virtual ward. These are subdivided into five "daily" beds, 35 "weekly" beds and
60 "monthly" beds, reflecting the frequency with which different patients are
reviewed on a ward round.
• Virtual ward staff can move patients between different “beds" as the patients'
needs change.
*Lewis GH. Case study: virtual wards at Croydon Primary Care Trust. London: King’s Fund; 2006. Available from:
http://www.kingsfund.org.uk/search_clicks.rm?id=6746&destinationtype=2&instanceid=349684

14. © Nuffield Trust
Lewis* described the following model of care known as 'virtual wards‘
(2 of 2)
• Virtual ward staff discuss patients on office-based "ward rounds", participating
either in person or by telephone.
• Certain specialist staff (e.g. tissue viability nurse) may cover several virtual
wards.
• The virtual ward staff share a common medical record.
• Systems to alert local hospitals, emergency departments and out-of-hours
providers that a patient is on a virtual ward.
• When a patient has been assessed by all relevant virtual ward staff, and has
been cared for uneventfully for several months in the ‘monthly review’ section of
the ward, then the ward staff may feel that the patient is ready to be discharged
back to the care of the GP practice.
*Lewis GH. Case study: virtual wards at Croydon Primary Care Trust. London: King’s Fund; 2006. Available from:
http://www.kingsfund.org.uk/search_clicks.rm?id=6746&destinationtype=2&instanceid=349684

15. © Nuffield Trust
Adaptations and evaluations
Site Feature Evaluation
Croydon Nurse-led NIHR funded study
Wandsworth VWGPs NIHR funded study
Devon Practice-based NIHR funded study
New York Homeless RCT
Toronto Discharge Virtual Ward RCT
North Somerset Clinical referrals Local study
Plus increasing number of different models in UK and abroad
See also Chenore T, Pereira Gray DJ, Forrer J, Wright C., Evans PH, Emergency hospital admissions for the
elderly: insights from the Devon Predictive Model J Public Health (2013)

16. © Nuffield Trust
Outline of three schemes
Croydon Devon Wandsworth
Date first virtual ward
opened
May 2006 October 2008 March 2009
Number of virtual
wards under study
2 then 8 1 4
Funding Croydon PCT NHS Devon and Devon County
Council
Wandsworth PCT and
Wandsworth Council
Model Nursing led GP practice led GP led
Full-time staff Community matrons and ward
clerks
Community matron and ward clerk Community matron, virtual ward
GP, and ward clerk
Number of part-time
staff (wider
multidisciplinary team)
Initial “pilot” virtual wards
project: pharmacist,
physiotherapist, occupational
therapist, district nurses, health
visitor for older people,
representative of Croydon
Voluntary Action
After the initial pilot phase:
none
Social workers, community
psychiatric nurse (CPN), CPN for
older people, staff grade elderly
care doctor, physiotherapist,
occupational therapist, voluntary
sector representative, district
nurses, GP, complex care team
manager (joint health & social care
appointment)
Social worker, district nurse,
physical therapist, occupational
therapist, pharmacist, drug &
alcohol therapist.

17. © Nuffield Trust01 May 2014 © Nuffield Trust
Evaluation methods

18. © Nuffield Trust
Evaluation Methods
Three pilots sites with different models of virtual ward
Retrospective analysis of existing projects
Track cohort of specific patients to look at service use over time
Exploit existing data through secure data linkage
Compare change to matched control group (matched on multiple
variable using propensity and prognostic score)
Costing service activity and interventions

19. © Nuffield TrustFrom: Predictive Models for Health and Social Care: A Feasibility Study
Information flows

20. © Nuffield Trust
Health and social care timeline – an individual’s history

21. © Nuffield Trust
Matching health diagnoses categories in intervention and
control groups
0%
10%
20%
30%
40%
50%
60%
Control Intervention

22. © Nuffield Trust
Example: Marie Curie Home Nursing Service.
Comparing hospital admissions for retrospectively matched controls

23. © Nuffield Trust01 May 2014 © Nuffield Trust
Findings

24. © Nuffield Trust
Lengths of stay on the virtual wards

25. © Nuffield Trust
Costs of service use in the six months before starting on the
virtual ward according to risk band
-£1,000
£0
£1,000
£2,000
£3,000
£4,000
£5,000
£6,000
£7,000
1 2 3 4 5 6 7 8 9 10
CostPerpatient
Risk Strata
A&E Community Elective
Emergency GP Out Patients
Social Care

26. © Nuffield Trust
Virtual ward patients
The virtual ward patients in one site had
• a mean combined model score of 0.63 compared with score of 0.06 for the rest of the
population.
• a higher rate of emergency hospital admissions (2.64 per patient compared with 0.06).
• more general practice surgery visits (42.99 visits compared with 5.55).
• more contact with community nurses (68.6 per cent of virtual ward patients had been in
contact with community nurses in the year before receiving the intervention compared
with 1.0 per cent for the rest of the population).
• more chronic health problems 2.48 vs 0.07 conditions for the rest of the population.
• more social care services eg 19.3 per cent of virtual ward patients had received home
care at some point in the previous twelve months, compared with 0.5 per cent for the
rest of the population.

27. © Nuffield Trust
Changes in observed costs
Individual service use costs on the six months before and
after starting the intervention (n=989)
% with a cost
(pre or post)
Total Cost
Pre(£000s)
Avg Cost pp
pre(£)
% Total (pre)
Avg Cost pp
Post (£)
% total Cost
Posts
GP 92% 135 501 8.0% 538 9.0%
Community 62% 396 401 6.4% 837 14.0%
A&E 60% 748 136 2.2% 100 1.7%
Elective 26% 2,407 757 12.0% 504 8.4%
Emergency 55% 496 2,433 38.8% 1,867 31.1%
Out Patients 78% 555 561 8.9% 437 7.3%
Social Care 32% 1,473 1,489 23.7% 1,714 28.6%
Total 6,210 6,279 100.0% 5,996 100.0%

28. © Nuffield Trust
Matching process to create ‘controls’
Intervention
patients (n=1,231)
Potential controls
(n=2,083,830)
Matched controls
(n=1,231)
Mean age in years (SD) 71.23 (16.86) 39.83 (23.32) 71.61 (17.45)
Female 54.8 55.1 55.6
Mean socioeconomic
score (SD) *
24.06 (10.88) 24.58 (11.91) 25.51 (10.90)
Mean number of
chronic conditions (SD)
2.70 (1.66) 0.21 (0.66) 2.46 (1.58)
Mean predictive risk
(SD)
0.60 (0.25) 0.08 (0.09) 0.57 (0.24)
Angina 23.2 1.5 21.4
Asthma 20.6 2.8 19.6
Cancer 15.5 2.7 12.5
Cerebrovascular
disease
19.8 1.1 19.0
Congestive heart
failure
22.7 0.8 17.2
COPD 23.2 0.9 18.5
Diabetes 29.7 2.8 28.5
History of falls 24.5 2.2 25.9
History of injury 44.6 6.9 45.7
Matching based on:
Age, sex, ethnicity, IMD
Recorded diagnoses
Prior use
Predictive risk score

29. © Nuffield Trust
Patterns of hospital costs (pre/post)

30. © Nuffield Trust
Changes in hospital activity

31. © Nuffield Trust
Observations on impact on care use
Sample dominated by one site
Difficulties in matching to patients with complex health problems
(had to use national hospital based models)
No evidence of reductions in emergency admissions at six months
Indications of possible reductions in OP and elective care

32. © Nuffield Trust
Reality of implementation
In largest site the model ‘changed’
• from multidisciplinary case management to standard service delivered by a
community matron supported by an administrative assistant
• Predictive model not used consistently throughout
• organisational commitment and investment in preventive care for high risk patients
but local GPs seemed less visible
• Long lengths of stay linked with incentives to have 500 patients on virtual wards.
Large differences between sites in costs of virtual wards itself
Two sites still in early stages - and have subsequently developed

33. © Nuffield Trust
Impact of eight different interventions on hospital use

34. © Nuffield Trust
Cautions in evaluation……
1. Recognise that planning and implementing large scale service
changes take time
2. Define the service intervention clearly – and be clear when the model
is changed
3. If you want to demonstrate statistically significant change, size and
time matter
4. Hospital use and costs are not the only impact measures
5. Carefully consider the best models for evaluation –
prospective/retrospective; formative/summative; quant./qualitative

35. © Nuffield Trust
General observations
There were different 'forms' of virtual ward in this study and we suspect an even wider
number of variants in other settings.
Our analyses have shown how patients being cared for on virtual wards included some
people with serious complex illnesses that have important health service implications.
Virtual wards are part of a generic approach to long term care which may be justified in
other terms, for example as ways to improve the quality of communication between
community health staff, the continuity of care, patient experience or safety. No simple
solutions we can take off the shelf
Though the evidence was not conclusive, the differential levels of service use in high risk
patients suggested that these would provide more fertile ground for interventions aimed at
reducing hospital use.

36. © 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)

37. © Nuffield Trust01 May 2014
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