Martin Bardsley: analysis of virtual wards


Published on

Published in: Health & Medicine
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Martin Bardsley: analysis of virtual wards

  1. 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. 2. © Nuffield Trust01 May 2014 © Nuffield Trust Background
  3. 3. © Nuffield Trust National Institute for Health Research (NIHR) report
  4. 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. 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. 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. 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. 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. 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. 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. 11. © Nuffield Trust Virtual Wards = Predictive Model + Hospital-at-Home
  12. 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. 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:
  14. 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:
  15. 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. 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. 17. © Nuffield Trust01 May 2014 © Nuffield Trust Evaluation methods
  18. 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. 19. © Nuffield TrustFrom: Predictive Models for Health and Social Care: A Feasibility Study Information flows
  20. 20. © Nuffield Trust Health and social care timeline – an individual’s history
  21. 21. © Nuffield Trust Matching health diagnoses categories in intervention and control groups 0% 10% 20% 30% 40% 50% 60% Control Intervention
  22. 22. © Nuffield Trust Example: Marie Curie Home Nursing Service. Comparing hospital admissions for retrospectively matched controls
  23. 23. © Nuffield Trust01 May 2014 © Nuffield Trust Findings
  24. 24. © Nuffield Trust Lengths of stay on the virtual wards
  25. 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. 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. 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. 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. 29. © Nuffield Trust Patterns of hospital costs (pre/post)
  30. 30. © Nuffield Trust Changes in hospital activity
  31. 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. 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. 33. © Nuffield Trust Impact of eight different interventions on hospital use
  34. 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. 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. 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. 37. © Nuffield Trust01 May 2014 Sign-up for our newsletter Follow us on Twitter ( © Nuffield Trust