The Role of Virtual Wards in Reducing Unplanned Admissions Maggie Ioannou MSc BA RGN RSCN RHV Director of Community Health Services Croydon PCT

Identifying Patients Key to success is in accurate identification of patients Clinician referrals do not work Threshold modelling (e.g. all patients aged >65 with 2+ admissions) do not work

Regression to the mean Average number of emergency bed days - 5 - 4 50 45 40 35 30 Average number of emergency bed days 25 20 15 10 5 Intense year - 5 - 4 - 3 - 2 - 1 + 1 + 2 + 3 + 4

Emerging Risk Average number of emergency bed days - 1 + 1 + 2 + 3 + 4 50 45 40 35 30 25 Average number of emergency bed days 20 15 10 5 - 1 + 1 + 2 + 3 + 4 - 5 - 4 - 3 - 2 Intense year

Predictive Risk Modelling Kaiser Permanente and other US providers have been using this method successfully for 20 years Their algorithms are proprietorial NHS commissioned its own algorithms which can be downloaded free of charge by PCTs

Intervention A&E data GP Practice data In-patient data   A&E data GP Practice data In-patient data Social Services data Intervention Combined Model Out-patient data

Intervention

10 Croydon Virtual Wards Croydon population = 340,000 10 virtual wards Catchment population of 34,000 residents per ward One ward per 15 GPs 100 “beds” per ward

Virtual Wards Mimic hospital ward Patients cared for in their own homes No physical ward building, hence the term virtual wards Patients case managed by multidisciplinary team Ward Team headed by Community Matron

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

Medical Input Community matron given the bypass telephone number to the duty doctor at each of the constituent GP practices Community matron able to book appointments to see the patient’s usual doctor

CICS “Weekly” 35 Patients “Daily” 5 Patients “Monthly” 60 Patients Croydon Intermediate Care Service Expert Patients’ Programme “Weekly” 35 Patients “Daily” 5 Patients “Monthly” 60 Patients Discharge

100 patients per ward “Weekly” 35 Patients “Monthly” 60 Patients “Daily” 5 Patients 5 (35  5) (60  20) = 5 + 7 + 3 = 15 patients for discussion each day

PREDICTED 0 PARR Score 98 100 OBSERVED 0 PARR Score 98 100 Admissions

Key Strengths Patients identified according to predicted need thereby reduces health inequalities and counters the inverse-care law Multidisciplinary, multi-sector partnership Eliminates duplication Patient-focused Simple intervention that is being adopted across the UK

Lessons learnt so far 1 Wards must make sense to primary care teams Takes time to integrate social care Issues of confidentiality must be faced early Impact across whole system is dynamic – in particular community nursing Takes time to keep acute trust on board and not antagonistic

More lessons Do not underestimate change management demands Trying to map savings across HRG groups is very complicated Important to remember that savings are whole system not attributable to one intervention Public relations crucial The price of winning awards!

What makes the partnership work Genuine trust and respect Shared vision that unplanned admissions are frequently avoidable Communication at all levels Sharing success; creating solutions together Facing the difficult issues in an open manner – brush nothing under the carpet Leadership “Can do” environment