Approaches to case finding: models and application

Managing referral rates and reducing admissions

Outer north east London have implemented two risk based approaches to case finding in order to reduce hospital admissions and referrals

Introduction

• Clinical risk targeting Population

• Disease risk analytics Disease

•Clinical risk targeting Population

Laura Osborn Planning and Delivery Project Manager NHS North East London and the City

Risk stratification • Identifies those most at risk of emergency admission - top 1% risk

Partnership working •Between the GP practice, Social services and provider services.

Avoid duplication •Services, resources and patient contacts

Proactive management •Long term conditions and social needs

Prevents avoidable hospital admissions •Robust planned care and patient education in the community

Outline and aims

Identify Service User

Case Conference Care Plan

Care Delivery Care Plan Review

Ongoing Care

Onward Referral

Self Management

Fortnightly cluster meetings held. Attended by core team Meetings approximately 45 minutes. 2-3 new patients plus other existing patients discussed.

Team agrees action plan for each patient

Patient is provided with information on what to do in case of emergency Care plan shared with MDT and

discussed at next meeting. The MDT team reviews the care plan and agrees if other measures need to be put in place to prevent the admission. The team risk rates the patient and agrees a follow-up period.

Community Matrons undertake an assessment of the patient . Members of the MDT provides patient with the necessary care to prevent admission. Liaison officer follows through with patient and MDT to ensure service is provided.

Patient referred to supporting services in the community

Patient kept on the register for a period of 6 months for on-going care.

Health Analytics used to identify top 1% of patients at risk. Clinical judgement used to supplement the risk stratification tool.

5

Integrated care model of care

Service User

GP

Community Matron

Social Worker

District Nurse

Practice Nurse

(Optional)

Care liaison officer

Therapies

Acute care specialists

End of Life Mental health

Third Sector

Drug & Alcohol services

The integrated care team

• Combined Predictive Model

Use Health Analytics

• Integrated electronic solution for patient care information

Commissioning tool

• Integrates care data from any source • Financial and clinical data

Multi functional tool

Risk profiling for integrated care

Segment by :

• Risk score • Age • Emergency admissions and attendance • Cost (primary and secondary) • Specific long term conditions

Role-based access

Electronic care plan functionality

Functionality

Practice List SizeNumber of

patients in HA top 1%

Practice 1 7798 167Practice 2 7425 122Practice 3 3246 54Practice 4 3686 61Practice 5 5103 97Practice 5 5200 82Practice 6 4348 47

Practice 7 12498 204Practice 8 10378 152Practice 9 4724 46Practice 10 6394 39Practice 11 4222 38Practice 12 3082 56Practice 13 2748 26

Clus

ter 1

Clus

ter 2

The data from Health analytics enabled us to group the practices into the ‘clusters’ depending on location & number of high risk patients in the cohort. This also allowed us to work with community providers and social care to begin work aligning the teams

Risk profiling for Integrated Care: Modelling the clusters

Risk profiling for integrated care: Selecting the cohort

Modelling indicates that

90% of these will have one or

more LTC

Identify top 1% risk segment –

4239 in Redbridge

Reviewed by Integrated Care

team – accepted if suitable

These people accepted into Integrated Care will then be discussed by the team and a care plan will be developed across both health

and social care

Within the top 1% there is a significant variance in risk scores – we are able to sort the patients in order of risk score to ensure the highest risk patients are considered first for case management

Top 10 highest risk scores in the top 1% (Average Emergency Admissions 6.4)

Bottom 10 risk scores in the top 1% (Average Emergency Admissions 0.1)

Identifying the highest risk patients

Over 1300 patients with MDT care plans in place

132 GP practices, 3 local authorities, 2 acute trusts and

1 community provider delivering the model of care

Improved co-ordinated care by multi-disciplinary teams

and reduced duplication

Every patient has a nominated and dedicated liaison

officer to coordinate personalised care

Rapid access to social care as needed through direct

referral to social care

Co-location of health and social care teams in B&D and

Redbridge building “high trust” partnership teams

Outcomes

•Disease risk analytics Disease

Robert Meaker Associate Director for Commissioning Support and Innovation NHS North East London and the City

Chronic Obstructive Pulmonary Disease (COPD)

Direct healthcare cost of over

£950,000,000 1 Indirect costs of £1,300,000,000 1

2 year cost of COPD in Barking &

Dagenham £5.5 million 2

affects around 4% of the adult

population. 1 Highest costing individual with COPD over 2 years £50,299 2

Severe mean, 10 care visits £8,000 p.p. 1

10 % of emergency admissions 1 14 % admitted patients die 1

35 % are readmitted within 90 days 1

1 Source European respiratory Society 2

Source ONEL business intelligence

Why Chronic Obstructive Pulmonary Disease ?

Practice 1 Practice 2

Practice 3

Practice 4

Practice 5 Practice 6

Practice 7

Practice 8

Practice 9

Practice 10

Practice 11

High Cost –Secondary Care Use

• Defining quality “Risk factors” – NICE Quality Standards for COPD

• Measuring Quality= Health Analytics data extraction system installed in each surgery

• Education programme at multiple levels – offering support where needed and wanted

• Empowering patients

Intention

Establish and monitor a set of 7 core areas for patient care, within primary care. 1) Post bronchodilator spirometry

2) Severity Measurement

3) Annual review

4) Smoking cessation

5) Pulmonary rehabilitation

6) Self management plan

7) Palliative care

The Health Analytics tool, identified a 10 fold baseline variation between practices on many quality measures

Identification of Interventions

Interventions to reduce risk (Quality)

0102030405060708090

100

SpirometryConfirmedDiagnosis

Self ManagementPlan Issued

Ref. PR

PrePost

Key Indicators Pre and Post Intervention 45 Practices with 2788 Registered COPD Patients

Impact of Interventions

Impact on COPD Admissions

COPD admissions showing sub analysis by patients known and not known to GP with a diagnosis of COPD within : Barking and Dagenham

479 461 470 479

499 519

540 545 561 584

610 641 651 658 656 657 647

681 690 684 646

599

479 453 632 608 562 534 483 464 664 623 618 617 604 583 562 543 528 514 503 412 398 393 300

600

1200

1/1/2010

1/3/2010

1/4/2010

1/6/2010

1/9/2010

1/11/2010

31/1/2011

1/3/2011

1/4/2011

16/6/2011

2/7/2011

4/8/2011

1/9/2011

8/10/2011

19/11/2011

11/12/2011

21/1/2012

1/2/2012

3/3/2012

8/4/2012

19/5/2012

9/6/2012

Number of patients not diagnosed with COPD by GP, having a COPD related IP admission (any type) in the last 12 months

Number of patients not diagnosed with COPD by GP, having a COPD related IP admission (any type) in the last 12 months

Total number of COPD related IP admissions (any type) in the last 12 months

Send each COPD patient a score card containing a report on the core primary care interventions

Promote improvement through patients

Patient empowerment

VIDEO

Intervention stage 2

• Define Patient Risk factors “Quality Care” • Measure Quality Care • Multi Level Educational Intervention

• Data reliability critical • Massive Practice Variation • Huge Learning need • Work from within • You can make a difference (and quickly)

Learning