Greater Manchester CHC Care Pathways

Building Rapid Interventions to reduce antimicrobial resistance & over-prescribing of antibiotics (BRIT)

Potential for Scalability

Ownership – what level? CCGs, GP, STP, NHS Eng??

- Yes – but need the data

What services and knowledge is available

Communication

Feedback Points

1. Patient and industry education and support

2. Communication and continuity

3.

Group Members

Craig Wood - cwood@fdbhealth.com - Hearst Health

Graham Death - graham.death@dhaca.co.uk - Digital Health & Care Alliance

Tjeerd Van Staa - tjeerd.vanstaa@manchester.ac.uk - UoM

Opportunities

Pressure

(OptimiseRx)

Clinical decision support at the point of care

Patient compliance

Patient education

- Targetted

- Pre-consultation

Barriers

Education – Vicious cycle

Data

Fragmented

Time

Tariffs/funding – business model

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Greater Manchester CHC Care Pathways

Building Rapid Interventions to reduce antimicrobial resistance & over-prescribing of antibiotics (BRIT)

Potential for Scalability

Blockchain technology for dynamic permissions

Feedback Points

1. Open API across systems would enable wider opportunities for the project

2. Use incentives to GPs to reduce prescription rates supported by educational programme for GPs

3. More radically – re ove GP’s a ility to pres ri e a ti ioti s

Group Members

Jo Hobbs - georgina.hobbs@manchester.ac.uk - GM Connected Health Cities

Carmel Dickinson - carmel.dickinson@manchester.ac.uk - Mi

Mark Claydon - mark.claydon@trustech.nhs.uk - Trustech

Peter Jenkinson - peter@middleforthgreen.co.uk - Middleforth Green Consultin Ltd.

Tim Meehan - tim.meehan@horizonscitech.co.uk - Horizon SciTech

Peter Harrison - peter.harrison@nokia.com - Nokia Technologies

Opportunities

People more tech-savvy and so probably

more likely to engage in projects like this.

E.g. collecting own data on wearable apps.

Take patient generated data to health

professionals to upload and join their official

data.

Standardisation of API will improve the

potential for scalability

Barriers

Interoperability between systems

Bad press and association with care.data

Public perceptions

NHS gave google access to health data and

that caused public mistrust

Page 51 of 78

Page 52 of 78

Greater Manchester CHC Care Pathways

Building Rapid Interventions to reduce antimicrobial resistance & over-prescribing of antibiotics (BRIT)

Potential for Scalability

National problem – prescribing issues/expectations/data capture

Better use of community pharmacy

Identification of demographics/cohorts – need for targeted messages

- How could this be replicated nationally

Needs to be adaptable to different demographics e.g. age/ethnicity

- People in the care homes

Using the wider CHC network to learn/spread/disseminate information

- Cross pathway learning

Feedback Points

1. Change in behaviour and expectations – GPs and patients

2. Partnership opportunities – public and private – communications, testing, spin out

3. Connected data environment is essential – GM and beyond

Group Members

Ian McKenna - iMcKenna@galen-research.com - Galen Research

Catherine Headley - Catherine.Headley@manchester.ac.uk - UoM

Mike Burrows - mike.burrows@gmahsn.org - GMAHSN

Azad Dehghan - a.dehghan@manchester.ac.uk - UoM

Sarah Rikard - sarah.rickard@srft.nhs.uk - GM Stroke ODN

William Welfare - william.welfare@phe.gov.uk - Public Health England

Stephen Lee - steve.p.lee@philips.com - Phillips

Anna Jenkins - anna.jenkins@liverpool.ac.uk - Uni of Liverpool

Opportunities

Changing patient flow/footfall

Patient interface

Big data analytics

Measure effectiveness of medication

Harnessing industry expertise

Innovative use of social media in public

health campaigns

GP electronic systems provide good

interface

Improving captive systems in hospital

prescribing

Barriers

Changing GP behaviours (barriers create

opportunities)

Changing patient expectations decision

point - do you need to see GP

Investment general

Investment towards behaviour change and

expectations – patient & on NHS

Are findings generalisable

Page 53 of 78

Page 54 of 78

Greater Manchester CHC Care Pathways

Building Rapid Interventions to reduce antimicrobial resistance & over-prescribing of antibiotics (BRIT)

Potential for Scalability

Process should be common across economotion

Sta dardised data see hat’s orki g a d ot orki g

Interoperability (or integration with existing systems)

Driven by the CCG – funding, resource, analytics

Feedback Points

1. CCG Focus (collaboration)

2. Technology/standardisation

3. Collaboration

Group Members

Adrian Owen - Adrian.Owen@insource.co.uk - Insource

Paul Hanmer - paul.hanmer@cmft.nhs.uk - Trustech

Grant Churnin-Ritchie - grant.churnin-ritchie@sas.com - SAS

Matt Fairley - matthew.fairley@systemc.com - SystemC

Andy Jeans - andy.jeans@orcha.co.uk - Orca

John Farenden - john.farenden@mac.com - EY

Sarah Barnes - EY

Lewis Pickles - lewis.pickles@tiani-spirit.com - Tiani Spirit

Charlotte McCowley - TBC

Sam Aspinall - sam.aspinall@vitalpac.com - SystemC

Neil Walbran - neil.walbran@healthwatchmanchester.co.uk - Health Watch Manchester

Opportunities

Use of technology to address the barriers (Apps)

E.g. Hubs – reception, clinical/NSE Pract

Ne odels of care Thi k Differe tly

Registering arrivals, capture older clinical

info. Already available.

Organisations to work collaboratively

GP sites – staff busy

Point of care testing – triage/self manage

(sputum sample score)

Personalisation of feedback e.g. app,

email, text, call

Barriers

1. Better Triage and points of contact

better info for patients

Standardisation of triage approach

Triage does ’t ide tify se erity increase £

risk risk management

2. Support for GP – pressure (time)

Page 55 of 78

Page 56 of 78

Greater Manchester CHC Care Pathways

Building Rapid Interventions to reduce antimicrobial resistance & over-prescribing of antibiotics (BRIT)

Potential for Scalability

Fits with 5y forward – new models, feds, neighbourhoods

Needs strong leadership, change in culture and effective stewardship

Data/results to move

Consider multiple entry points v single access point

Needs public engagement

Feedback Points

1. P.O.C testing – feasible, effective, economic, acceptable

2. culture (clinicians/pts)

3. Changes in clinical pathway – need adopting, need accepting + using

Group Members

Stephen Melia - stephen.melia@manchester.ac.uk - UoM

Jane Macdonald - jane.macdonald@gmahsn.org - GMAHSN

Sarah Knowles - Sarah.Knowles@manchester.ac.uk - UoM

David Park - david.park@cisco.com - Cisco

Matthew Machin - matthew.machin@manchester.ac.uk - UoM

Chris Etchells - chris.etchells@kmswristband.com - KMS Solutions

Keli Shipley - keli.shipley@adi-uk.com - ADI Health

Opportunities

Prescribing by microbiology result

(Stewardship)

P.O.C testing?

CHC predictive data + P.O.C (confidence) +

‘ology results i a ti ely a er P.O.C multi-professional (pre-visit) i.e.

screening

Barriers

No one site testing

No scalable history

Need to communicate system effectively

Wide access to single pt. record

System adoption and individual GP

Page 57 of 78

Page 58 of 78

Greater Manchester CHC Care Pathways

Building Rapid Interventions to reduce antimicrobial resistance & over-prescribing of antibiotics (BRIT)

Potential for Scalability

Feedback Points

1. Smartphone/wearable tech variability in access to technology

2. Data visualisation

3.

Group Members

Ruth Norris - ruth.norris@manchester.ac.uk - UoM

Niels Peek - niels.peek@manchester.ac.uk - UoM

Kieran O'Malley - kieran.omalley@manchester.ac.uk - UoM

Gary Clarke - gary.clarke@manxtelecom.com - Manx Telecom

Paul Turner - p.turner@wigan.gov.uk - Wigan Council

Chris Hart - christopher.hart@astrazeneca.com 0 AstraZeneca

Steve Hilton - steve@libertyapps.co.uk - Liberty Apps

Lisa Bennet - Lisabennett.work@gmail.com - Quintiles

Opportunities

Personalised medicine

Visualising the data

Reliable/coordinated/useful

Breaking the cultural/learnt norms

(antibiotics) through public engagement

GP as a nexus for change in patient

Knowing the baseline for comparability

Barriers

Cultural norm – over-prescribing

NHS digital – datasets

Information overload

Tools to clinicians

Limitations of algorithms

Over-reliance of the information – false

belief

Page 59 of 78

Page 60 of 78

Greater Manchester CHC Care Pathways

Building Rapid Interventions to reduce antimicrobial resistance & over-prescribing of antibiotics (BRIT)

Potential for Scalability

Feedback Points

1. Who’s eha iour are e tryi g to ha ge (e d user/GP/health pra titioner)

2. Who is the driver (CCG/commissioner/pharmacist/consultant)

Definition of what a pathway is

3. Data governance (relevance) - transparency/interoperability

Group Members

Zoher Kapacee - zoher.kapacee@manchester.ac.uk - UoM

Zabeda Ali-Fogarty - zabeda@esp-it-consultancy.com - ESP IT Consultancy

Roger Wallhouse - roger@healthsys.com - Health System Solutions Ltd.

Reg Tabb - reginald.tabb@bms.com - Bristol-Myers Squibb Pharmaceuticals

John King - john.king@ethos-partnership.com - Ethos Partnership

Joanna Balderstone - joanna.balderstone@bms.com - Bristol-Meyers Squibb Pharmaceuticals

Adam Slawson - adam.slawson@fluxx.uk.com - Fluxx

Ken Hsu - ken.hsu@healthwatchmanchester.co.uk - Health Watch Manchester

Ben Waterhouse - bwaterhouse@caci.co.uk - CACI Ltd

Opportunities

Shared experiences

Definition of care pathways (knowledge)

Portal to allow patients to input symptoms

(decision tree)

Identify variability in antimicrobial

infections

Research into individual prescribing

practices

Knowledge bank specifically for patients

- Monitoring outcomes

Educating practitioners (convert know how

into defined process)

Set up a test GP – a place to test ideas and

collect data from real patients

Artificial intelligence

- ANI = now

- What about AGI and ASI – 30 – 45 years

from now

Barriers

Identify buyers (payer evidence)

True understanding of cost/benefit

Data governance – privacy/security

- How can data on an individual level be

used

Change in behaviour = practitioners and

patients

What are emotional nudges we can text? E.g.

? e.g.?

Who are the people along the prescription

journey and what are their journeys/needs?

Why are patients asking for antibiotics?

What’s the patie t/GPs e otio al jour ey?

Analysis into information delivery e.g. letters

Page 61 of 78

Page 62 of 78

Greater Manchester CHC Care Pathways

Building Rapid Interventions to reduce antimicrobial resistance & over-prescribing of antibiotics (BRIT)

Potential for Scalability

Self-monitoring

- Benchmarking peers

- Access to ring fenced appointments – coughs and colds – (capacity freed from reduced prescribing)

- Generates research data

Feedback Points

1. Need comprehensive data to see impact of problem moving through health system

- patient reported

- 1 °/2 °/social care/community

Group Members

Lisa Dutton - Lisa.Dutton@srft.nhs.uk - NIHR CLAHRC Greater Manchester

Rosemary McCann - rosemary.mccann@phe.gov.uk - Public Health England

William Dixon - Will.Dixon@manchester.ac.uk - UoM

Andrew Dodgson - andrew.dodgson@cmft.nhs.uk - Public Health England

Adrian Parry-Jones - adrian.parry-jones@manchester.ac.uk - UoM

Chris Ashton - christopher.ashton@srft.nhs.uk - GM Stroke ODN

Opportunities

Mass education e.g. viral disease

Understanding neutral (untreated) history.

Self-reported daily symptoms –

benchmarking

Breadth of data in CHC

- 1 °/2 °/social care/community

Barriers

Strap beliefs + traditions in clinical practice

Self-interest > greater good

Consultation time

Risk of shifting problem acute care e.g.

age

Concerns about missing infection and not

treating

Page 63 of 78

Page 64 of 78

Greater Manchester CHC Care Pathways

A Learning Health System for stroke care in Greater Manchester

Potential for Scalability

Algorithm built in to detect long term >130 mm/Hg systolic BP

Gameification – success rate

Use tech to plan and coordinate patient journey better

Lea a d uild o lesso s f o ig usi ess that is u i uitous i life to o st u t a ette e-2-e pathway –

etickets etc

Importance of linking data and patient interface i.e. pharmacy/secondary care appointments/social care

portal

Feedback Points

1. Retrospective review of all mimics should be 25% currently 50%

2. Transmission of data from Paramedic/NWAS to stroke

3. Collaboration with industry to develop app for workstream 2

Group Members

Ruth Norris - ruth.norris@manchester.ac.uk - UoM

Kieran O'Malley - kieran.omalley@manchester.ac.uk - UoM

Chris Hart - christopher.hart@astrazeneca.com - Astra Zeneca

Lisa Bennet - Lisabennett.work@gmail.com - Quintiles

Steve Hilton - steve@libertyapps.co.uk- Liberty Apps

Gary Clarke - gary.clarke@manxtelecom.com - Manx Telecom

Opportunities

Triage – Facial recognition software

Transmission of data

NWAS – stroke unit. Divert en-route if

necessary

Retrospective review of all stroke patients.

Comorbidities?

HER in ambulance

Barriers

Collecting FAST criteria

Feeling of being audited

Real time data transmission remote areas

Security/encryption

Time pressures/morbidity action

Cost

Page 65 of 78

Page 66 of 78

Greater Manchester CHC Care Pathways

A Learning Health System for stroke care in Greater Manchester

Potential for Scalability

Large potential to scale nationally

Feedback Points

1. Potential to extend to outside the hospital

2. Include to ambulance/GP/community

3. Information is stored as rapidly as possible

Group Members

Zabeda Ali-Fogarty - zabeda@esp-it-consultancy.com - ESP IT Consultancy

Roger Wallhouse - roger@healthsys.com - Healthcare Systems Solutions Ltd.

Paul Turner - p.turner@wigan.gov.uk - Wigan Council

John King - john.king@ethos-partnership.com - Ethos Partnership

Ken Hsu - ken.hsu@healthwatchmanchester.co.uk - Health Watch Manchester

Niels Peek - niels.peek@manchester.ac.uk -UoM

Opportunities

Protection and prevention e.g AF

Connecting from different locations in multi-

disciplinary environments

Getting the ambulance service involved

Emergency services training telemonitoring

Transfers

Barriers

Technologies – mobile tech variation

Behaviours – patient, - staff

Stakeholders for cost/investors/buyers

Adapting to changes

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Page 68 of 78

Greater Manchester CHC Care Pathways

A Learning Health System for stroke care in Greater Manchester

Potential for Scalability

Focus care on these people most at need via traffic light system

Feedback Points

1. Video triage in ambulance?

2. improved self-monitoring/adherence

3. Machine to machine data management traffic light system (for recurrence in primary care)

Group Members

Matthew Machin - matthew.machin@manchester.ac.uk - UoM

Stephen Melia - stephen.melia@manchester.ac.uk - UoM

Sarah Knowles - Sarah.Knowles@manchester.ac.uk - UoM

Christopher Etchells - chris.etchells@kmswristband.com - KSI Solutions

Keli Shipley - keli.shipley@adi-uk.com - ADI Health

David Park - david.park@cisco.com - Cisco Systems

Opportunities

Video triage in ambulance

Patient self-management/self-tests

Improve adherence using wearable tech etc

Link specialist/primary care data

Improve monitoring via tech – i.e. patients

at risk to reduce load on primary care

Barriers

Does it help diagnosis?

Time for professionals e.g. GPs

Nobody accountable for overall care

pathway

Page 69 of 78

Page 70 of 78

Greater Manchester CHC Care Pathways

A Learning Health System for stroke care in Greater Manchester

Potential for Scalability

Paramedics - Specialist advice centre

Feedback Points

1. Potential to extend to outside the hospital

2. Include to ambulance/GP/community

3. Information is stored as rapidly as possible

Group Members

Opportunities

Electronic transfer of information directly

into GP work flow (LPRES example)

Wearable technology

Monitoring apps – practice nurses

- Practice nurses

- Algorithms?

- Decision support

Telehealth e.g. Liverpool CCG

Family involvement

Careplan

Barriers

Stroke prevention - Poor sharing of

information between hospital and primary

care

Ambulance – poor decision support for

paramedics on scene: consequences of

getting it wrong

- Decision support

- Asking a clinician

- Doctors working differently – tariffs, job

plans, availability

- Paramedic – specialist advice centre who

have patient data, video, vital signs,

decision, health records

–

Page 71 of 78

Page 72 of 78

Greater Manchester CHC Care Pathways

A Learning Health System for stroke care in Greater Manchester

Potential for Scalability

Use of technology to assist decision making in ambulance service beyond stroke

Use of interventions beyond GM

Feedback Points

1. Use of technology: patients, paramedics – streamline processed – improve outcomes, and hospitals

PREDICTION

2. Changing culture and behaviour

3. Accessing data from diverse datasets

Group Members

Ian McKenna - iMcKenna@galen-research.com - Galen Research

Catherine Headley - Catherine.Headley@manchester.ac.uk – UoM

Mike Burrows - mike.burrows@gmahsn.org – GMAHSN

Azad Dehghan - a.dehghan@manchester.ac.uk – UoM

Sarah Rikard - sarah.rickard@srft.nhs.uk - GM Stroke ODN

William Welfare - william.welfare@phe.gov.uk - Public Health England

Stephen Lee - steve.p.lee@philips.com – Phillips

Anna Jenkins - anna.jenkins@liverpool.ac.uk - Uni of Liverpool

Opportunities

1. - Training + understanding

- technology used to feedback from

paramedic to expert

- using other things than FAST

- use of a tablet/app to record information

2. – Cultural change with approach to

intervention

- Flow of information between departments

+ decision points/makers

3. – Blood pressure management – overlap

with many other conditions

- use of wearables in high risk groups –

warning system algorithm, - large benefits

for relatively small group

Barriers

Accessing and using data from diverse range of

sources

1. How ambulance crew operate

Changing behaviours – paramedics, -

patients

2. Cultural assumption that there will be a

poor outcome

- diverse sources of data – ambulance,

A&E, stroke unit, neurosurgery

3. Timescales for flow of information and

action within 1 month risk period of

recurrence

Page 73 of 78

Page 74 of 78

Greater Manchester CHC Care Pathways

A Learning Health System for stroke care in Greater Manchester

Potential for Scalability

Missing people

False -

False +

Acute in they’re in the right place

Feedback Points

1.

2.

3.

Group Members

Opportunities

Decision support to identify ~genuine” strokes

Point of care testing? In ambulance better

communication

Machine learning to identify exceptions

Barriers

Page 75 of 78

Page 76 of 78

Greater Manchester CHC Care Pathways

A Learning Health System for stroke care in Greater Manchester

Potential for Scalability

Patient empowerment; apps to monitor and record BP – integrate this into NHS primary and secondary care

record

Test bed of NHS or patients/prototyping, stroke association app

Cross learning between RA and CVA

Feedback Points

1. Develop a repository for patient derived data that can be accessed by clinical staff with as low a barrier as possible

2. Set a vision

Group Members

Chris Ashton - christopher.ashton@srft.nhs.uk - GM Stroke ODN

Lisa Dutton - Lisa.Dutton@srft.nhs.uk -NIHR CLAHRC Greater Manchester

Adam Slawson - adam.slawson@fluxx.uk.com - Fluxx

Adrian Parry-Jones - adrian.parry-jones@manchester.ac.uk - UoM

Graham DeAth - graham.death@ethos-partnership.com- Ethos Partnership

Rosemary McCann - rosemary.mccann@phe.gov.uk - Public Health England.

Opportunities

Apps already available

Patient and priorities – fatigue

RTW; emotional support

Motivate people – BP: AF

Impact of AI in next 25 years

Setting an ideal for the experience of

pathway

Evaluate interventions at each stage of

pathway and how each affects outcome

Simulation solutions population level

Barriers

Inability to integrate data from monitoring

app in ESR

Changing culture of clinicians and NHS

system

Busyness versus Business

Patient preference for local hospital

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