demand & capacity

0

Demand & CapacityExecutive Summaries

1

System case for change

Fragmented commissioning landscape under financial pressure with limited integrated plans• 5 CCGs, 2 Local Authorities, leading to lack of coordinated and strategic commissioning for Norfolk

• Now moving in the right direction (e.g. joint governance, establishment of neighbourhoods, etc.), but starting

from behind, with West Norfolk under particular financial and operational pressures

• STP facing ~£95m in-year deficit for FY18/19 (approx. ~£45m deterioration from previous year)

• All physical acute trusts are in deficit

Primary Care is under increasing pressure with increasing demand and decreasing physicians• PC services have 9% excess demand. This has downstream impacts (e.g., 10% inappropriate A&E attendances)

• This issue is set to deteriorate with GP workforce declining by 1% pa

• This is a national issue with NHS-E and the GP Forward View both advocating new models of primary care

• However maturity of PCN based models within the STP are low with none yet to report completing Stage 1

Clinically disadvantaged and fragmented acute footprint• All hospitals see high volumes of non-elective work (partly driven by high EEAST conveyances)

• QEH faces an abnormally high temporary staffing burden

• NNUH is carrying a significant PFI cost contributing to a structural financial deficit

• QE and NNUH currently in special measures, and are rated inadequate by the CQC

Insufficient community and social care support with high numbers of MFFDs in acutes• 49 at NNUH, 57 at JPUH and 61 at QEH (although ~40% at QEH are out of area placements)

• Bed blockage is a particular problem at JPUH and QEH, with patients staying on average ~8 additional days

after being declared MFFD

• If ~85% of MFFD patients could be moved to a community setting, ~43.5K acute bed days could be freed (~7%

of total IP bed day)

Senior leadership focused on operational imperatives rather than long term strategy…• … which has made it hard for key clinicians and managers to drive transformational change at pace

2

Executive summary | System view

There are demand and capacity mismatches across the system today which, given forecasted

growth, could result in a ~500 bed deficit by 2023 in a "Do nothing" scenario• Primary care is under pressure, with ~9% of unmet demand compounded by a decreasing GP workforce

• All acutes are at or over capacity today

• Non-elective demand is growing between 4-8% and will fill available elective capacity within 2-3 years

• Capacity across NCHC and NCC is not enough to meet demand, resulting in acute bed blockages (~170

medically fit for discharge patients are sitting at any one time across the acutes)

• "Do nothing" scenario forecasts the STP to run a ~£140m deficit by FY22/23 taking into account future income

& demand growth across the system

The current system issues cannot be addressed by any single provider. Collectively

interventions across the system could create a sustainable position today• Primary care interventions could reduce A&E attendances by 20%, emergency admissions by ~3% and OP

attendances by 10%, delivering ~£5m of benefit

• Increasing intermediate care capacity to shift medically fit for discharge patients could free up ~130 beds in

the acutes (~7% of overall acute bed days), with a net benefit of £13M. This influences acute length of stay

• Reducing length of stay variation across acutes could drive an additional 1% bed day reduction from reducing

elective length of stay. This equates to a 20 bed impact across the system

• Further benefits of up to £36M could be realized from broader integration & standardization across acute

sites, and it is likely that additional cost opportunities at other system providers also exists

• With all components combined, a total of ~180 acute beds could be freed today, although ~130 beds or bed

equivalents would be transferred into the community

• Further system integration could increase total cost opportunity to 10-15% savings against today's acute cost

base (incremental £50-99m) which return the STP in a financially sustainable position

However, given forecast growth, there will be shortfall of ~140 beds by FY22/23 despite

interventions. This means further capacity new models of care delivery are required• QEH forecasted to require an additional 13 beds, JPUH an additional 22 beds, NNUH an additional 85 beds and

NCHC an additional 21 beds or bed equivalents in 5 years time

• Taking into account future demand & income growth across the entire system, the STP would need to deliver

the full 15% acute cost opportunity to maintain financial sustainability by FY22/23

3

Acute deepdive | Summary

Demand and capacity mismatches evident across the system represent a broad system issue that must be addressed as a collective• Overall demand and capacity position today reflects a system issue

• Demand and capacity mis-match with non-elective demand set to exceed all bedded capacity within 2-3 years

• Primary care capacity constrained with overflow potentially putting unnecessary strain on the acutes

• Acute capacity is being utilised by patients who are deemed medically fit accounting for ~7% of bed days

Addressing specific issues in primary, community & social care will positively influence the

acute position today but cannot fully address future demand and capacity mismatches• New primary care models and community solutions could improve the Acute position but not fully prepare

them for the future

The acutes need to pursue both local optimization efforts and system based integration

opportunities to tackle their future demand & capacity and financial issues• High levels of cost and productivity variation exist across the acute footprint representing opportunity

• Increasing the level of integration could have quality and cost benefits

• The potential financial upside could be in the region of 5-15% of the collective acute cost base based on a

triangulation of methods (This includes the impact of both local and integration based opportunities)

• Quality benefits from increasing scale should also be considered further

Implementation of all outlined schemes will still leave a demand and capacity gap which will

require additional capacity or adoption of new ways of working• Even with all interventions applied 120 beds would be required across the three acutes by 2023

• This needs to be planned with consideration of key enablers

The acutes must now build from what they have already achieved, mobilise as a collective

and work towards clinically led, integrated approaches to care delivery

4

Implementing Primary Care Networks (PCNs) are a national priority area and a necessary

focus area for the STP• PC services have 9% excess demand which could drive additional demand in the physical acutes (e.g., 10%

inappropriate A&E attendances)




PCNs across the STP face unique demographic and workforce challenges and vary

significantly across their publically reported outcome measures• Demographic differences exist (e.g., 31% of the Norwich population are <25 versus STP average of 27%)

• Performance differs (e.g., Kings Lynn - 25% fewer 2WW referrals & 21% more cancer admissions

than expected)

• Workforce challenges differ across PCNs (e.g., Gorleston has a 28% shortfall in GPs vs national average)

Tailored strategies that take into account observed variation will be needed to address the

current and future STP challenges both within primary care and across the system• Leveraging alternative workforce models across PCNs could bridge the demand gap in ~4 years

• Specific interventions can serve to reduce demand and improve the primary care offering

• Other interventions will have impacts across the physical and mental acutes (e.g. 20% reduction in A&E

demand, 3.25% of non-elective admissions)

• Given incremental staffing costs of implementation this would result in in a net saving to the STP of £5m

• Estates and IT plans should be integrated across PCNs and CCGs to cater for new models of care

Next steps to support implementation will require clear allocation of roles and

responsibilities and a long term roadmap

Primary care deep dive | Summary

5

There are ~170 medically fit for discharge (MFFD) patients at any one time across all 3

acute trusts that could be cared for in an alternative, lower cost setting• 49 at NNUH, 57 at JPUH and 61 at QEH (although ~40% at QEH are out of area placements)



• If ~85% of MFFD patients could be moved to a community setting, ~43.5K acute bed days could be freed

(~7% of total IP bed day)

MFFD patients could be supported by a number of intermediate care bed settings or bed

equivalents which include a mixture of:• Intermediate care/reablement beds – ~130 beds would be required today rising to ~150 beds in 5 years

• Community virtual ward care - ~135 additional FTEs would be required today rising to ~160 FTEs in 5 years

• NFS reablement packages at home - ~10K reablement packages required today rising to ~11.7K in 5 years

The cost arbitrage opportunity indicates a net savings would be £13M• The removal of MFFD bed days from acutes creates a £22M gross savings opportunity for the acutes

• Approximately £9M investment required in intermediate care beds to meet the MFFD demand in

alternative setting

A detailed bottom-up analysis of MFFD patients and their care needs will be required to

determine the best and correct mixture of intermediate care services

Social & communitycare |Summary

6

Demand & CapacitySystem overview

7

Conducted 1 on 1 interviews with CEOs, CFOs COOs and Clinical leads

from all providers, CCGs and GP practices to better understand system

issues and test emerging findings

Steering Committee formed and used to align on content, to test the overall approach and to review progress

Held alignment sessions with data contacts at JPUH, QEH,

NNUH, NCHC, NCC, ECCH to validate demand and capacity

data and to test assumptions

15Provider

alignment sessions

6Steering

Committees

Approach |

Our approach

involved broad

engagement

and drew from

numerous data

sources

30StakeholderInterviews

13Data sources

Collated and processed internal data from CSU, NNUH, QEH,

JPUH, NCHC, ECCH, NCC and SCC together with public sources

e.g. HES data, Model Hospital, ONS, NHS Digital

8

Approach | Insights and recommendations supported by a demand and capacity model linked to finances

Fully linked baseline of demand & capacity• Includes flows between services and providers

5 year forward projection in "Do nothing" scenario

3 agreed components modelled• Impact on today's baseline and 5 year projected view

• Includes impact in isolation and in aggregate

Built-in dynamic functionality for scenario testing• Dashboard built to measure impact of changing key demand

and capacity levers

Linked financial impact• Financial baseline position across STP NHS organisations

(today and 5 year projected view)

• Includes financial impact assessment of proposed

compenents & system wide underlying position forecast

9

Executive summary | System view

There are demand and capacity mismatches across the system today which, given forecasted

growth, could result in a ~500 bed deficit by 2023 in a "Do nothing" scenario• Primary care is under pressure, with ~9% of unmet demand compounded by a decreasing GP workforce

• All acutes are at or over capacity today

• Non-elective demand is growing between 4-8% and will fill available elective capacity within 2-3 years

• Capacity across NCHC and NCC is not enough to meet demand, resulting in acute bed blockages (~170

medically fit for discharge patients are sitting at any one time across the acutes)

• "Do nothing" scenario forecasts the STP to run a ~£140m deficit by FY22/23 taking into account future income

& demand growth across the system

The current system issues cannot be addressed by any single provider. Collectively

interventions across the system could create a sustainable position today• Primary care interventions could reduce A&E attendances by 20%, emergency admissions by ~3% and OP

attendances by 10%, delivering ~£5m of benefit

• Increasing intermediate care capacity to shift medically fit for discharge patients could free up ~130 beds in

the acutes (~7% of overall acute bed days), with a net benefit of £13M. This influences acute length of stay

• Reducing length of stay variation across acutes could drive an additional 1% bed day reduction from reducing

elective length of stay. This equates to a 20 bed impact across the system

• Further benefits of up to £36M could be realized from broader integration & standardization across acute

sites, and it is likely that additional cost opportunities at other system providers also exists

• With all components combined, a total of ~180 acute beds could be freed today, although ~130 beds or bed

equivalents would be transferred into the community

• Further system integration could increase total cost opportunity to 10-15% savings against today's acute cost

base (incremental £50-99m) which return the STP in a financially sustainable position

However, given forecast growth, there will be shortfall of ~140 beds by FY22/23 despite

interventions. This means further capacity new models of care delivery are required• QEH forecasted to require an additional 13 beds, JPUH an additional 22 beds, NNUH an additional 85 beds and

NCHC an additional 21 beds or bed equivalents in 5 years time

• Taking into account future demand & income growth across the entire system, the STP would need to deliver

the full 15% acute cost opportunity to maintain financial sustainability by FY22/23

10

Note: Demand based on FY17/18 activity except for ECCH (see FN1) and Social Care (snapview view in Sept 2018). Theoretical capacity calculated in a number of different ways across the system based on available data. 1. Only 6 months of data provided for ECCH OOH beds so 6 months has been estimated based on 6 month averages 2. Assumes DNA rate (as % of total booked appointment) reduced to 5% and hospital cancellations reduce to 10% 3. Includes est. beds occupied by private occupants/other LA funded, assumes ~91% of care homes are occupied and also assumes that 10% of total registered care home beds are unusable

Baseline Today | Average demand and capacity varianceHighlights the difference between theoretical capacity versus demand placed upon the system

-9%

6%

2%4%

2%

-1%

1%

-5%

3%

9%

-8%

14%

4%2%

5%

9%

-10

0

20

10

ECCH1 ECCHQEH Social

Care

hours

QEH

Capacity variance

(%)

GP 111 EEAST QEH JPUH NNUH JPUH NNUH JPUH NNUH NCHC NCHC Social

Care

beds3

1%

20%

A&E

attendancesIP bed days OP attendances2

Acute Care

IP bed dayCommunity

contacts

CommunityUEC

Out of Hospital

Social

5.4M 302K 157K 66K 81K 133K 160K 151K 325K 302K 280K 843K 69K 12K 1.0M 427K 9.9K 3M

XX Theoretical capacity

"DO NOTHING" VIEW

Note that QEH's capacity

has decreased further in

FY18/19 due to closure of

ward as a result of

staffing issues

Note that

additional

deferral demand

is not included –

to be added

11

Backup | System growth driven by core input growth rates

Output growth ratesCore input growth rates

1. HES A&E data—Source of referral code 00 2. HES IP data—Admission method code 22

Growth

rate (%) Source

Overall acute

elective referrals2.3

Apr-Sep 17 to Apr-Sep 18 growth rate across all three

trusts for five CCGs

GP referrals

to A&E1 13.7FY15/16–17/18 CAGR for A&E attendances from GP

referrals across three acute trusts—HES A&E data

GP emergency

referrals to JPUH2 7.6FY15/16–17/18 CAGR in emergency IP admissions

referred from GPs (>> See backup)

GP emergency

referrals to NNUH

and QEH2

0.0Assumed to be flat (conservative view—actual referrals

have been decreasing by ~5-9%/year)

GP referrals

to NCHC2.5

Using acute elective referral growth rate from

QEH and NNUH as a proxy

GP referrals

to ECCH1.6

Using acute elective referral growth rate from

JPUH as a proxy

Ambulance growth

3.2FY15/16–17/18 CAGR for A&E attendances

from ambulance referrals across three acute

trusts—HES A&E data

111 growth

4.7Estimate from 111 (~5–10% per year)—lower end taken

as conversion rates are likely to change as well which

will reduce overall inflow into A&E/ambulance callouts

Source

Growth

rate (%)

QEH 2.3

JPUH 2.3

NNUH 2.3

QEH 4.4

JPUH 8.1

NNUH 3.6

QEH 2.3

JPUH 2.3

NNUH 2.3

QEH 2.3

JPUH 2.3

NNUH 2.3

QEH 5.7

JPUH 4.9

NNUH 5.9

NCHC 2.5

ECCH 2.4

NCHC 2.5

ECCH 1.6

Elective spells

Emergency spells

Day cases

OP attendances

A&E attendances

Inpatient spells

Patient contacts

All 2.0Social care

12

View in 5 years (FY22/23) | "Do nothing" scenarioClear capacity strains across entire system

-17%

-10%

-23%

-18%

-24%

-17%

-26%

-18%

-11%-8%

-3%

-12%

1%

-7% -6% -5%

-30

-20

-10

0

10

JPUHJPUHQEHQEHGP 111 ECCH1EEAST JPUH Social

Care

beds3

NNUH NNUH QEH ECCHNNUH NCHC NCHC Social

Care

hours

-3%

0%

A&E


Acute Care

IP bed dayCommunity

contacts

CommunityUEC

Out of Hospital

Social



"Do nothing" scenario

• Demand grows in line with growth rates

• Capacity remains constant as today

Note: Demand increased using growth rates; Assumes capacity stays constant 1. Assumes DNA rate (as % of total booked appointment) reduced to 5% and hospital cancellations reduce to 10% 3. Includes est. beds occupied by private occupants/other LA funded, assumes ~91% of care homes are occupied and also assumes that 10% of total registered care home beds are unusable

Capacity variance

(%)

"DO NOTHING" VIEW

13

Specific system components | Three system solutions have been modelledFurther opportunities may exist across the system which are not modelled here

Description

Theoretical system impact of

implementation today1 Sources

Primary

care

Implement locality based model

to increase capacity, improve

quality of care and reduce acute

demand1

A&E attendances reduced by ~20%

• ~15% reduction by addressing GP "overspill" into A&E

• ~5% from improved mental health provision

• Sub-set of assumed GP overspill into A&E from model

• MH pilots from Psych/GP co-location – NHS England

Emergency admissions reduced by ~3% from better MH

provision and improved cancer and diabetes care

• MH pilots from Psych/GP co-location – NHS-England

• Analysis of excess admissions from QoF submissions

OP attendances reduced by ~10% through alternative

support provided in primary care

• Virtual primary care consultation studies

Community /

Social Care

Augment intermediate care

capacity to shift medically fit for

discharge (MFFD) patients from

acutes into a community setting

resulting in a LoS impact across

the acutes

Non-elective LoS in acutes reduced by ~8% (~7% of total

acute bed day baseline driven by system changes) – 43.5K

bed days

• MFFD data from NNUH, JPUH and QEH – used to estimate

excess bed days and therefore the impact on LoS – Noted

to be within model hospital ranges

~43.5K MFFD bed days moved into

community setting, equivalent to:

• ~130-50 beds

• ~135-60 Virtual Ward FTEs

• ~10-12K reablement packages/~400-470 Norfolk First

Support FTEs

• NCHC virtual ward and home ward data Oct 17 – Sep 18

• NCC reablement data FY17/18

Acute

optimization

Reduce LoS and Cost/WAU

variation across the system by

local optimization and achieve

additional synergy benefits from

broader integration

Elective LoS reduced by ~8% (1% of total bed day

baseline)

• Model hospital analysis of LoS by specialty, adjusted for

acuity and focused on those not attributed to MFFD

• LoS adjusted to lowest across sites

Cost opportunity of ~£36M identified through

triangulation from model hospital variation and other best

practice examples – requires system solutions to enable

• Model hospital Cost / WAU analysis by specialty by site –

lowest internal selected with maximum impact cap of 10%

See Appendix for more detail on

methodology and impact

1. Note this will take more time to deliver over coming years. Note that impact on QEH has been adjusted to account for non-N&W patients (approximately 70% N&W and 30% non-N&W)

14


Combined view | Impact of implementing all schemes "Today" Specific interventions in primary care, acutes and community/social care increase capacity across system

8%

26%

30%27%

8%

21%

1%

15%

21%

1%

21%

6%2%

5%

9%

30

20

-10

0

10

GP JPUH QEH

23%

Capacity variance

(%)

NNUH Social

Care

hours

QEH111 NCHCEEAST QEH NNUH NCHCJPUH JPUH NNUH ECCH1 ECCH Social

Care

beds3

0%

6%

A&E


Acute Care

IP bed dayCommunity

contacts

CommunityUEC

Out of Hospital

Social



COMBINED VIEW

"Combined view" scenario

• Impacts from primary care, community / social care &

acute optimization applied to today's baseline


15

Combined view | Impact of implementing all schemes at 5 years (FY22/23) Even with interventions, additional investment still required in 5 years to meet demand

-8%

7%

16%

6%

-3% -5%-8%

-5%

2%

8%

-9%

8%

-7% -6% -5%

-20

0

20

Capacity variance

(%)

GP 111 ECCHJPUHEEAST Social

Care

beds3

QEH JPUHNNUH QEH NNUH QEH JPUH NNUH NCHC ECCH1

0%

NCHC Social

Care

hours

-3%

0%

A&E


Acute Care

IP bed dayCommunity

contacts

CommunityUEC

Out of Hospital

Social



"Combined" components

• Reduced demand and LoS from all 3

components modelled forward


Note: Demand increased using growth rates; Assumes capacity stays constant 1. Assumes DNA rate (as % of total booked appointment) reduced to 5% and hospital cancellations reduce to 10% 3. Includes est. beds occupied by private occupants/other LA funded, assumes ~91% of care homes are occupied and also assumes that 10% of total registered care home beds are unusable

COMBINED VIEW

16

Backup: "Do nothing" scenario would require additional ~500 beds within 5 years, dropping to ~140 if all outlined interventions were implementedFurther opportunity across the system does exist if new models of care are pursued

Additional 520 beds could be required by FY22/23 if

no interventions are made...

0

-50

-100

-9

-33

JPUHQEH NNUH NCHC ECCH

-53

0

-70

5 yr view – "Do nothing"

100 159 210 28 -

Note: # Beds required assumes 91% occupancy rateSource: HES inpatient data; NCHC inpatient data, BCG analysis

Capacity variance (# Bed days, K)

... Dropping to ~140 if interventions to reduce

demand and LoS were implemented today

-40

20

-20

0

QEH JPUH

-7

NNUH NCHC ECCH

-4-7

-28

1

5 yr view – "Combined

Interventions"

13 22 85 21 -


Beds Beds

Note that additional ~22 intermediate care beds/bed equivalents

would also be required in 5 years to accommodate growing MFFD

demand, otherwise capacity variance shown above will be even higher

17

Illustrative FY18/19 financial position assumes impact could be recognised immediately on the current FY18/19 baseline

-100

-50

50

100

0

Illustrative STP NHS Partners deficit breakdown in 2018/19, £m

Full synergy of system

integration benefit

(incremental on acute

optimization)

Forecast 18/19

underlying position

5

Component 1: Primary

Care new model of care

Component 3: Acute

optimization at areas

of lower efficiency

50(10% of base)

36

Component 2:

Additional social &

community capacity

13

9-58

95

Theoretical FY18/19

underlying position

(incl. acutes integration)

41

Theoretical FY18/19

underlying position

(post-scenario impact)

49(15% of base)

50-99

1. Top down assumption based off estimate for total cost opportunity of 5-15% for acute integration-see appendix for evidence baseNote: Figures are indicative; assumes that net benefit of proposed components are applied to the current forecast FY18/19 baseline positionSource: CCG & Provider FY18/19 in-year FY18/19 financial returns, FY17/18 NHS Reference costs data, Demand & Capacity Model, Model Hospital data FY16/17

Proposed components (Accounts for ~5% of base)

• Primary care

• Additional Social & Community care capacity

• Acute

1

Stretch ambition from full

acute reconfiguration and

benefits of system working

2

Proposals modelled to provide net £54m benefit

for STP (~5% of total acutes cost base)

System reconfiguration could deliver additional 5-10%1

of cost base (£50-99m) – further work required to

evaluate this opportunity

Combined view (NHS) | Three proposed initiatives modelled to deliver £54m benefit against today's baseline, leaving STP in ~£40m deficit

COMBINED VIEW

18

2022/23 STP NHS Partners financial position driven by 4 transformation initiatives under existing and future models of care

-200

200

-400

0

Full potential

synergy of

system

integration

benefit

(incremental)5

278

86

1

Component

3: Acute

optimization

and system

integration4

(incremental

on CIPs)

73

Commissioner

QIPPs4

Do nothing,

after CIPs and

QIPPs 2022/23

1632

Component 1:

Primary Care

new model of

care

STP NHS Partners deficit breakdown in 2022/23, £m

Component

2: Additional

social &

community

capacity

29

Do nothing,

before CIPs

and QIPPs

2022/23

66

Forecast 18/19

underlying

position

Acute trust and

other provider

demand growth

pressure3

231

CCG income

increase

52

95

Phyiscal Acute

Trust CIPs

Acute trust and

other provider

income

increase1

211

Forecast 22/23

underlying

position

300

142

CCG demand

growth

pressure2

1. Direct income to providers e.g. NHSE 2. CCG expenditure growth excl. main STP Trusts (NNUH, JPUH, QEH, NCHC & NSFT) e.g. primary care, ambulance & off-patch acutes 3. Expenditure growth from (NNUH, JPUH, QEH, NCHC & NSFT) 4. Impact of acute local optimization & system integration as incremental to Physical Acute CIPs to achieve 10% acute cost base opportunity 5. Full synergy benefit to achieve 15% savings on total acute trust cost base (15% includes 2% p.a. achievement of CIPs) Note: all figures stated are in-year run rate impact calculated over 4 year period FY18/19-FY22/23). Potential savings from CCG consolidation not included due to mandated redeployment in programme budgetsSource: CCG & Provider FY18/19 in-year FY18/19 financial returns, FY17/18 NHS Reference costs data, Demand & Capacity Model, Model Hospital data FY16/17

Base case

• Income growth

• Demand growth pressure

• BAU CIP and QIPP

1

Proposed interventions

2

Combined view | Outlined initiatives plus system integration could deliver £140m savings, leaving STP financially sustainable by FY22/23Note: Further opportunities exist across the system but have not been worked up

Assumes £23m absorbed

by planned increase in

primary care contracts

Net of £11m per year

investment in intermediate

care bed equivalents

Combined impact of physical acute CIPs, Component 2 & incremental

acute integration benefit to deliver ~10% of acute cost base (excluding

impact from demand management through Primary Care component)

COMBINED VIEW

Excludes income

from STP CCGs

Stretch ambition for system

integration to achieve cumulative

15% acute cost base opportunity

19

Recommendations and illustrative immediate next steps (4 months)

Implement tailored PCN strategies across the system to more effectively manage primary

care demand and down-stream demand• Ratify primary care network strategies with LDGs and clinicians in pilot practices

• Clarify roles of the central governance bodies and the local delivery groups

• Support a broad cultural change effort focusing at a practice level

• Define PCN, place and system IT requirements and accelerate standardization

• Circulate workforce targets and ambitions

Invest in lower cost community & social care capacity to off-load acute hospital bed capacity

where it is inappropriately occupied• Conduct a clinically led bottom up exercise to validate MFFD opportunity

• Align on methodology using beds versus bed equivalent social & community options

• Collaborate with Estates workstream to identify potential site for development

• Conduct detailed costings and secure investment

Pursue more integrated system working across acutes to realise scale benefits • Strengthen governance and ensure top level leadership are aligned behind the ambition

• Mobilize clinical teams to formally evaluate in-scope specialties and validate opportunities

• Agree phasing of all in-scope specialties and learn from ongoing pilot areas (e.g. urology)

• Formulate high level plans for clinical and non-clinical integration (where appropriate)

Bridge the remaining short-fall with other interventions or strategic estates initiatives• Evaluate new approaches (e.g. controlling public self-referrals, pathway standardization, etc.)

• Explore other opportunities from the system (e.g. Primary Care optimization, community support)

• Engage regulators in the overall position and future plans to achieve long term sustainability

• Conduct detailed assessment of estates plans and plan for increase in acute beds in 5 years (~140 beds

even with all interventions)

Address locally accessible optimization opportunities across the acutes• Establish warranted versus unwarranted variation across the acute footprint

• Identify through corporate and clinical engagement areas of opportunity

• Systematically introduce continuous improvement cycles to improve services

• Look to right-size corporate functions enabled through digital solutions

20

Demand & CapacityDeep dive | Acutes

21

Conducted 1 on 1 interviews with CEOs, CFOs COOs and Clinical leads

from all providers, to better understand system issues and test

emerging findings

Steering Committee formed and used to align on content, to test the overall approach and to review progress

Held alignment sessions with data contacts at JPUH, QEH,

NNUH to validate demand and capacity data and to test

assumptions

15Provider

alignment sessions

6Steering

Committees

Approach |

Our approach

involved broad

engagement

and drew from

numerous data

sources

30StakeholderInterviews

>10Data sources

Collated and processed internal data from CSU, NNUH, QEH,

JPUH, with public sources e.g. HES data, Model Hospital, ONS,

NHS Digital

Acutes

22

Acute deepdive | Summary

Demand and capacity mismatches evident across the system represent a broad system issue that must be addressed as a collective• Overall demand and capacity position today reflects a system issue

• Demand and capacity mis-match with non-elective demand set to exceed all bedded capacity within 2-3 years

• Primary care capacity constrained with overflow potentially putting unnecessary strain on the acutes

• Acute capacity is being utilised by patients who are deemed medically fit accounting for ~7% of bed days

Addressing specific issues in primary, community & social care will positively influence the

acute position today but cannot fully address future demand and capacity mismatches• New primary care models and community solutions could improve the Acute position but not fully prepare

them for the future

The acutes need to pursue both local optimization efforts and system based integration

opportunities to tackle their future demand & capacity and financial issues• High levels of cost and productivity variation exist across the acute footprint representing opportunity

• Increasing the level of integration could have quality and cost benefits

• The potential financial upside could be in the region of 5-15% of the collective acute cost base based on a

triangulation of methods (This includes the impact of both local and integration based opportunities)

• Quality benefits from increasing scale should also be considered further

Implementation of all outlined schemes will still leave a demand and capacity gap which will

require additional capacity or adoption of new ways of working• Even with all interventions applied 120 beds would be required across the three acutes by 2023

• This needs to be planned with consideration of key enablers

The acutes must now build from what they have already achieved, mobilise as a collective

and work towards clinically led, integrated approaches to care delivery

23


System issues | Demand and capacity variance today reflects a system issue

-9%

6%

2%4%

2%

-1%

1%

-5%

3%

9%

-8%

14%

4%2%

5%

9%

-10

0

20

10

ECCH1 ECCHQEH Social

Care

hours

QEH

Capacity variance

(%)

GP 111 EEAST QEH JPUH NNUH JPUH NNUH JPUH NNUH NCHC NCHC Social

Care

beds3

1%

20%

A&E


Acute Care

IP bed dayCommunity

contacts

CommunityUEC

Out of Hospital

Social



Note that QEH's capacity

has decreased further in

FY18/19 due to closure of

ward as a result of

staffing issues

Note that

additional

deferral demand

is not included –

to be added

Acutes

24

System issues | Future projections of acute demand in a “do-nothing” scenario suggest non-elective demand will fully displace elective care by 21/22

4

18

No. of Elective bed days (K)

8

FY17/18

FY18/19

FY19/20

FY20/21

13 14

FY21/22

14

14

FY22/23

16 16 15 14

Demand met in JPUH Demand met elsewhere

19


62 63

FY18/19

FY17/18

2640

FY19/20

FY20/21

62

FY21/22

64

FY22/23

62 62 62 63 64

218


18

FY17/18

11

FY19/20

17

FY18/19

17

FY20/21

FY21/22

18

FY22/23

17 17 18 18 18

No. of Bed

equivalents elsewhere0 11 24 39 43 41 56 79 122 186 190 193 5 33 52 53 54 55

No. of Spells met

elsewhere (K)10.0 1.4 3.0 4.9 5.5 5.3 8.2 11.5 17.6 27.0 27.5 28.0 1.1 6.9 10.8 10.9 11.2 11.4

QEH position will

also be impacted

by staffing

challenges

JPUH displacement NNUH displacement QEH displacement

1. Assumes average LoS of 2.6 days per elective spell (FY17/18 average)Note: Demand able to be met in hospital calculated by taking current bed base at 91% occupancy rate and subtracting all emergency activity plus activity categorized under admission method “Other” and “Transfers”Source: HES inpatient data; BCG analysis

Acutes

25

System issues | Upstream ~9% of primary care appointments are not being met today which could influence up to 15% of A&E demand

50

105

110

120

115 114

20/21

Appointments per week (K)

104104

18/19

116

103

109

19/20

118

101

114

Demand Capacity (Do nothing, current trend)1 Capacity (STP plans, best case)2

1. Based on historic trends in GP workforce (1% decline p.a.) 2. Based on STP recruitment targets Source: STP Predictions; NHS Digital

Dem

and g

ap u

nder a

ll models

The number of primary

care appointments is

21x the volume of

A&E attendances

Of patients who cannot

get an appointment 8%

may attend A&E: ~15%

of A&E attendances

Acutes

26

….which suggest ~8% of total bed days could be managed downstream

5%

651

149

8%

Number of bed days (K)

95%

NNUH

14%

86%

JPUH

8%

92%

QEH

92%

Total

340

161

MFFD days Non-MFFD days

Avg. numer of MFFD

at any one time

Avg. LoS

as MFFD

49 2.3NNUH

57 8.3JPUH

61 8.0QEH

Total 167 4.3

Source: NNUH MFFD data (Sep 17–Oct 18), JPUH MFFD data (Sep 17–Oct 18) & QEH MFFD data (Oct snapshot)

System issues | There are ~170 MFFD patients across the acutes….

Acutes

27

Increasing Primary Care Capacity can reduce A&E and

non-elective admissions

By utilization of the wider workforce including Mental Health

and Physiotherapist capacity in primary care can be increased

to meet the current 9% shortfall

This could reduce A&E attendances by ~20% and non-elective

admissions by ~3%

Increasing Community Care Capacity can reduce MFFD

44k MFFD bed days transferred into either:

– ~130-50 community beds

– ~135-60 community Virtual Ward FTEs

– ~10-12K community reablement packages

System solutions | Addressing issues outside the acutes creates spare capacity today but does not provision for the future…

Source: BCG analysis see Phase 2 Summary pack for more details

From To 2023

A&E

capacity

variance

QEH 1% 26% 7%

JPUH 4% 30% 16%

NNUH 2% 27% 6%

IP bed

capacity

variance

QEH -1% 8% -3%

JPUH 1% 21% -5%

NNUH -5% 1% -8%

Addressing MFFD and Primary care creates capacity in

system today to help relieve pressures in 2023

Acutes

28

Integration | Model Hospital benchmarks reveal significant cost variation across the acute footprint representing potential opportunity…

1. Weighted Activity Unit—expected cost: £3.5k 2. Assumes all Treatment Function Codes are consistent across Trusts – this should not be used to inform the answerNote: Analysis based on available specialties: Excludes Breast Surgery, Dermatology, ENT, Medical and Clinical Oncology, Plastics and Burns and rheumatologySource: Model Hospital Data FY16/17

Cost/WAU1 by

specialty, POD Elective Non-elective Day case Outpatient Other Total

Specialty2 NNUH JPUH QEH NNUH JPUH QEH NNUH JPUH QEH NNUH JPUH QEH NNUH JPUH QEH NNUH JPUH QEH

Cardiology £2,740 £3,364 £5,187 £3,910 £3,968 £3,811 £2,911 £955 £7,097 £2,309 £6,715 £2,816 £2,983 £2,702 £3,948 £3,236 £4,267 £3,431

Diabetes & Endocrinology £3,075 £4,582 £3,818 £3,254 £3,031 £1,828 £2,228 £3,058 £3,415 £4,115 £3,181 £3,261

Gastroenterology £3,454 £3,449 £3,340 £2,654 £3,474 £3,342

General Medicine £386 £1,722 £3,119 £3,583 £3,469 £3,417 £2,654 £2,328 £1,973 £3,182 £3,333 £4,172 £3,497 £3,331 £3,402

Geriatric Medicine £2,696 £3,025 £2,608 £3,837 £3,020

Neurology £2,726 £2,775 £2,945 £4,285 £3,880 £4,338 £3,000 £2,805 £3,098

Paediatrics £3,865 £2,970 £3,406 £2,987 £3,741 £4,737 £3,996 £1,970 £6,151 £3,202 £3,535 £2,665 £3,621 £3,021 £3,740 £3,229 £3,518 £3,894

Respiratory £4,202 £2,922 £4,391 £4,412 £3,326 £1,496 £2,959 £2,576 £3,554 £3,699 £4,006 £3,822

Stroke £2,483 £2,289 £5,069 £2,432 £5,021 £5,121 £2,432 £5,016 £5,121

General Surgery £3,093 £3,950 £3,835 £3,239 £3,695 £3,134 £3,338 £2,832 £3,854 £3,104 £5,629 £2,816 £3,232 £3,767 £4,309 £3,224 £3,647 £3,370

Orthopaedic Surgery £3,584 £2,589 £3,303 £2,933 £3,480 £3,085 £3,494 £2,430 £3,801 £2,859 £4,064 £3,303 £4,206 £4,052 £4,340 £3,207 £3,181 £3,268

Urology £3,443 £3,363 £3,717 £3,611 £2,764 £3,415 £3,965 £3,608 £4,863 £2,711 £4,161 £2,641 £3,495 £3,845 £4,235 £3,395 £3,577 £3,735

Obstetrics & Gynaecology £3,290 £3,090 £3,848 £3,188 £3,673 £3,939 £3,601 £2,568 £3,538 £3,495 £3,371 £3,748 £4,123 £2,812 £4,224 £3,284 £3,423 £3,853

highestlowest

Acutes

29

Integration | …there is also significant Length of Stay variation although some of this is explained by system issues

LoS in days Elective Non-elective

Specialty NNUH JPUH QEH NNUH JPUH QEH

Cardiology 0.96 1.33 6.38 9.43 8.79

Dermatology 0.83 1.08

Diabetes & Endocrinology 1.97 2.67 2.25 8.93 7.85 9.05

Gastroenterology 4.57 1.25 9.83 9.85

General Medicine 3.26 2.91 9.34 10.59 9.59

General Surgery 4.93 3.32 1.91 8.93 7.75 8.54

Geriatric Medicine 1.00 10.28

Obstetrics & Gynaecology 1.77 2.02 0.88 3.45 3.64 3.53

Orthopaedic Surgery 3.29 3.77 2.34 10.32 13.19 10.40

Paediatrics 1.45 1.01 0.28 4.39 3.11 3.70

Respiratory 7.39 3.86 9.24 9.36

Rheumatology 10.62

Urology 2.06 2.07 2.06 5.34 5.58 5.34

Total bed day reduction opportunity1 815 (1%) 4,315 (25%) 8 (0%) 5,407(2%) 13,167(10%) 4,872(4%)

Note: Analysis based on available specialties; Areas with disparity in bold; Assumes TFCs are reported in a standard way 1. Calculated by shifting LoS to the lowest LoS across 3 trusts after accounting for acuity differences (see Acute deep-dive packs for further detail) Source: Model Hospital

XX Areas of disparity

Acutes

A large proportion of variation in non-elective LoS is likely driven by MFFD issues which is

addressed in the community & social care interventions

30

Integration | …there is also significant Length of Stay variation although some of this is explained by system issues

Reduction in bed days Elective Non-elective


Total bed days 62,311 17,546 16,778 27,214 6,721 10,438

Cardiology – - 8 - - 394

Dermatology - 2 - - - -

Diabetes & Endocrinology - - - 680 - -

Gastroenterology 617 - - - - -

General Medicine - 114 - - 9,850 2,384

General Surgery - 734 - 2,439 - 1,448

Geriatric Medicine - - - - - -

Obstetrics & Gynaecology - 2,069 - - - 20

Orthopaedic Surgery - 1,283 - - 3,271 87

Paediatrics 198 113 - 2,288 - 539

Respiratory - - - - 45 -

Rheumatology - - - - - -

Urology - - - - - -

Total bed day reduction

opportunity815 (1%) 4,315 (25%) 8 (0%) 5,407(2%) 13,167(10%) 4,872(4%)

Note: Analysis based on available specialties;1. Weighted average of each trust reduction by total bed days; This assumes TFCs are reported in a standard waySource: Model Hospital Data FY16/17; HES bed days data

highestlowest

Acutes

31

Integration | Evidence suggests that more integrated ways of working can have both quality and efficiency impacts

Scale can impact the quality of care through

standardisation and leveling up. This can

impact:

• Outcomes e.g., mortality; re-operation rates

• Safety and quality metrics

• Workforce sustainability and satisfaction

• Patient experience

Quality

System working can also unlock efficiency

opportunities that would be unachievable

through local efforts. This can include:

• Workforce synergies e.g., on-call rotas

• Operational benefits e.g., LoS

• Estates benefits e.g., reduced duplication

Efficiency

Acutes

32

Integration | Integration can lead to an increase in scale, which is strongly linked to higher quality of care

82% 81%

26%18% 19%

0

50

100

74%

Surgery Oncology

0%

Paediatric care

0%0%

No statistical relevancePositive correlation Negative correlation

~240 ~70# studies1

1. Individual research papers might be double-counted when providing correlation points for more than one specialty and on physician and hospital levelSource: Include meta-studies: 1. Chowdhury et al. (2007); 2. Halm et al. (2002)

~70

• Coordination can help drive some

standardisation, but does not lead to an

increase in scale, and will therefore not

deliver full potential benefits

• Clinical reconfiguration leads to an

increase in scale, which is highly

correlated with higher quality of care

Clinical reconfiguration is

required to get full scale effect

No studies found a negative correlation between scale and

quality; 70-80% indicate a positive correlation

% of

studies

Acutes

33

-10

0

10

20

30

US

Model Hospital

Consumables

Clinical Staff

Support Staff

ITUK

Outlier1, excluded

BCG experience with

hospital transformations

Integration | Integration can drive financial efficiencies of between 5-15%

Cost savings potential (%) based on triangulation of four methods

STP benchmark

analysis (~10%)

Service line level

scale effects (4-10%)2

Hospital level

scale effects (11-15%)

1. E.g., Observation not in line with other findings, limited applicability to Norfolk; 2. Data points refer to individual value drivers, not to impact on total cost rangeSource: Triangle OBC; 2020 Delivery; BCG x Frontier Economics; Scientific journals and non-scientific journals; Model Hospital Data; BCG Analysis

Real-world

Evidence (2-10%)

Acutes

34

Alignment of selective clinical

services, with shared rotas and

clinical pathways. Individual

Trusts maintain significant

autonomy

Collaboration on majority of

clinical services. Cross-site

working. Individual Trusts retain

control of budgets

Integration clinical and

corporate services. Sharing of

budgets and aligned incentives

Full horizontal and vertical

integration. Shared incentives

across the system

Pros

• Minimal disruption

• Builds on existing plans

Pros

• Clinical synergies possible

• Outcomes may improve

• Incremental progression

of plans

Pros

• Horizontal quality

and efficiency scale

benefits possible

• Reduces risk of provider silos

Pros

• Horizontal and vertical scale

benefits possible

• Aligns with STP strategy

Selective collaboration Strong collaboration Full integration ACO/ICO

Integration| The acutes need to set a clear ambition for the level of integration they want to pursue

Increasing integration

Note: Further work required to explore con mitigation

Cons

• Limited financial benefits

• Limited quality benefits

• Service may remain

vulnerable

Cons

• Cannot maximise

scale benefit

• Risk of clinical fragmentation

• Disincentives for individual

budget holders

Cons

• Disruptive and

time consuming

• Risk of impact to services in

short term

• Misses vertical

integration opportunities

Cons

• Significant time to implement

• Highly complex and disruptive

across the system

• Requires structural changes

outside of acutes (e.g.,

harmonization of CCGs)

Acutes

35

System solutions | …despite implementation of all system solutions 120 acute beds would still be required by 2023 (~140 overall)

Additional 520 beds could be required by FY22/23 if

no interventions are made...

-20

-80

-60

-40

0

20

JPUHQEH NNUH NCHC ECCH

-33

-53

-70

-9

0

5 yr view – "Do

nothing"

100 159 210 28 -

1. Assumes 91% occupancy rateSource: HES inpatient data; NCHC inpatient data, BCG analysis


... Dropping to ~140 if interventions to reduce

demand and LoS were implemented today

0

-20

-30

-10

10

20

QEH JPUH NNUH NCHC ECCH

-4-7

-28

-7

1

5 yr view – "Combined

Interventions"

13 22 85 21 -


Acutes

Beds Beds

Note that additional ~22 intermediate care beds/bed equivalents

would also be required in 5 years to accommodate growing MFFD

demand, otherwise capacity variance shown above will be even higher

36

Data sharing enables timely and accurate

information to be available to all members of

the acutes and wider STP

Requires common IT systems where possible and

system interlinks where required e.g. between

acute services and primary care

All acutes are likely to require additional bed

capacity with NNUH requiring ~85 even with

interventions

This will require identification of suitable

estates resources and provision of additional

capacity where appropriate

Information sharing is key to efficient

working across acutes and STP

Adequate estates resources must also be

identified and implemented

Key Enablers| IT and Estates provision vital to efficient implementation

Acutes

37

Impact | Acute deep dive

Demand and capacity impacts System financial impactsLimited impact on IP bed capacity; driven by LoS reduction in elective only Financial impact primarily driven by unit cost

improvement due to optimization within specialties

across trusts

Note: System financial impacts in isolation are greater than the combined effect due to a moving baseline

Today 5yr view

NNUH

JPUH

QEH

£14.9M £29.7M

£11.2M £24.2M

£9.9M £18.1M

“Do

nothing”

today

S&C

impact

today

S&C

impact

future

-5%

-1%

1%

-5%

-1%

4%

-18%-17%-24%

IP—JPUHIP—QEH IP—NNUH

Acutes

38

Next steps | Seek to resolve in the next 3 months

Ensure acute leadership are fully aligned behind the integration ambition by ensuring

Executive groups must align and agree ambition (esp. QEH)(e.g. savings targets, quality targets, time-lines, non-negotiables, clinical roadmaps, end-state, etc.)

Ensure appropriately strengthened governance body to provide senior leadership and

oversight and effective coordination across all plans

Ensure adequate implementation resource is in place to support planning and

implementation at pace

**Engage Medical Directors early and mobilise them behind a singular ambition.

Establish a clinical leaders group to support the programme

**Ensure quick wins are realized through well supported pilots (e.g. urology) and that

successive specialties are initiated and progressed

Ensure systems are aligned sufficiently to measure, monitor and track both delivery

and quality outcomes ensuring progress is made against plans

Gaining momentum behind the key enablers will be critical for delivery

Review the direction of travel after the next CQC results for NNUH

**Already in progress with acute transformation workstream

Acutes

39

Demand & CapacityDeep dive | Primary care

40

>30Stakeholderinterviews

Clarified pain points and emerging findings with GPs, practice

managers, mental health staff and management through a

series of 1 on 1 stakeholder interviews

Approach|

Adopted a

systematic

approach to

develop an

aligned

primary care

strategy

Tested the emerging primary care strategy with GP Provider

groups, GP forums and mental health work stream groups and

iterated on the final output

6Stakeholder

forums

6Governance committees

Regularly reported on the approach, emerging findings and direction of travel. Tested materials and acted on feedback to provide the appropriate assurance

>10Data Sources

Collated, combined and processed data from both public and private

sources including: GP Patient Survey, STP workforce predictions, QOF

Returns, ONS

Primary Care

41

Implementing Primary Care Networks (PCNs) are a national priority area and a necessary

focus area for the STP• PC services have 9% excess demand which could drive additional demand in the physical acutes (e.g., 10%

inappropriate A&E attendances)




PCNs across the STP face unique demographic and workforce challenges and vary

significantly across their publically reported outcome measures• Demographic differences exist (e.g., 31% of the Norwich population are <25 versus STP average of 27%)

• Performance differs (e.g., Kings Lynn - 25% fewer 2WW referrals & 21% more cancer admissions

than expected)

• Workforce challenges differ across PCNs (e.g., Gorleston has a 28% shortfall in GPs vs national average)

Tailored strategies that take into account observed variation will be needed to address the

current and future STP challenges both within primary care and across the system• Leveraging alternative workforce models across PCNs could bridge the demand gap in ~4 years

• Specific interventions can serve to reduce demand and improve the primary care offering

• Other interventions will have impacts across the physical and mental acutes (e.g. 20% reduction in A&E

demand, 3.25% of non-elective admissions)

• Given incremental staffing costs of implementation this would result in in a net saving to the STP of £5m

• Estates and IT plans should be integrated across PCNs and CCGs to cater for new models of care

Next steps to support implementation will require clear allocation of roles and

responsibilities and a long term roadmap

Primary care deep dive | Primary narrative

Primary Care

42

Focus area | We cannot meet primary care demand with our current model of care

105

115

120

110

50

104

101

19/2018/19

104

116

103

20/21

114

Appointments per week (k)

118

109

114

Demand Capacity (STP plans, best case)2Capacity (Do nothing, current trend)1

Gap between appointment demand and GP capacity at current GP workloads

1. Based on historic trends in GP workforce (1% decline p.a.) 2. Based on STP recruitment targetsSource: STP Predictions; NHS Digital

Primary Care

43

Focus area | This has impacts beyond primary care with some unable to get appointment attending A&E

Notes: All numbers apts/week unless stated Source : NHS Digital; GP Patient Survey; The Lancet; STP workforce projections

Appointment

Demand

Excess demand

GP Face-Face

GP Phone

GP Home visit

Nurse

Other NHS

Pharmacist

A&E

Not Seen

10K

4K 1K

2K<1K

4K

103K

64K

11K

2K

27K

113K

GP FTE

542

Nurse FTE 368

General Practice

Assumptions• Demand from June 2018 NHS Digital GP Dataset and 2017

GP Survery

• If unable to get apt; 16% do nothing, 2% pharmacists, 8%

A&E, 34% other NHS service, rest retry GP

GP Triage

6K

Primary Care

44

Focus area | GP Workforce is under increasing pressure

1258 1258 1258 1266 1273 1281

377 377 383 385 386

572 616724

0

1000

2000

3000

8

2019

9 1015

382

389

2018

Projected primary care workforce '18-23

650

11

426

2021

686

374

13

2022

511467

525

381

20232020

+3%

GP GP locum GP Nurse AdminDirect Patient Care

Source: NHS Digital GP Workforce Data, March 2018; STP Primary care Workforce projections

527 522 517 512 508

144 186

0

200

400

600

800

2020

104

532

2018

54

2019 20222021

232

2023

532581

626661

699739

STP workforce plan indicates 3% annual increase, GP

numbers to rise by 5.5% annually

However GP numbers currently decreasing

by 1% annually

STP Predictions Current Trend

Primary Care

45

"We envisage ‘at scale’ working in larger

practice groupings will create opportunities to

embed a more locally focused team"

GP Forward view - 2016

"Practices should share community nursing,

mental health and pharmacy teams among

others"

GP Five year forward view - 2014

• Forming 20 Primary Care Networks to drive

integration of care

– Monitoring progression of plans through

maturity assessments

• Setting an ambition to form an integrated

care system (ICS)

National direction towards PCN model The STP has started along this road

Focus area | PCN based models of care are the direction of travel for the STP and for NHS England

Primary Care

46

Focus area | Maturity levels vary across regions but all regions have yet to fully complete Step 1

Primary Care

Networks Integrated teams

Understanding

variation in outcomes

Understanding patients

needs

Standardizing models

of care

Shared records

available1

GYW

Norwich

West Norfolk

North Norfolk

South Norfolk

Note: 1.Step 2 goalSource: Selected areas from PCN maturity reports 2018

Some progressNo progress Completed

Primary Care

47

Norwich 20-24 population peak

requires specific services to meet

young population needs e.g.

sexual health

GY&W – Dual peaks at 50-54 and

70-74 indicate possible double hit

in demand in future years

North, West and South Norfolk –

Similar aging population requiring

services to meet population

demand e.g. dementia care

PCN differences | Aging populations though Norwich requires specific services for its young population

5 100515 10 15

Population 1,000s

15-19

Female

20-24

5-9

25-2930-3435-3940-44

Male

45-4950-5455-5960-6465-6970-7475-79

0-4

10-14

85-8980-84

90+

North Norfolk CCG

20 2010 0 10

Female

0-4

Population 1,000s

Male

65-69

25-29

10-14

20-24

45-49

70-74

15-19

55-59

5-9

90+

50-54

75-79

60-64

30-34

40-44

80-84

35-39

85-89

Norwich CCG

15 5 5 15010 10

Male

0-45-9

Population 1,000s

Female

25-29

40-4445-49

35-39

55-5960-64

30-34

20-24

65-69

50-54

75-79

15-19

85-89

70-74

80-84

10-14

90+

South Norfolk CCG

0515 15510 10

Population 1,000s

25-2930-34

90+

80-84

Male

35-39

55-59

10-14

70-74

60-64

0-4

40-4445-49

Female

50-54

65-69

75-79

5-9

20-2415-19

85-89

05 1551015 10

75-79

60-64

45-49

90+

40-44

25-29

35-39

65-69

80-84

20-24

50-54

70-74

30-34

85-89

55-59

0-45-9

10-14

Population 1,000s

MaleFemale

15-19

GY&W CCGWest Norfolk CGG

Source: ONS Population forecasts

Primary Care

48

Currently 8% of patients can’t get a GP appointment

• Resulting in an additional ~800 inappropriate

attendances to A&E per week

High nursing levels helping to offset demand

• 0.37 FTE per 1kpts vs. 0.27 FTE per 1kpts nationally

But an extra ~50 GPs would be required to meet national

GP/patient ratio

• This would require up to an additional ~£5M1

However, GP workforce is currently declining at 1%/yr

requiring innovative solutions to address the issues

Primary care workforce already under pressure

Variation in demand gap but all localities

under strain

PCN differences | Variation in demand gap across PCNs requires tailored workforce plans

10%

5%

20%

0%

15%

NN

2

Patients unable to get apt (%)

NN

1

MID

SN

HIP

Low

est

oft

Gre

at

Yarm

outh

Bre

ckla

nd

NN

3

Sw

aff

ham

Fens

Coast

al

Kin

g’s

Lynn

Kett

s O

ak

Gorl

est

on

NN

4

South

Waveney

Norw

ich 2

Norw

ich 4

Norw

ich 3

Norw

ich 1

Norwich

CCG

South

Norfolk CCG

North

Norfolk

CCGNorfolk CCGGY&W CCG

1. Based on £110k/year per GP FTE, £48k per Nurse FTE, £30k per DPC FTE, £24k per Admin FTESource: GP Practice Survey 2017, National workforce statistics

Primary Care

49

PCN differences | Requirements to meet demand by 2023 are unlikely to be achievable

Locality GPs Nurses DPC1 AdminRequired Difference Required Difference Required Difference Required Difference

GY&

W

Gorleston 26.6 3.6 12.7 2.4 8.4 0.5 28.8 1.8

Great Yarmouth 38.3 4.3 18.3 0.0 16.4 1.0 30.8 1.9

Lowestoft 47.7 10.9 22.8 7.7 9.3 0.6 65.4 4.0

South Waveney 32.0 6.0 15.3 0.0 12.2 0.8 86.9 5.3

Nort

h N

orf

olk NN1 25.0 5.5 11.9 0.0 36.5 2.3 30.5 1.9

NN2 24.3 5.5 11.6 4.3 20.5 1.3 69.6 4.4

NN3 27.4 5.7 13.1 0.0 30.8 2.0 64.7 4.1

NN4 28.0 3.7 13.4 0.7 21.8 1.4 70.6 4.5

Norw

ich

Norwich 1 37.4 12.3 18.8 1.0 6.6 0.4 63.9 4.3

Norwich 2 40.2 4.5 18.3 3.1 10.1 0.7 52.8 3.6

Norwich 3 27.8 3.7 16.5 4.0 7.0 0.5 23.0 1.6

Norwich 4 36.7 4.3 19.1 3.0 9.5 0.8 34.3 2.9

South

Norf

olk Breckland 25.5 3.6 12.2 0.2 1.1 0.1 29.6 2.5

Ketts Oak 45.8 10.8 21.9 0.0 39.8 3.3 98.5 8.2

MID 27.6 3.7 13.2 0.0 20.7 1.7 58.2 4.9

SNHIP 38.8 4.4 18.5 2.2 40.3 3.4 86.8 7.2

West

Norf

olk Coastal 16.4 3.0 7.8 0.5 12.8 0.9 30.1 2.1

Fens 29.5 5.8 14.1 0.0 22.4 1.6 48.7 3.4

King’s Lynn 23.6 3.4 11.3 0.0 5.7 0.4 21.2 1.5

Swaffham 36.6 10.2 17.5 0.0 36.8 2.6 38.9 2.7

Total 635.3 115.0 303.1 33.4 368.4 25.9 1032.6 72.1

0%

10%

5%

15%

20%

Nursing DPCGP Admin

% Staffing shortfall

1. Direct Patient Care Source: NHS Digital, NHS GP Practice Profiles, Growth Rates from ONS

Percent of staff needed to be recruited to

meet 2023 target

Primary Care

50

PCN differences | Outcome measures vary across PCNs and will require tailored targets

Implications:

1. High variation across regions-

e.g. GY&W and Norwich

regions show increased need

2. High variation across PCNs

within regions - e.g. Gorleston

vs. South Waveney

3. High variation across

performance indicators within

PCNs- e.g. Coastal is both the

best and worst for separate

metrics

Note: Scores are relative across the rows demonstrating hotspots and issues across the regionSource: Publically available QOF data on GP outcome measures

NHS Great

Yarmouth and

Waveney CCG

NHS North

Norfolk CCG

NHS

Norwich CCG

NHS South

Norfolk CCG

NHS West

Norfolk CCG

Gorl

est

on

Gre

at

Yarm

outh

Low

est

oft

South

Waveney

NN

1

NN

2

NN

3

NN

4

Norw

ich 1

Norw

ich 2

Norw

ich 3

Norw

ich 4

Bre

ckla

nd

Kett

s O

ak

MID

SN

HIP

Coast

al

Fens

Kin

g's

Lynn

Sw

aff

ham

Patient Satisfaction 19 13 12 6 5 2 11 17 15 16 20 9 19 14 7 8 1 4 3 10

Cancer Performance 7 19 20 5 3 16 2 1 17 12 15 8 15 10 6 18 9 15 11 5

Diabetes Performance 20 17 19 16 10 3 4 18 14 1 6 9 7 12 11 2 5 9 14 16

AF Performance 19 13 11 18 6 2 4 20 4 5 7 13 1 9 15 8 16 10 14 18

COPD Performance 20 13 14 9 13 16 13 6 7 3 15 2 6 13 2 6 17 19 18 9

Mental Health Performance 7 11 4 6 8 10 13 18 2 3 15 1 14 17 20 16 9 19 12 6

Weighted Total Rank 20 19 17 7 1 4 4 16 12 5 18 2 14 15 8 9 6 13 11 10

Primary Care

51

PCN Strategy | Requirements, outcomes and targets have been identified per PCN

Current outcomes for each

neighborhood can be found in

the Primary Care Dashboard

Outcome targets for each

neighborhood can be found in

the Primary Care Dashboard

PCN outcomes PCN targets

Supplied elsewhere

Detailed workforce

requirements per

neighborhood can be

found in the Primary Care

Deep Dive Model

Workforce model

Detailed here

Detailed evidence based

interventions to drive primary

care and system impacts

Specific interventions

Primary Care

52

PCN Strategy | An alternative workforce model should be leveraged

Locality GPs Nurses ANPs1 Admin MH/Wellbeing Physios

Required Difference Required Difference Required Difference Required Difference Required Difference Required Difference

GY&

W

Gorleston 18.8 2.0 12.1 1.9 4.0 3.0 30.7 3.6 5.3 5.3 5.3 5.3

Great Yarmouth 41.9 0.0 25.9 1.0 5.7 3.2 33.5 4.6 7.7 7.7 7.7 7.7

Lowestoft 36.6 3.7 29.0 7.0 9.7 0.0 69.1 7.6 9.5 9.5 9.5 9.5

South Waveney 25.6 0.8 28.0 6.0 7.6 0.0 89.3 7.8 6.4 6.4 6.4 6.4

Nort

h N

orf

olk NN1 28.5 0.0 22.0 5.0 7.1 0.0 32.4 3.8 5.0 5.0 5.0 5.0

NN2 20.5 0.0 13.2 3.0 4.6 0.0 71.5 6.3 4.9 4.9 4.9 4.9

NN3 24.5 0.0 17.4 1.0 5.2 0.0 66.8 6.2 5.5 5.5 5.5 5.5

NN4 24.5 0.0 20.6 4.0 7.3 0.0 72.7 6.6 5.6 5.6 5.6 5.6

Norw

ich

Norwich 1 28.7 4.9 18.8 1.0 6.6 0.0 66.7 7.2 7.5 7.5 7.5 7.5

Norwich 2 33.6 0.0 18.3 3.1 6.7 0.0 55.8 6.6 8.0 8.0 8.0 8.0

Norwich 3 21.2 3.6 16.5 4.0 5.7 0.0 25.1 3.7 5.6 5.6 5.6 5.6

Norwich 4 34.7 0.0 19.1 3.0 5.7 0.0 37.1 5.7 7.3 7.3 7.3 7.3

South

Norf

olk Breckland 19.4 1.9 14.0 1.0 3.9 0.0 31.6 4.4 5.1 5.1 5.1 5.1

Ketts Oak 35.6 3.0 25.8 2.0 6.9 0.8 101.9 11.7 9.2 9.2 9.2 9.2

MID 24.4 0.0 26.3 3.0 9.7 0.0 60.3 7.0 5.5 5.5 5.5 5.5

SNHIP 37.0 0.0 20.3 2.0 5.8 2.6 89.6 10.1 7.8 7.8 7.8 7.8

West

Norf

olk Coastal 18.0 0.0 9.3 1.0 2.5 1.6 31.3 3.3 3.3 3.3 3.3 3.3

Fens 26.1 0.0 21.7 1.0 4.4 0.7 50.9 5.6 5.9 5.9 5.9 5.9

King’s Lynn 20.1 0.0 13.3 0.0 3.5 3.5 22.8 3.1 4.7 4.7 4.7 4.7

Swaffham 31.6 0.0 24.0 2.0 5.5 1.8 41.6 5.4 7.3 7.3 7.3 7.3

Total 551.2 19.9 395.7 52.0 118.3 17.2 1079.4 118.9 127.1 127.1 127.1 127.1

Note:1. Subset of Nurses; Differences is from current FTEs and includes projected retirements;Source: BCG Analysis

20%

0%

80%

100%

40%

60%

Staffing mix

Current Model Locality model

Nurses

ANPs

GPs Admin

Physio

MH/Wellbeing

Primary Care

53

PCN Strategy | This can negate the projected gap by 2023

Locality Current Gap Predicted Gap 2023 Gap with locality model

GY&

W

Gorleston 16% 25% -11%

Great Yarmouth 7% 17% -3%

Lowestoft 8% 18% -3%

South Waveney 9% 19% -1%

Nort

h N

orf

olk NN1 7% 17% 2%

NN2 5% 15% -5%

NN3 9% 19% 1%

NN4 9% 18% 0%

Norw

ich

Norwich 1 18% 23% -3%

Norwich 2 18% 22% 9%

Norwich 3 27% 22% 9%

Norwich 4 19% 20% 1%

South

Norf

olk Breckland 15% 24% -1%

Ketts Oak 7% 16% -7%

MID 10% 19% 2%

SNHIP 10% 20% -1%

West

Norf

olk Coastal 5% 15% -6%

Fens 7% 16% -5%

King’s Lynn 8% 18% -12%1

Swaffham 12% 22% 0%

System Wide 8% 18% -4%

Note:1. Poor workforce data quality may skew results for Kings LynnSource: BCG Analysis

With new model capacity gap

eliminated by 2023

0

125

115

110

120

105

20/2118/19 22/23

k apts/week

21/2219/20

Demand

Capacity (Locality Model)

Capacity (Do nothing, current trend)

Primary Care

54

Primary care capacity could

increase by 24% with locality

model

This will make up the current 8%

shortfall and predicted 10%

increase in demand leaving 4%

spare capacity.

An additional 470 FTEs would be

required (~20% increase) 195 from

Primary care, 125 from MH and

125 from MSK by 2023:

• 20 GPs

• 55 Nurses inc 20 ANPs

• 120 Admin Staff

• 125 MH/Wellbeing staff

• 125 Physios and MSK

Additional workforce will cost an

additional ~£20m/yr compared to

~£110m currently by 2023 (~20%

increase).

Some of this increase may be

mitigated by redeployment of

staff from other areas e.g. NSFT

Staffing cost per appointment

however falls by 4%

Demand and capacity Workforce Finance

PCN Strategy | Increasing capacity by ~24% would cost an additional ~£22m by 2023

Primary Care

55

Systems such as nurse led triage,

self care tools and digital

triage/consultation sign post

patients to correct systems

Proactive management of patient

populations reduces prevents

problems before they arise. E.g.

care homes

Interventions can decrease

primary care physician's workload

in two ways:

• Diverting patients to

appropriate services e.g. use

of MH and physiotherapy in

primary care settings

• Decreasing workload per

patient e.g. increased admin

support

By increasing primary care

capacity and capability demand

on other providers can be

reduced E.g.

• Improved end of life care

reduces the requirement for

unplanned admissions

• Enhanced outpatient referral

systems decreases the number

of face to face appointments

required

Reduces primary care demand to

control flows into the system

Supports primary care physicians

to manage current demand

Augments primary care to off-

load downstream system partners

PCN Strategy | Other interventions care can impact primary care but also improve system performance overall

Primary Care

56

PCN Strategy | Selected interventions can impact primary care…

Assumption Value used Method Sources

Impact of Mental Health

provision on GP demand

10% reduction in GP demand to keep

workforce requirements realistic—

0.2 FTE/GP required

GP demand for mental health is 30% of all appointments

according to interviews and publically available sources

Interviews e.g., MH Primary care work stream

Impact of physiotherapy



workforce requirements realistic—

0.2 FTE/GP required

According to available sources between 9 and 32% of GP

appointments are musculoskeletal and could be dealt with

by a physiotherapist

GP Forward view, BCG Case experience

Impact of ANP provision on GP

demand

8% reduction in GP demand to match

lower end estimate—0.15 FTE per GP

required

According to available sources between Advanced nurse

practitioners can see 8-30% of patients instead of GPs


Impact of additional

administrative support

2% reduction in GP demand taken as

low end estimate requiring an

additional 0.1 FTE per GP

By improving administrative support GP time can be freed

up for other tasks. Studies have shown savings of 1-16% of

GP time

Making Time in General Practice—NHS Alliance; BCG case

experience; https://www.lexacom.co.uk/case-studies

Electronic consultation /Triage 2% reduction in PC demand taken as

early stage estimate

Electronic consultation systems elicit patients symptoms

and navigate to most appropriate service reduces demand

by up to 7%

Docklands Electronic consultation service

Rele

asi

ng G

P c

apacit

yPrimary Care

57

PCN Strategy | … but also have impacts on the wider system

Assumption Value used Method Sources

Dem

and m

anagem

ent Impact of increase in primary

care capacity

15% decrease in A&E attendances By matching unmet GP demand this reduces the 8% of

overspill which presents to A&E which is equal to ~15% of

A&E attendances. This has been triangulated with the

%age of inappropriate A&E attendances across the STP

which is 8% higher than national average

GP Patient Survey; HES A&E data

Virtual Hospital Care Pathway 10% reduction in requirement for

outpatient appointments

By using digital consultations and consultant triage

up to 80% of face to face outpatient appointments

can be avoided

Virtual hospital pathway pilots

Impro

ved C

are

Impact of improving cancer

care and diagnosis

0-0.5% Decrease in non-elective bed

days in acute trusts (0.5% taken)

Improving emergency admissions for those with cancer to

national averages for localities who are currently above

this level saves ~310 admissions (~2000 bed days)

National QoF returns;

Impact of improving diabetes

care

0–0.5% decrease in non elective bed

days in acute trusts (0.5% taken)

Addressing patients with HBA1C > 64mmol/mol reduces

absolute risk of admissions in diabetics by 6% and an

additonal 6 per 11mmol/mol increase ~200 admissions

(~1200 bed days)

National QoF returns; Relationship between HbA1c

and risk of all-cause hospital admissions among

people with Type 2 diabetes D. Yu et al Diabetes

Medicine 2013

Impact of improved MH

provision

5% reduction in A&E attendances

and 0-2.5% reduction in non-elective

admissions—mid range values taken

(5 and 1.25% respectively)

Co-located MH pilots have indicated that A&E attendances

fell by 61% and hospital inpatient admissions by 75% in the

~30% of patients with MH complaints (10% and 25%

respectively)

NHS England Guidance for co-located MH services

Primary Care

58

PCN Strategy | Primary care interventions could reduce A&E, IP bed days and OP attendances and save £5m

Baseline

today (K)

PC

component

today (K)

Difference

(K) Variance (%)

A&E

attendances

QEH 63.1 51.2 11.9 18.8

JPUH 77.7 62.1 15.6 20.1

NNUH 131.2 104.9 26.3 20.1

Emergency

spells

QEH 34.6 33.7 0.9 2.6

JPUH 22.8 22.0 0.8 3.5

NNUH 55.4 53.8 1.7 3.0

IP bed days

QEH 161.6 158.5 3.1 1.9

JPUH 150.1 146.4 3.8 2.5

NNUH 343.7 337.2 6.5 1.9

OP

attendances

QEH 303.8 282.9 20.9 6.9

JPUH 270.6 243.6 27.0 10.0

NNUH 773.6 696.4 77.2 10.0

Activity: Reduction in A&E, IP bed days and

OP attendances

Finance: £5M opportunity from primary

care driven by non-electives and A&E

Total STP ImpactNNUH

1.5M

JPUH QEH

4.2M

1.1M

2.4M

1.3M

5.2M

OutpatientsElectives A&EDay casesNon-electives

Primary Care

59

Data sharing enables timely and accurate

information to be available to all members of

the PCN improving effectiveness

Requires common IT systems where possible and

system interlinks where required e.g between

acute services and primary care

A locality, CCG and STP level strategy is

required to ensure inter operability

PCNs will need to identify locations for the

wider work force to see patients and for shared

admin teams to work together e.g.

• GP Practice consultation rooms

• Locality hubs

• Shared business hubs for back office staff

Where this cannot be found due to capacity

limitations locality or CCG level plans should be

put in place to procure adequate provision

Information sharing is key to efficient

working across PCNAdequate estates resources must also be

identified and implemented as a system

PCN Strategy | IT and Estates provision vital to efficient implementation requiring plans at all levels of STP

Primary Care

60

Impact | Primary care deep dive

Demand and capacity impacts System financial impactsSufficient to meet demand in the short term but impacts likely to be outpaced

by growth across the system

Financial impact due to reduced attendances at A&E and

non-elective admissions


Today 5yr view

NNUH

JPUH

QEH

£2.4M £15.0M

£1.3M £9.3M

£1.5M £8.0M

“Do

nothing”

today

S&C

impact

today

S&C

impact

future

4%2% 1%

2%

-1%

-5%

-1%

9%

3%

30% 27%21%

15%

1%

-4%

26%

3%7%

16%6%

IP—

JPUH

A&E—

JPUH

-10%

IP—

NNUH

A&E—

QEH-

IP—QEHA&E—

NNUH

OP—

QEH

OP—

JPUH

OP—

NNUH

7% 8%

-5%

-19%-12%

2%

Primary Care

61

Next Steps | Responsibilities for Primary Care delivery will be split across all levels of governance

Primary care work-stream governance

Primary and community

care workstream

Primary and community care workstream• Overall accountable for delivery

• Reports on progress to the STP Exec.

• Co-ordinates with other governance bodies at the STP level

• Sets the overall ambition, direction and targets for the programme

at a PCN, Place and System level

• Provides oversight for Local Delivery Groups and Community teams

• Tracks and monitors progress at an LDG level

• Unblocks issues at local levels

Local teams• Sets local ambition and direction within each LDG

• Inputs into ambition and design at a PCN, Place and System level

• Drives cultural change at an individual practice level and PCN level

• Outlines and approves local workforce and financial plans

• Responsible for delivery of plans

• Co-ordinates pilot implementation across individual practices

• Monitor outcomes and holds local practices to account

• Reports up to the Primary Care and Community Workstream

• Co-ordinates across community services and strengthens linkages

Mental Health Primary

Care work stream

Mental health primary care

workstream• Responsible for designing

and implementing

MH/Wellbeing provision in

primary care

STP Mental Health

Delivery Group

STP Executive

STP digital

workstream

STP digital workstream• Overall accountable for

delivery of digital components

• Responsible for rolling our

system solutions and system

standardization approaches

• Co-ordinates and oversees

uptake and enablement of

digital solutions

Local

Delivery

Groups

Local

Delivery

Groups

Local

Delivery

Groups

Local

Delivery

Groups

Local

Delivery

Groups

STP estates

planning group

STP estates planning group• Overall accountable for the

strategic estates plan

• Responsible for

implementing capital

projects to ensure

sustainable estates

footprint in primary care

Community

teams

STP Executive

• Sets the strategic STP ambition

• Oversees the Primary and Community care workstream

• Unblocks issues at the STP level

• Approves and ratifies overall direction of the programme

Lead governance body

Primary Care

62

Next Steps | High level roadmap for delivery

Short Term (4 months) Medium Term (1 year) Long Term (2 years)

Primary and

community care

workstream

• Build from current work to set high level

strategic ambitions for the LDGs

• Agree the allocation of activity across PCNs,

Place and System

• Establish linkages with other governance groups

to ensure integrated development

• Identify and begin working with pilot

LDGs and practices

• Re-launch governance approach

• Promote utilization of transparent dashboards

measuring performance (process/operational)

• Ensure implementation cascades through LDGs

at an acceptable pace

• Support and directly manage system processes—

e.g., recruitment campaigns etc.

• Lead negotiations of new models of

commissioning

• Introduce continuous improvement cycles

across LDGs supported by data

• Deliver harmonized access to data across areas

Local Delivery

Groups and

Community teams

• Identify local champions and pilot practices

• Begin cultural change activities

• Communicate central ambition and targets

• Outline bottom up local plans across PCNs

• Set key outcome measures

• Outline requirements for digital and

system solutions

• Develop a workforce strategy accounting for

skill mix and new models of care

• Submit data into central dashboards in a

standardised way

• Locally commission PCN based interventions

• Transition >50% of practices into new system

• Augment linkages with community teams

• Transition 100% of practices into new systems

• Performance manage commissioned services

and local practices

• Launch shared services across primary care

teams to realise further synergies

• Embed data driven continuous improvement

Mental Health

Primary Care

workstream

• Outline new models of mental health care

delivery in primary care

• Ensure secondary care teams are adequately

provisioned to offer services in primary care

• Outline new ways of commissioning services

• Launch in specific pilot sites

• Implement more broadly and embed

continuous improvement cycles

STP Digital

workstream

• Compile list of requirements from broader

governance teams

• IT system audit at PCN level

• Develop data strategy

• Implement central operational dashboards

• Begin to harmonise data systems

• Embed electronic patient records across the

primary care teams and other providers

• Fully implement a harmonized digital solution

across the system

STP Estates

planning group

• Compile list of requirements from broader

governance teams

• Understand the capacity shortfalls

• Outline estates strategy and build plans

• Initiate high value estates plans • Continue to execute against the strategy

• Refresh existing estate

Primary Care

63

Demand & CapacityEstimation | Community and Social

64

There are ~170 medically fit for discharge (MFFD) patients at any one time across all 3

acute trusts that could be cared for in an alternative, lower cost setting• 49 at NNUH, 57 at JPUH and 61 at QEH (although ~40% at QEH are out of area placements)



• If ~85% of MFFD patients could be moved to a community setting, ~43.5K acute bed days could be freed

(~7% of total IP bed day)

MFFD patients could be supported by a number of intermediate care bed settings or bed

equivalents which include a mixture of:• Intermediate care/reablement beds – ~130 beds would be required today rising to ~150 beds in 5 years

• Community virtual ward care - ~135 additional FTEs would be required today rising to ~160 FTEs in 5 years

• NFS reablement packages at home - ~10K reablement packages required today rising to ~11.7K in 5 years

The cost arbitrage opportunity indicates a net savings would be £6M• The removal of MFFD bed days from acutes creates a £13M gross savings opportunity for the acutes

• Approximately £7M investment required in intermediate care beds to meet the MFFD demand in

alternative setting

A detailed bottom-up analysis of MFFD patients and their care needs will be required to

determine the best and correct mixture of intermediate care services

Social & communitycare |Primary narrative

Community & Social

65

….which suggest ~8% of total bed days could be managed downstream

5%

651

149

8%

Number of bed days (K)

95%

NNUH

14%

86%

JPUH

8%

92%

QEH

92%

Total

340

161

MFFD days Non-MFFD days

Avg. numer of MFFD

at any one time

Avg. LoS

as MFFD

49 2.3NNUH

57 8.3JPUH

61 8.0QEH

Total 167 4.3

Source: NNUH MFFD data (Sep 17–Oct 18), JPUH MFFD data (Sep 17–Oct 18) & QEH MFFD data (Oct snapshot)

Context | ~170 MFFD patients at any one time across the acutes….

Community & Social

66

Alternative models | MFFD analysis shows ~10K MFFD patients could be moved

Note: # MFFD patients calculated by dividing # MFFD patients at any one time by Avg LoS and multiplying by 365 Source: NNUH MFFD data (Sep 17 – Oct 18), JPUH MFFD data (Sep 17 – OCt 18) & QEH MFFD data (Oct snapshot)

131

Today view

TotalSuitable

for shift

Likely that solution will involve a mixture of all three

services—additional bottom up analysis of MFFD patient

requirements needed

NNUH 49 7.7K

57 2.5K

35 1.6K

141 12K 120 10K

Beds (at 91%

occupancy rate)

135Virtual/Home

Ward FTEs

400Norfolk First

Support FTEs

Assumes MFFD demand grows in line

with overall spell growth at each acute

Five year view

152Beds (at 91%

occupancy rate)

158Virtual/Home

Ward FTEs

468Norfolk First

Support FTEs

JPUH

QEH

XX Number of MFFD patients at any one time XX Number of Total MFFD patients

Assume ~15% of MFFD beds not

suitable to be moved e.g., still

waiting for hospital tests or

assessments—to be tested

QEH has total of 69

MFFDs at any one time

but ~40% are placed OOA

Community & Social

67

Opportunity | Increasing social/community care could reduce bed days at existing providers by ~50K and provide net savings of £13M

Activity: Existing providers can reduce bed

days by ~50K

Finance: £13m opportunity from shifting

MFFD to lower cost care setting

Final end

baseline

Revised

baseline

Number of bed days/year (K)

Social Care/

Community

Baseline Intermediate

Care Setting

742

-50

69244 735

QEH

NCHC

NNUH

JPUH

ECCH

Intermediate care bed/bed equivalent

Total STP

Impact

NNUH JPUH QEH Investment

8.5m

7.9m

5.6m

-9.0m

13.0m

Electives

Non-electives

Day cases

Outpatients

A&E

Investment

NCHC and ECCH

demand would also

be reduced due to

transfer of DTOCs to

intermediate setting

Community & Social

68

Impact | Social and Community impacts

Demand and capacity impacts System financial impactsSufficient to meet demand in the short term but impacts likely to be outpaced by growth

across the system—in particular intermediate care bed capacity needs to be expanded to

meet growing demand

Financial impact due to cost difference between

inpatient and community bed equivalents


Requires ~£9–11M investment in

intermediate care bed equivalents

Today 5yr view

NNUH

JPUH

QEH

£8.2M £10.5M

£7.9M £11.9M

£5.6M £5.5M

-8%

N/A

-5%

-1%

1%

0%

6%

14%

-1% 0%

-12%

IP—NCHCIP—QEH

-11%-16%

-14%

-20%

IP—JPUH IP—NNUH Intermediate

Care Bed/Bed

Equivalent

“Do

nothing”

today

S&C

impact

today

S&C

impact

future

ICB bed demand will grow—

assume additional capacity

will be provided otherwise

capacity variance will lead to

additional bed days in acutes

Community & Social

69

Short term Medium term Long term

• Establish working group with

representation from acutes,

community & social care

• Mobilise clinical leadership to co-

develop plans and engage front line

clinical staff in the most effective way

• Conduct clinically led bottom-up

assessment of MFFD patients at all 3

acutes to validate opportunity and

determine service needs

• Develop high level strategy plan

including workforce and financial

implications

• Identify key services and new

pathways to pilot in specific high-need

areas

Next steps

• Identify funding streams

• Implement pilot plans with concrete

outcome measures

• Start implementation of recruitment

plans

• Roll out plans across STP

• Continue to monitor and refine

outcome metrics

Community & Social

70

Demand & CapacityAppendix

71

Approach | Key modelling principles

Select core input growth rates used to determine others• Core input growth rates agreed

• Used to determine growth rates further downstream

Flat line growth applied for 5 year forward view

Component impacts applied to baseline today and then

projected forward

Baseline demand includes activity and unmet demand• Includes RTT clearance for elective IP activity, day case and OP

• Includes DTOCs for community providers

Base case conversion rates assumed to remain constant in

projection forward

72

Backup| Primary care component assumptions

Intervention Value used Method Impact in D&C Model

Impact of increase in primary

care capacity

15% decrease in A&E attendances1 By matching unmet GP demand this reduces the 8% of

overspill which presents to A&E which is equal to ~15% of

A&E attendances. This has been triangulated with the %age

of inappropriate A&E attendances across the STP which is

8% higher than national average


• Taken from "GP overspill" bucket within self-

referral category

• Assume that all attendances would have been

discharged as inappropriate admissions –

conversion rates adjusted accordingly

Impact of improving cancer

care and diagnosis

0-0.5% Decrease in non-elective bed

days in acute trusts (0.5% taken)2

Improving emergency admissions for those with cancer to

national averages for localities who are currently above

this level saves ~310 admissions (~2000 bed days)

1% reduction in emergency admissions

• Taken from GP referrals to emergency admissions

Impact of improving diabetes

care

0–0.5% decrease in non elective bed

days in acute trusts (0.5% taken)2

Addressing patients with HBA1C > 64mmol/mol reduces

absolute risk of admissions in diabetics by 6% and an

additonal 6 per 11mmol/mol increase ~200 admissions

(~1200 bed days)

Virtual Hospital Care Pathway 10% reduction in requirement for

outpatient appointments3

By using digital consultations and consultant triage up to

80% of face to face outpatient appointments can be

avoided

10% reduction in total OP attendances

• Taken from GP referrals

• Assume no OP appointments would be admitted –

conversion rates adjusted accordingly

Impact of improved MH

provision


and 0-2.5% reduction in non-elective

admissions – mid range values taken

(5 and 1.25% respectively)4

Co-located MH pilots have indicated that A&E attendances

fell by 61% and hospital inpatient admissions by 75% in the

~30% of patients with MH complaints (10% and 25%

respectively)


• Apportioned across all admission sources

1.25% reduction in e emergency admissions

• Taken from GP referrals to emergency admissions

For further detail, see Primary

Care Deep Dive pack

Source: 1. GP Patient Survey 2. Analysis of QOF submissions 3. Virtual Hospital Pathway pilot 4. NHS England

73

Backup| Acute component assumptions


Impact of LoS efficiency

improvements in elective

activity

1% total LoS reduction on system:

• 4315 elective bed days at JPUH

• 815 elective bed days at NNUH

• 8 elective bed days at QEH

Model hospital analysis of LoS by specialty identified LoS

variations

After accounting for acuity differences (i.e. excluding high

acuity specialties), LoS opportunities were identified by

reducing LoS to the lowest LoS achieved across the three

sites

JPUH, NNUH and QEH average LoS adjusted by

reducing FY17/18 total bed days by bed day

opportunities and recalculating lower LoS

Revised LoS then applied to total # spells

Unit cost reduction £43M cost opportunity identified

from Model Hospital analysis

Model hospital analysis of Cost/WAU identified variations

within selected specialties by point of delivery across the 3

acute sites

Cost/WAU was reduced to the best internal benchmark

across the three actues to calculate average potential %

savings opportunity for each acute by POD

Theoretical financial opportunity calculated from

applying % savings opportunity by trust by POD to the

FY18/19 baseline expenditure and applied to

FY18/19 activity projections

For further detail, see Acute

Deep Dive pack

74

Backup| Social & Community Care component assumptions


Shift MFFD patients to

intermediate care setting –

Acute impact

Bed day reduction of:

• 15.2K bed days – NNUH

• 17.6K bed days – JPUH

• 10.8K bed days – QEH

# MFFD bed days estimated using trust data on average #

MFFD patients at any one time and avg LoS as an MFFD

patient

Assume that only 57% of QEH MFFD patients are placed in

N&W and that overall 15% of MFFD patients are not suitable

to be moved

JPUH, NNUH and QEH average LoS adjusted by

reducing FY17/18 total bed days by MFFD bed days to

be shifted and recalculating lower LoS

Shift MFFD patients to

intermediate care setting –

Social/Community impact

Bed day increase of 43.5K bed days

or 10K spells

Intermediate care bed calculated using total bed days to

be shifted at occupancy rate of 91%

Virtual Ward FTE equivalent calculated using NCHC Virtual

Ward & Home Ward data Oct 17 – Sep 18

• 10K spells require additional ~190 contacts / day

• NCHC FTEs have capacity of ~1.4 contacts / day

• Therefore ~135 additional FTEs required

Norfolk First Support (NFS) FTE equivalent calculated:

• Total NFS packages required (one per spell – 10K)

• NFS FTEs currently handle ~25 packages / year

• Therefore additional ~400 FTEs needed

Intermediate care bed setting increased by:

• 131 beds; or

• 135 Virtual/Home Ward FTEs

400 NFS FTEs

Community DTOCs removed from demand baseline as

assumed that DTOCs would be met in new

intermediate care setting (and form part of overall

MFFD bed days)

For further detail, see Social &

Community pack

75

Backup: Interventions would result in ~9% bed day reduction today across existing providers

800

600

0

200

400

Intermediate

Care

addition

# Bed days / year (K)

Acute

scenario

742

150

344

751011

-5

Correction

from

combining

scenarios

147

716

321

Existing

provider

baseline

125

147

125

-50

321

6910

Primary care

scenario

44

Final system

baseline

162-14

Community /

Social

scenario

Baseline

demand

- Today

0

0 67344

69

-9%

QEH NNUH

Intermediate care bedJPUH NCHC

ECCH

Total bed day

reduction (K)

% of Total bed

day baseline

QEH 14.7 9.1%

JPUH 25.1 16.7%

NNUH 22.2 6.5%

NCHC 6.3 8.4%

ECCH 0.6 5.5%

COMBINED COMPONENTS

76

System issues| Although two acutes have acceptable temporary staffing levels, QEH remains an outlier with recruitment and retention issues

Source: 2017/18 Trust Annual Reports; NHS Model Hospital

7.23%

10.70%

3.01%

5.29%

10.37%

0

5

10

15

NNUH

Agency Spend Staff Cost (%)

QEHJPUH

5.1%

National

median

6.73%

-55%-27%

-3%

2016/17 2017/18

0

100

200

400

300

Contribution of Agency Cost to WAU (£)

£260

£340

+31%

National median QEH

QEH's temporary staffing costs remain high This is reflected in temp staffing costs per WAU

77

System issues| Two trusts in special measures, rated inadequate by CQC

NNUH(2018)

JPUH(2016)

QEH(2018)

Area ratings Inadequate Good Inadequate

Safe Inadequate Rq. Improvement` Inadequate

Effective Rq. Improvement Good Rq. Improvement

Caring Good Good Good

Responsive Rq. Improvement Good Rq. Improvement

Well led Inadequate Good Inadequate

Specific service ratings Inadequate Good Inadequate

Critical care Good Good Good

OPD & Diagnostics imaging Rq. improvement Good

Urgent and Emergency Inadequate Good Inadequate

Outpatients Rq. Improvement Rq improvement

Maternity Rq. Improvement Good Inadequate

Medical Good Inadequate

Diagnostic imaging Rq. Improvement Rq. Improvement

Surgery Inadequate Good Rq. Improvement

Children Good Good

End of life Rq. Improvement Good Rq. Improvement

Source: CQC Data and NHS Digital from September 2018

Two Trusts rated

as inadequate

raising quality

concerns across

the system.

System working

could help

address some of

the quality

issues

78

Integration | Achieving internal STP benchmark1 cost/WAU for each specialty/POD across the 3 Trusts has theoretical upside in region of 10%

1. Based on internal STP benchmark; achieving lowest cost/WAU per specialty/POD combination from 3 acute trusts 2. Assumes Treatment Function Codes are consistent across all TrustNote: Analysis based on available specialties: excludes Breast Surgery, Dermatology, ENT, Medical & Clinical Oncology, Plastics & Burns and rheumatologySource: Model Hospital Data FY16/17

% cost/WAU

opportunity1 Elective Non-elective Day case Outpatient Other Total

Specialty NNUH JPUH QEH NNUH JPUH QEH NNUH JPUH QEH NNUH JPUH QEH NNUH JPUH QEH NNUH JPUH QEH

Cardiology 0% -19% -47% -3% -4% 0% 0% 0% -59% 0% -66% -18% -9% 0% -32% -2% -18% -14%

Diabetes & Endocrinology 0% 0% -33% -15% 0% 0% -40% 0% 0% 0% 0% -27% 0% 0% -17% -11% 0% -12%

Gastroenterology 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

General Medicine 0% 0% -45% -5% -1% 0% 0% 0% 0% -26% -15% 0% 0% -5% -24% -6% -2% 0%

Geriatric Medicine 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Neurology 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% -2% -7% -9% 0% -11% -2% -2% -8%

Paediatrics -23% 0% -13% 0% -20% -37% -51% 0% -68% -17% -25% 0% -17% 0% -19% -14% -21% -26%

Respiratory -30% 0% 0% 0% 0% 0% -55% 0% 0% -13% 0% 0% 0% -4% 0% -6% 0% 0%

Stroke -8% 0% -55% 0% -52% -53% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% -52% -53%

General Surgery 0% -22% -19% -3% -15% 0% -15% 0% -27% -9% -50% 0% 0% -14% -25% -6% -17% -10%

Orthopaedic Surgery -28% 0% -22% 0% -16% -5% -30% 0% -36% 0% -30% -13% -4% 0% -7% -13% -13% -15%

Urology -2% 0% -10% -23% 0% -19% -9% 0% -26% -3% -37% 0% 0% -9% -17% -8% -14% -15%

Obstetrics & Gynaecology -6% 0% -20% 0% -13% -19% -29% 0% -27% -4% 0% -10% -32% 0% -33% -3% -7% -18%

highestlowest

79

Integration | 29,000 bed days may be saved across the system if lowest LoSbetween sites and PODs is reached

Reduction in bed days Elective Non-elective


Total bed days 62,311 17,546 16,778 27,214 6,721 10,438

Cardiology – - 8 - - 394

Dermatology - 2 - - - -

Diabetes & Endocrinology - - - 680 - -

Gastroenterology 617 - - - - -

General Medicine - 114 - - 9,850 2,384

General Surgery - 734 - 2,439 - 1,448

Geriatric Medicine - - - - - -

Obstetrics & Gynaecology - 2,069 - - - 20

Orthopaedic Surgery - 1,283 - - 3,271 87

Paediatrics 198 113 - 2,288 - 539

Respiratory - - - - 45 -

Rheumatology - - - - - -

Urology - - - - - -

Total bed day reduction

opportunity815 (1%) 4,315 (25%) 8 (0%) 5,407(2%) 13,167(10%) 4,872(4%)

Note: Analysis based on available specialties;1. Weighted average of each trust reduction by total bed days; This assumes TFCs are reported in a standard waySource: Model Hospital Data FY16/17; HES bed days data

highestlowest

80

Integration | Analysis indicates a integrated ways of working could save £45-120m, based on high level estimates

Clinical staff Support Staff Consumables IT/Overheads

Acute Trusts Cost £450m £70m £220m £30m

↓ 1-5%

£4-22m

↓ 1-5%

£1-3m

↓ 1-5%

£2-11m-

↓ 5-10%

£22-45m

↓ 8-10%

£5-7m-

↓10-12%

£2-3m

↓ 1-5%

£5-22m

↓ 1-10%

£1-7m- -

Total Benefit £32-89m £7-17m £2-11m £2-3m

Note: This forms supporting evidence to illustrate merger benefits (mutually exclusive from other exhibits) 1. Totals may not add up to sum due to rounding; 2. Cost savings from the positive effect of scale on quality, leading to lower volume per patient; Reductions based on BCG Case studies, expert interviews

Clinical effectiveness2

More efficient

processes

Better FTE

utilization

Higher

ProductivityValu

e o

f sc

ale

Exhibit 1

81

Integration | Overview of evidence and corrections applied per value lever

Value lever

for scale Clinical staff Support staff Consumables IT/Overheads Corrections

ClinicalEffectiveness

Assumption: Reduction in volume leads to same reduction in staff and

consumables• ~50% reduction in LoS when doubling volume of knee/hip surgeries

(BCG case at NL hospital)—estimate• 8% overall reduction in LoS estimated for the clinical

reconfiguration of a German hospital (~6 years to achieve) (BCG case)—estimate

• ~5–36% reduction in revision rate for total hip arthroplasty reduces as result of scale; reduction depends on baseline (Australian Orthopedic Association)—backward looking

• ~20% savings in admin/clerical staff

• 24% of total cost synergies in healthcare M&A come from central functions, e.g.,

– Travel/transport

– Finance

– IT

– Legal• (BCG analysis on 13 healthcare M&A

cases)—backward looking

Correction of 10% for total range (5–50%) because

• Expenses not wholly driven

by these factors

• Probability of achieving

scale

• Extrapolation across

specialties

• Assumptions on as-is

Lower range: 5% * 0.1 = 1%

Upper range:50%*0.1 =5%

More efficient processes

• ~10% efficiency savings (BCG case experience)—estimate

• 45% reduction in OR time of knee/hip surgeries (BCG case at NL hospital)—estimate

• 10% efficiency savings (BCG case experience)—estimate

• 16% reduction in process time for support staff (BCG case experience)—estimate

N/A Corrections

• 10% on OR time, as scale

effect is highly variable

between specialties

• 50% for support staff

process time due to

limited observation

Lower range:Clinical: 0.1*45 = 5% Support:0.5*16% = 8%Upper range: 10%

Better asset and workforce utilization

• 1% of staff cost in theatre, critical care,and anaesthetics

• 5% reduction of on-call rota for Clinical and Ops staff

N/A No corrections applied Lower range: 1%Upper range: 5%

Exhibit 2

Note: This forms supporting evidence to illustrate merger benefits (mutually exclusive from other exhibits)

82

Integration | Trust-level scale curve suggests potential for cost reduction by integrating trusts

1. Peer group consists of ~60 UK foundation and non-foundation trusts; data 2016–2017 2. Single site is defined as having 1 site in HSJ intelligence data 3. BCG case experienceNote: This forms supporting evidence to illustrate merger benefits (mutually exclusive from other exhibits). Theoretical cost savings potential likely not fully achieved due to other factors impacting cost (e.g., case complexity). No observations of >1,000 beds, hence marginal improvement could be smaller Source: HSJ Intelligence; NHS beds report Q4 2017; Dept Health Social Care; BCG analysis

Net 11-14% cost saving potential when volume tripled12.0

0.0

0.5

1.0

1.5

Avg. expense

per bed (£M)

Number of Beds

R2 = 0.54

Scale factor = 28%

p < 0.0001

Combined trust 1800 beds

Theoretical potential: 22-28%

Efficiency potential: 11-14%

300 600 900 1200

• Discounted theoretical

potential by 50% to

account for limited data

over 1000 beds

Exhibit 3

83

Integration| Real world evidence on impact of scale benefits indicate potential savings of ~£26M (2%) to ~£130M (10%)

Findings Impact

Decrease in costs per patient as a result of more integrated ways of working between two German (orthopaedic and traumatology surgery departments into one centre (Source: Z Orthop Unfall, sept. 2016)

Realized cost savings after increasing integration, based on a meta-analyses that included data on 476 hospitals intergrations from US,GB, and Norway between 1982–2000. Highest cost reductions realized when hospitals <200 beds (Source: Tidsskr Nor Laegeforen, 2010)

Estimated synergy benefits from merger of two UK acute providers merger through purchasing at scale, elimination of duplicate equipment, lower costs to serve and back-office synergies from corporate and support services (excluded further potential savings from clinical reconfiguration) (Source: BCG case experience)

Realized cost savings after increasing integration, based on large sample of hospital integrations between 2000–2010. Results varied depending on e.g., location of acquiring system (lower when nearby) or acquisition by multi-hospital1 system (higher saving) (Source: Schmitt, UCLA Anderson, 2017)

Decrease in operating expense based on an econometric analysis (statistically significant) on 375 hospital integrations and acquisitions (Source: Charles River Associates, 2017)

Adjusted total impact (scaled down by 70%) 2-10%

Note: This forms supporting evidence to illustrate merger benefits (mutually exclusive from other exhibits) 1. American Hospital Association defines a multihospital system as two or more hospitals owned, leased, sponsored, or contract managed by a central organization. Correction: Savings incl. site-independent drivers, such as procurement, for which we correct with 30% (Based on BCG Case experience during hospital integration evalutions), We exclude the negative result (-8%) as the cost increase seems to be driven by the way the integration was executed. Note: May want to complement findings with in-depth case studies of comparable consolidations, e.g., Glasgow, Karolinska

~14%

~10%

~4-7%

~2.5%

Exhibit 4

~10%

84

PCN differences | Requirements to reach national average workforce vary by locality

Locality GPs Nurses DPC1 AdminRequired2 Difference Required2 Difference Required2 Difference Required2 Difference

GY&

W

Gorleston 25.0 7.2 11.9 1.6 7.8 0.0 27.0 0.0

Great Yarmouth 36.0 (6.0) 25.9 0.0 15.4 0.0 28.9 0.0

Lowestoft 44.8 8.2 29.0 0.0 8.7 0.0 61.4 0.0

South Waveney 30.1 4.5 28.0 0.0 11.5 0.0 81.5 0.0

Nort

h N

orf

olk NN1 23.5 (5.0) 22.0 0.0 34.1 0.0 28.5 0.0

NN2 22.8 2.3 13.2 0.0 19.2 0.0 65.2 0.0

NN3 25.8 1.2 17.4 0.0 28.8 0.0 60.6 0.0

NN4 26.3 1.8 20.6 0.0 20.4 0.0 66.1 0.0

Norw

ich

Norwich 1 35.1 6.5 18.8 0.0 6.2 0.0 59.5 0.0

Norwich 2 37.8 4.2 18.0 1.8 9.4 0.0 49.2 0.0

Norwich 3 26.1 7.5 16.5 0.0 6.5 0.0 21.4 0.0

Norwich 4 34.5 (-0.2) 19.1 0.0 8.7 0.0 31.4 0.0

South

Norf

olk Breckland 23.9 5.4 14.0 0.0 1.0 0.0 27.2 0.0

Ketts Oak 43.0 7.5 25.8 0.0 36.5 0.0 90.3 0.0

MID 25.9 1.5 26.3 0.0 19.0 0.0 53.4 0.0

SNHIP 36.4 (0.5) 20.3 0.0 36.9 0.0 79.5 0.0

West

Norf

olk Coastal 15.4 (2.7) 9.3 0.0 11.9 0.0 28.0 0.0

Fens 27.7 1.5 21.7 0.0 20.8 0.0 45.4 0.0

King’s Lynn 22.1 2.0 13.3 0.0 5.3 0.0 19.7 0.0

Swaffham 34.4 2.8 24.0 0.0 34.2 0.0 36.2 0.0

Total 596.4 49.6 395.5 3.8 342.4 0.0 960.5 0.0

20%

25%

0%

5%

10%

15%

GP

% Staffing shortfall

AdminNursing DPC

Percent of staff needed to be recruited to

meet target today

Notes: 1 Direct patient care; GP target to national average; Nurses target to maximum of national average or current; Others remain constant;Awaiting data to split into 20 localities from 19 displayed total will not be affectedSource: NHS Digital, NHS GP Practice Profiles

85

PCN differences | Ageing population driving growth in demand especially in GY&W and North Norfolk

Note: Demand growth calculated appointment demand by age applied to Norfolk population and aggregated by region + 1% adjustment from GP Forward viewSource: ONS Population data; Scottish GP Contacts; GP Forward View

Demand increasing faster than population growth

2%

0%

1%

4%

3%

% Growth

STPNorwich South

Norfolk

GY&W West

Norfolk

North

Norfolk

Demand Growth Population Growth

• Great Yarmouth and Waveney has large demand

growth driven by increases in both 60-65 and 75+

age groups

• North Norfolk's demand disproportionate to

population growth due to over 65s growing by

~1%pa more than population growth – second

largest disparity in STP after GY&W

• Norwich, South Norfolk and West Norfolk demand is

growing in line with the STP in general with over

65s growing ~0.8% faster than general population

86

Differences in

demographics

across CCGs drive

differing service

needs and future

growth

Staffing levels vary

between CCGS and

PCNs requiring

different levels

and prioritization

of recruitment

Large variation in

performance

across CCGs and

PCNs require

bespoke targets

for each locality

Combines multiple

factors to form

bespoke PCN level

plans

Demographics Workforce Outcomes Service model

PCN Strategy | Variation in demographics, workforce and performance influence a PCN model

87

Backup | Primary Care Network maturity matrix

Excerpt from

NHSE guidelines relating

to effective working

across Primary Care

Networks—currently

self reported levels of

maturity place most

local groups in Step 1

Foundations for

transformation Step 1 Step 2 Step 3

Right

scale

Plan: Plan in place articulating

clear vision and steps to getting

there, including actions at network,

place and system level

Engagement: GPs, local primary

care leaders and other stakeholders

believe in the vision and the plan to

get there

Time: Primary care, in particular

general practice, has the headroom

to make change

Transformation resource: There

are people available with the right

skills to make change happen, and a

clear financial commitment to

primary care transformation

Practices identify PCN

partners and develop shared

plan for realisation

Analysis on variation in outcomes

and resource use between practices

is readily available and acted upon

Basic population segmentation is

in place, with understanding of

needs of key groups and their

resource use

Integrated teams, which may not

yet include social care and voluntary

sector, are working in parts of the

system

Standardised end state models of

care defined for all population

groups, with clear gap analysis to

achieve them

Steps taken to ensure operational

efficiency of primary care delivery

and support struggling practices

Primary care has a seat

at the table for system strategic

decision-making

PCNs have defined future business

model and have early components

in place

Functioning interoperability within

networks, including read/write

access to records, sharing of some

staff and estate

All primary care clinicians can

access information to guide

decision making, including risk

stratification to identify patients for

proactive interventions, IT-enabled

access to shared protocols, and real-

time information on patient

interactions with the system

Early elements of new models of

care in place for most population

segments, with integrated teams

throughout system, including social

care, the voluntary sector and easy

access to secondary care expertise.

Routine peer review

Networks have sight of resource

use and impact on system

performance, and can

pilot new incentive schemes

Primary care plays an active

role in system tactical and

operational decision-making,

for example on UEC

PCN business model fully

operational

Fully interoperable IT, workforce

and estates across networks,

with sharing between networks

as needed

Systematic population health

analysis allowing PCNs to

understand in depth their

populations' needs and design

interventions to meet them,

acting as early as possible to

keep people well

New models of care in place for all

population segments, across system.

Evaluation of impact of early-

implementers used to guide roll out

PCNs take collective responsibility

for available funding. Data being

used in clinical interactions to make

best use of resources

Primary care providers full decision

making member of ICS leadership,

working in tandem with other

partners to allocate resources and

deliver care

Integrated

working

Targeting

care

Managing

resources

Empowered

Primary

Care

88

PCN Strategy | Reduce demand entering primary care

Note: Items in bold chosen for Phase 1 model1. Equivalent of ~1% reduction, based on care home population of ~0.5% with appt demand of 400/1k pop per week (~2x estimated contact rate for patients >75)Source: Holt et al., 2016 (https://doi.org/10.3399/bjgp16X684001); https://www.england.nhs.uk/wp-content/uploads/2016/03/releas-capcty-case-study-2-163.pdf; https://www.england.nhs.uk/wp-content/uploads/2016/03/releas-capcty-case-study-1-145.pdf

Strategy Description Supporting examples ImpactE-Consultations E-Consultation systems elicit patients symptoms and

navigate to most appropriate service

Docklands E-Consultation system

• 18% of patients used system

• 40% completed remotely in 2.9 min

• 20% resolved over phone in 5.5 min

• 40% resolved in 10 min appointment

• Up to 7% reduction in GP

demand per week

Shared triage A single locality hub manages triage system

across multiples practices, with interoperable

systems and appointment pooling

Primary Care Network —Shared access hub for patients

Modality partnerships—Centralised triage

• Demand redistribution and

flexible capacity

Enhanced care home services Additional clinical support to people in nursing and

care homes

Wirral Care home GP service

• 3 practices employed a GP for six sessions a week to manage their care

home

• Single lead GP provided continuous proactive service

• Up to 26% reduction in requests

for GP visits from care homes1

Self Care Tools & Apps Provide support for patients to self-care and undertake

behavioural change programmes

OurPath App

• Behavioural change programme to reduce diabetes prevalence

• Up to 50% reduction in risk of

developing type 2DM for ‘at risk'

groups

Nurse-led telephone triage Nurses, supported by clinical decision systems review

all appointment requests

ESTEEM trial, 2016

• Avg. patient-GP contact duration reduced (9.1 to 7.7 min)

• Avg. patient-nurse contact duration increased (0.6 to 7.1 min)


demand per week

AI-based triage/

NHS 111 online

Patients calling GP practice are redirected to NHS111

who triage and book appointments

NHS111 Online trials (e.g., Senseley/Babylon)

• Redirect 40% of patients to PC, versus ~60% for

telephone based 111

• Up to 30% reduction in referrals

to PC vs. standard NHS111

Selected interventions

https://doi.org/10.3399/bjgp16X684001

https://www.england.nhs.uk/wp-content/uploads/2016/03/releas-capcty-case-study-2-163.pdf

89

Strategy Description Supporting examples ImpactUse of wider workforce Use of wider workforce to see patients who do not

require GP e.g.

• Mental health & wellbeing

• Physiotherapy

GP demand for mental health is 30% of all appointments according to

interviews and publically available sources. Audits have also shown

between 9 and 30% of GP appointments could be dealt with by a

physiotherapist and 8-30% by ANPs and extended role nurses.


demand due to MH/Wellbeing

provision

• 9-30% reduction in GP demand

due to Physiotherapy support

GP Assistant (Admin) Manage bureaucracy that does not require a GP

• E.g., data entry, hospital bookings, reviewing

normal blood test results

Brighton and Hove

• Est saving of 40 minutes per day per GP

• Assistant spends 3 hours per week for every 5k patients

• Up to 16% increase in capacity

per GP1

Digital solutions Deploy technology solutions to increase efficiency and

automate administrative tasks

• E.g., e-Dictation software, patient online self-

service tools, text and email correspondence to

patients and specialists

Digital dictation software

• E.g., Improved transcribing accuracy,

reduces admin time

• Up to 20 hours of admin time

released per practice per week

at one practice

Improved interface

with the acutes

Improve communications between hospital doctors and

GPs

• E.g., more timely and useful discharge letters,

access to specialists, re-booking hospital appts

Brighton

• Redesigned discharge letters

• Teledermatology services to allow GPs to get diagnosis and

management plan from consultant dermatologist

• 1-3% increase in

capacity per GP2

Shared back-office Centralised administrative functions within a single

hub

• E.g., shared payroll, pooled administrative staff

South Cheshire and Vale Royal GP Alliance

• 30 practices using technology to enable shared secretarial pools,

remote working and outsourcing of admin functions

• Efficiency savings

from pooled back-office/admin

roles

Improve communication

with patients

Reduce number of appointments which DNA, allowing

for more productive use of clinician time

Text messaging App

• Improved patient communication and appointment reminders

• Potential to reduce est. 4% of

GP appts DNA'd1

Note: Items in bold chosen for Phase 1 model1. 40 minutes = 4 extra appts per day (4/25 appoiintments = 16%) 2. National audit (3% acute hosp. gen demand); M&SE audit(1% acute hosp generated demand)Source: http://www.nhsalliance.org/wp-content/uploads/2015/10/Making-Time-in-General-Practice-FULL-REPORT-01-10-15.pdf; GP Forward View 2016 (Audit of ~5000 GP consultations); 2016 Audit of practices in five localities in Mid and South Essex(~1400 consultations); https://www.lexacom.co.uk'; https://www.lexacom.co.uk/case-studies/

PCN Strategy | Support physicians to manage demandSelected interventions

http://www.nhsalliance.org/wp-content/uploads/2015/10/Making-Time-in-General-Practice-FULL-REPORT-01-10-15.pdf

https://www.lexacom.co.uk/

90

Strategy Description Supporting examples ImpactImproved LTC case management Reduce future demand by improving case finding and

management of Long term conditions

N&W Clinical outcome modelling3

• Potential reduction of 30 strokes per year by improving AF

treatment

• Reduction in emergency admissions from cancer by 300

• Reduction in emergency admissions in diabetics by 200

• Reduction of up to 1% non-

elective acute bed days

Virtual Hospital Care Pathway By using advice and guidance and consultant triage

most face to face new outpatient appointments can

be avoided.

Virtual hospital pathway pilots reduced new outpatient appointments by

prescreening and offering advice and guidance

• Up to 80% reduction in

requirement for new

outpatient appointments

Improved MH provision By implanting MH provision in primary care improved

access and care

Co-located MH pilots have indicated that A&E attendances fell by 61% and

hospital inpatient admissions by 75% in the ~30% of patients with MH

complaints.

• 0-10% reduction in A&E

attendances and 0-2.5%

reduction in non-elective

admissions2

Enhanced EOL pathways Integrated health and social care services at home,

with discharge support from hospitals

Marie Curie's Nursing Service

• Nurses and HCA's deliver specialist support for EOL based on individuals

care plan

• ~£500 lower costs per person

(inc. acute, social, primary and

community care)

Note: Items in bold chosen for Phase 1 model1. Estimate based on audit of SystmOne data from practices across M&SE in Source: Roche, 2014; Shifting the Balance of Care (Nuffield, Mar 2017);

https://www.mariecurie.org.uk/professionals/commissioning-our-services/why-marie-curie/impact; 2. Guidance for commissioning co-located MH staff

PCN Strategy | Offload downstream servicesSelected interventions

https://www.mariecurie.org.uk/professionals/commissioning-our-services/why-marie-curie/impact

91

PCN Strategy | Selected interventions will become model inputs

Assumption Value chosen Method Sources

Impact of Mental Health



workforce requirements realistic –

0.2 FTE/GP required

GP demand for mental health is 30% of all appointments

according to interviews and publically available sources

Interviews e.g., MH Primary care work stream

Impact of physiotherapy



workforce requirements realistic –

0.2 FTE/GP required

According to available sources between 9 and 32% of GP

appointments are musculoskeletal and could be dealt with

by a physiotherapist


Impact of ANP provision on GP

demand

8% reduction in GP demand to

match lower end estimate – 0.15

FTE per GP required

According to available sources between Advanced nurse

practitioners can see 8-30% of patients instead of GPs


Impact of additional

administrative support

2% reduction in GP demand taken as

low end estimate requiring an

additional 0.1 FTE per GP

By improving administrative support GP time can be freed

up for other tasks. Studies have shown savings of 1-16% of

GP time.

Making Time in General Practice – NHS Alliance ; BCG

case experience; https://www.lexacom.co.uk/case-

studies/

E-Consult/Triage 2% reduction in PC demand taken as


E-Consultation systems elicit patients symptoms and

navigate to most appropriate service reduces demand by

up to 7%

Docklands E-Consultation service

Improved LTC case

management

1% reduction in non-elective acute

bed days

Improved capacity allows better quality of care resulting in

reduction in strokes, cancer and diabetic admissions

Analysis of QOF returns

Virtual Hospital Care Pathway 10% reduction in outpatient

appointments taken as conservative


Virtual hospital pathway pilots reduced new outpatient

appointments by prescreening and offering advice and

guidance

Virtual Hospital pilot sites

92

Backup | Additional workforce costs estimated at £23m by 2023

Unit cost1 FTEs required by 2023 Total

GP £111,000 20 £2.2m

Nurses £48,000 52 £2.5m

DPC £30,000 26 £0.8m

Admin £24,000 119 £2.9m

MH/Wellness £48,000 127 £6.1m

Physios £66,000 127 £8.4m

Total £22.8m

1.Total Cost; Source: Estimates based off NHS workforce data; Previous BCG case experience;

93

Backup: MFFD patient calculations

NNUH JPUH QEH Total

Avg # MFFD patients at any one time 49 57 35 140

Avg LoS as MFFD 2.3 8.3 8.0 -

Approx. # MFFD patients1 7,722 2,503 1,586 11,812

# MFFD patients to potentially shift (taking

15% haircut to total MFFD)6,564 2,128 1,348 10,040

# Bed days to potentially shift2 15,188 17,555 10,787 43,530

# Bed equivalents (at 91% occupancy) 46 53 32 131

1. Calculated by dividing # MFFD patients at any one time by Avg LoS and multiplying by 365 2. Calculated by multiplying # MFFD patients to potentially shift by Avg LoSSource: NNUH, JPUH & QEH MFFD data

94

Backup: Bed equivalent calculations

# Source

1 Total MFFD patients 10K MFFD to be transferred

2 Avg LoS (days) 4.3 Avg for MFFD across acutes

3Avg contacts / patient

/ day1.6

NCHC Virtual Ward Oct 17 –

Sep 18 contacts data

4 Total add. contacts 69.6K Oct 17 – Sep 18 contacts data

5 Add. contacts / day 191 #4 divided by 365

6Theoretical capacity -

# Contacts / day95

3rd quartile # contacts / day

(Oct 17 – Sep 18)

7 # FTEs 67 NCHC HR data

8 # Contacts / FTE / Day 1.4 #6 divided by # 7

9 # Add. FTEs 135 #8 divided by #5

# Source

1 # Packages FY17/18 5901 NCC data

2 # NFS FTEs 234 NCC estimate

3 # FTEs / Package 25 #1 divided by #6

4 # MFFD packages 10K# MFFD patients to be

transferred

5 # Add. FTEs 400 # 4 divided by # 3

Virtual / Home Ward Norfolk First Support

Source: NNUH, JPUH & QEH MFFD data; NCHC Virtual Ward data Oct 17 – Sep 18; NCHC Virtual Ward HR data; NFS data

demand & capacity

Documents