demand & capacity
Post on 09-Dec-2021
10 Views
Preview:
TRANSCRIPT
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)
• 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
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)
• 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)
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
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" 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
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
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
attendancesIP bed days OP attendances2
Acute Care
IP bed dayCommunity
contacts
CommunityUEC
Out of Hospital
Social
5.9M 302K 157K 66K 81K 133K 160K 151K 325K 302K 280K 843K 69K 12K 1.0M 427K 9.9K 3M
XX Theoretical capacity
COMBINED VIEW
"Combined view" scenario
• Impacts from primary care, community / social care &
acute optimization applied to today's baseline
• Capacity remains constant as today
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
attendancesIP bed days OP attendances2
Acute Care
IP bed dayCommunity
contacts
CommunityUEC
Out of Hospital
Social
5.9M 302K 157K 66K 81K 133K 160K 151K 325K 302K 280K 843K 69K 12K 1.0M 427K 9.9K 3M
XX Theoretical capacity
"Combined" components
• Reduced demand and LoS from all 3
components modelled forward
• 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
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 -
Capacity variance (# Bed days, K)
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
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
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
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
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
No. of Elective bed days (K)
62 63
FY18/19
FY17/18
2640
FY19/20
FY20/21
62
FY21/22
64
FY22/23
62 62 62 63 64
218
No. of Elective bed days (K)
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
Specialty NNUH JPUH QEH NNUH JPUH QEH
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
Capacity variance (# Bed days, K)
... 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 -
Capacity variance (# Bed days, K)
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)
• 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
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
provision on GP demand
10% reduction in GP demand to keep
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
GP Forward view, BCG Case experience
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
Note: System financial impacts in isolation are greater than the combined effect due to a moving baseline
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)
• 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)
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
Note: System financial impacts in isolation are greater than the combined effect due to a moving baseline
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
15% reduction in A&E attendances
• 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
5% reduction in A&E attendances
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)
5% reduction in A&E attendances
• 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
Intervention Value used Method Impact in D&C Model
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
Intervention Value used Method Impact in D&C Model
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
Specialty NNUH JPUH QEH NNUH JPUH QEH
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)
• Up to 15% reduction in GP
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
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.
• Up to 30% reduction in GP
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
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
91
PCN Strategy | Selected interventions will become model inputs
Assumption Value chosen 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
provision on GP demand
10% reduction in GP demand to keep
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
GP Forward view, BCG Case experience
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
early stage estimate
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
early stage estimate
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
top related