NHS East and North Hertfordshire Clinical Commissioning ... · - Primary Care strategy and...

Organisational Performance and Delivery (OPD)

3rd Feb 2015

Actions

Agenda Item Lead Manager/AD

Lead Director/AD

Clinical Lead 7 Jan 2015 Actions

2. Locality Commissioning

- Primary Care strategy and co-commissioning

- Locality dashboards

James Gleed Helen Edmondson Trudi Southam

Dee Boardman Primary care strategy and co-commissioning: Develop options/elements for co-commissioning framework (JG) Develop proposal for a CCG GP IT group to be considered at April OPD. (JG) Provide an update on planned housing developments in CCG area to April OPD (JG) Provide an update on research activities to April OPD (JG) To summarise PN training plan for 15/16 and discuss with Sheilagh (JG) Locality: Review of information from Post Death Audits to be reviewed in context of SHMI. (RJ & SR with HE) Bring back locality actions plans based on issues identified in dashboards to March OPD (HE, TS). Develop proposal for locality dashboards to build in metrics from schemes and quality of primary care to March OPD (HE, TS).

3. Falls Chris Badger Sharn Elton Fiona Sinclair Local falls campaign to be ‘launched’. Pathways and business case for community falls prevention and rapid response provision to come back to next OPD meeting in March 2015. Phased implementation of service provision.

OPDD 4th March 2015 Item 1

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4. Joint Commissioning & Partnership Board

Jacqui Bunce Beverley Flowers

Prag Moodley Winterbourne View/Transforming Care – this scheme should be Red rated as whilst we have plans for move of 11 patients, we do not currently have a forward strategy, we continue to address on a patient by patient basis. Dementia diagnosis and pathway to be discussed/addressed through locality structures. Development session (25/2/14) has been arranged to review the revised countywide strategy in more detail. Beverley Flowers to draft letter to Tom Cahill regarding HPFT CAMHS activity and performance requesting trajectory, and challenging safeguarding issues regarding high level of multiple DNA’s. HPFT CAMHS performance to come back to March 2015 OPD meeting.

5. Transport Update Jacqui Bunce Beverley Flowers

CCG to develop a more links between community transport and the developing primary care strategy and the Community Wellbeing Team in the HCC. Update to come back to April OPD.

6. End of Life Care Trudi Southam Dee Boardman Clearly articulate the role of the HomeFirst Clinical Nurse specialist and ensure KPI’s are agreed. Develop a role description for GP clinical lead and ensure there is a process for recruitment to this in line with working being undertaken through Governing Body for all clinical lead roles. Scope the practice with regard to gold standard meetings (where do these take place) and advanced care plans (Commissioning Framework, HCT and acute) to inform future arrangements. EOL business case(s) to come to April OPD meeting.

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7. Proposal from HCT to move to an 8 week physio pathway

Neil Hales Alan Pond Rachel Joyce The request from HCT was agreed to clear waiting list.

The permanent solution to be address through MSK / Physio project to come back to April OPD.

8. Contract Headlines re 2015/16

Neil Hales Alan Pond Lesley requested breakdown of main provider contract values. Spring House activity to be added into ENHT baseline. HomeFirst will need to go out to tender (paper regarding full implementation to go to (Feb) Governing Body) Tom Cahill to come to present Integrated Care Programme Board proposition. PAH stroke CQUIN will not continue and performance will be managed through contractual arrangements.

9. Performance Exceptions Gerry Moir Sharn Elton Sharn Elton to write formally to East and North Herts NHS Trust regarding RTT performance trajectory. The operations team should focus on challenging the Trust regarding their ability to respond to demand, which is a fairly predictable pattern, and how they flex when there is higher demand. Review short stay admissions through quality review meeting. Staffing information to be reviewed against information and SPC data.

10. Operations & Systems Resilience

Jo Burlingham Gerry Moir Phil Lumbard

Sharn Elton EEAST request for trolleys for “cohorting” needs to be resolved, Sharn Elton to review. Age profiles of admissions over 7 days to be reviewed. Winter resilience schemes – KPIs only give cost per case not impact. KPIs need to demonstrate impact to SRG. Locality managers to visit and ‘walk’ primary care schemes to provide feedback.

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11. Acute in Hours Visiting Service Update

Kay Dhesi Dee Boardman Nicky Williams Capacity mapping has to be completed when considering winter schemes as there was an impact on Out of Hours staffing in 14/15. Skill mix and case mix to be reviewed. Escalation process does need to be in place but not for ‘business as usual’. Formalise AIHVS contract basis from April 2015.

12. Outstanding Dec 2014 and Jan 2015 actions 13. Items for Governing Body

All actions from December 2014 and January 2015 OPD are completed or are in progress through scheduled Development sessions. 3 items for future (February) Governing Body meetings:

• Integrated care programme board proposition • Integrated business case for in practice and care home resource • HomeFirst

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Brought forward agenda plan Lead AD/Manager

Lead Director Clinical Lead Month

Medicines Management and Optimisation Team core offer and options for additionality (Business case for in practice and care home support)

Pauline Walton Chris Badger March

Interface Geriatrician Trudi Southam Trudi Southam Robin Christie March

Over 75 health checks progress update Trudi Southam Dee Boardman March

Neurological Services (including coproduction bid) Jill Catchpole Beverley Flowers

March

Map of Medicine Trudi Southam Trudi Southam Robin Christie March

Locality commissioning (actions noted in February) Helen Edmondson Trudi Southam

Dee Boardman March

Falls pathways and business case Chris Badger Sharn Elton Fiona Sinclair March

HPT CAMHS performance and trajectory Jacqui Bunce Beverley Flowers

Steve Kite March

Operations & System Resilience • Winter schemes • Vanguard bids Herts and Essex care homes

Jo Burlingham Sharn Elton March

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Primary care strategy and co-commissioning (actions noted in February)

James Gleed Dee Boardman April

End of Life Care (business case) Trudi Southam Dee Boardman Rachel Joyce April

MSK / Physio Kay Dhesi Dee Boardman Rachel Joyce April

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Princess Alexandra Hospital Trust

Capacity Planning: Phase 2

Preliminary Report

HEAD OFFICE: 16 St Martin’s le Grand, St Paul’s, London, EC1A 4EN Edinburgh Office: Exchange Place 2, 5 Semple Street, Edinburgh, EH3 8BL Sheffield Office: Electric Works, Sheffield, S1 2BJ t: +44 (0)20 7240 1121 e: [email protected] w: www.methodsanalytics.co.uk

16 February 2015


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Contents

1. Introduction ............................................................................................. 3

1.1. Requirements .............................................................................................................................3

1.2. Phase 2 model ............................................................................................................................3

1.3. Data ............................................................................................................................................4

1.4. Caveats to information contained within the Phase 2 model ....................................................4

2. Approach .................................................................................................. 5

2.1. Methodology ..............................................................................................................................5

2.2. Inputs ..........................................................................................................................................5

2.3. Pre-analysis .................................................................................................................................7

2.4. Modelling and simulation ...........................................................................................................9

2.5. Issues ....................................................................................................................................... 11

2.6. Community data ...................................................................................................................... 11

3. Developing outcomes through scenario generation ................................... 12

3.2. How the model generates scenarios ....................................................................................... 12

3.3. Setting parameters for the scenarios ...................................................................................... 12

3.4. Principal outputs ..................................................................................................................... 13

3.5. Performance analysis .............................................................................................................. 14

4. Key Findings ........................................................................................... 15

4.1. Question 1: How many beds do we need, by organisation across the local health economy? .......................................................................................................................................... 15

4.1.1. PAHT ........................................................................................................................................ 15

4.1.2. SEPT ......................................................................................................................................... 17

4.1.3. Rehabilitation .......................................................................................................................... 18

4.2. Question 2: Number and type of bed needed and the length of stay for each bed. .............. 20

4.2.1 Acute........................................................................................................................................ 20

4.2.2. Hospital at Home / ORLA ........................................................................................................ 23

4.2.3. Community .............................................................................................................................. 24

4.5. Question 3: Does the acute system have enough beds to meet required standards – emergency and elective? .................................................................................................................. 27

4.5.1. The Emergency standard – 95% in 4 hours ............................................................................. 27

4.5.2. The Elective standard .............................................................................................................. 29


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4.6. Question 4: Does the community have enough beds to enable the acute system to function effectively? ........................................................................................................................................ 31

5. Summary ................................................................................................ 33

6. Appendix A: The Capacity Model ........................................................... 35

6.1. Model features ........................................................................................................................ 35

6.2. How to use the model to test a capacity scenario. ................................................................. 35

6.3. Outputs from the Model ......................................................................................................... 37

1. Appendix B: Privately commissioned activity ......................................... 40


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1 1. Introduction

1.1. Requirements West Essex CCG and partners are currently undertaking resilience planning for the local health economy, which includes the Princess Alexandra Hospital Trust (PAHT), West Essex CCG and East and North Hertfordshire CCG. One of the objectives of this work is to determine the number and type of beds needed across the health economy to ensure delivery of the quality of care commitment to their patients and to achieve national standards (a four hour maximum wait in ED, and an 18 week maximum wait for elective procedures). This is supported by a two-phase modelling project provided by Methods Analytics. In Phase 1 an arithmetic model of bed occupancy and the risk of exceeding maximum capacity was developed. The purpose of this report is to elaborate on the findings of Phase 1 and present the outputs from Phase 2, a much more extensive and detailed stochastic discrete event model of patient flows. This means we have used a probability based approach moving virtual individual patients through care pathways based on local actual patient data to simulate the real world. The key purpose of the Phase 2 model is to address four questions posed to us: – 1 - How many beds do we need, by organisation across the local health economy? 2 - Number and type of bed needed and the length of stay for each type of bed. 3 - Does the acute system have enough beds to meet required standards – emergency and

elective? 4 - Does the community have enough beds to enable the acute system to function effectively?

1.2. Phase 2 model The Phase 2 Model uses a stochastic discrete event approach to modelling flows and occupancy levels. This uses both much more fine-grained data and tracking of patients across the local patient pathway. This approach involves intensive computation to produce reliable outputs. To achieve statistically meaningful results, multiple model runs are required. The Model extends the coverage of the Phase 1 model in order to attempt to provide more detailed answers to these questions by modelling the following elements of the local health economy: –

Unplanned care from presentation at ED to discharge, including inter-ward moves Elective care Community beds Community-based services that may impact flows of patients through acute and community

beds. Movements into and out of residential care beds have been included but the residential bed base has not been analysed in detail due to data limitations.



The model covers acute and community settings separately as it is not currently possible to track individual patients across the whole of the pathway, in a future phase it may be possible to do this if a unique local identifier can be used. This would enable a powerful inter-organisational analysis to be undertaken. The community model divides services into those providing beds, which allows occupancy to be modelled, and non-residential services. These are modelled using service capacity as the metric rather than bed occupancy. The model allows setting of parameters such as demand, number of beds or service capacity, bed type, length of stay, number of contacts (community services), patterns of admission/contact and discharge by day and time of day. These are initially set according to historical data analysis. The user can then see how creating novel scenarios by changing these alters occupancy levels or service saturation at varying points in the system. Full details of the Model’s features are described in the Appendix.

1.3. Data As in Phase 1 data from the period 1st November 2013 to May 31st 2014 was used to populate the model. Model development was based on variety of data sets in addition to those used in Phase 1, notably community data. Details of the data used are provided in the Appendix. Usable data was provided for:

Acute data activity for PAHT Ward movement data for PAHT St Clare’s Hospice occupancy data South Essex Partnership Trust bed occupancy data North and East Herts. CCG community hospital bed data Care homes bed base for both CCGs PAHT admission from care home data West Essex CCG Continuing Health Care funded beds East and North Herts. CCG care homes bed base

Unfortunately usable data could not be sourced from Essex Social Services team. There was no available source of data on care home occupancy. In addition we have undertaken some bespoke analysis using SUS PBR data so we can inform on commissioned activity from the private sector.

1.4. Caveats to information contained within the Phase 2 model The information contained within the model has been compiled from local data supplied to Methods Analytics. Where ward or service capacity is questioned (for example a medical specialty with a suspected erroneous bed capacity), revised figures can be entered at any time to re-run a capacity simulation. The capacity required is calculated from projected demand, which is a function of the hospital data supplied for the 7 month time frame agreed. Where the model is used to predict optimum bed capacity, this should be viewed as a guide figure only, as the true number should be informed after a number iterations and real life scenario tests.



2 2. Approach

2.1. Methodology The Phase 2 Simulation Model of the West Essex health economy has been built using data from the services and organisations involved supplemented by a systematic qualitative analysis of patient flows. These inputs were used to define and populate the model. Data was extracted and combined to generate a set of parameters defining the demand, flow volumes and patterns and probabilities that drive the model. These parameters include: – Physical constraints such as the number of beds or service provision available Operational decisions such the allocation of beds to specialties The balance of beds between elective and non-elective pathways Performance, including as length of stay, the timing and pattern of discharges External factors including changes in levels and patterns of demand The data provided allowed us to build a baseline picture of the current situation. By making adjustments to the parameters listed above, the user can explore how the system behaves in normal circumstances and under stress, and investigate “what if” scenarios and projections. The model has been designed specifically to answer the four main questions in the requirement.

2.2. Inputs To help understand patient flows and the use of beds and services, pathway flow diagrams were prepared for elective, non-elective and community segments. Information gathering was carried out through a series of interviews and interactive workshops supported by analysis of pre-existing documentation and models. These inputs were correlated and analysed to extract relevant elements of the pathways and displayed in a common graphical format. The flow diagrams focus on identifying bed or service “pools” with inflows, outflows and inter-pool movements. These pools form the basis for the simulation model, and were later mapped to information provided about physical wards, units and specialties, and cross-correlated with pre-analysis of data on patient flows. Outputs from the simulation model are based either on single pools or combinations aligned, for instance, to specialty, pathway or location.



Figure 1: Elective flows at PAHT

Figure 2: Non-elective flows at PAHT To properly represent the community, physical bed provision (e.g. community, rehabilitation, etc.) has been separated from services that do not have a bed pool as such. Such services can be provided wherever the patient or client is, e.g. hospital, residential care, outpatients, or at home. Podiatry serves as an example of delivering a service in this way. These services are therefore modelled in terms of capacity (number of visits, number of patients supported etc.) rather than physical bed base.



Figure 3: Community flows in the health Economy The diagrams show flows running from left to right. Key pools are shown in yellow, with key groupings of pools (for instance by location or bed type) also shown. The major flows between pools as were identified through the information gathering process are shown in black, along with some additional flows that commonly occur or were logically necessary (shown in red). The Community model also shows the services for which detailed data could not be made available. Although not completely modelled, inputs to PAHT are shown in detail as an aid to understanding where demand variations and pressures may arise.

2.3. Pre-analysis Prior to modelling all input data sets were explored and the characteristics of the data were understood. Simple counts and proportions were developed for all aspects required for modelling.

Table 1: Data characteristics - PAHT Speciality

Specialty #Spells %Female Age (yrs) LOS (days) %sameday %1night %2nights %3+nights #Wards #Transfers %SglWard Ward1 Ward2 Ward3 %Top3

A_and_E 2369 54% 53 1.6 77% 16% 2% 5% 1.2 0.2 85% CDU EAU zzz 99%

CardEM 475 45% 69 8.9 1% 11% 7% 81% 2.4 1.7 19% EAU FLEM CCU 92%

GastrEM 84 51% 69 7.1 7% 24% 15% 54% 1.7 0.7 46% EAU HARV HARV 68%

GenMdEM 7242 55% 66 6.3 29% 16% 9% 46% 1.7 0.6 49% EAU AMBU CDU 73%

GenSgEM 2071 52% 49 4.6 17% 25% 17% 41% 1.7 0.6 49% EAU CDU KING 65%

GeriEM 1008 53% 71 7.5 16% 18% 9% 57% 1.7 0.7 45% EAU STRO CDU 73%

GynEM 525 100% 32 2.2 46% 29% 12% 13% 1.4 0.3 71% CDU GAMB PENN 66%

PaedEM 841 45% 4 4.2 12% 32% 20% 37% 1.0 0.0 97% DOL SCBU SAM 98%

SurgEM 152 41% 31 2.0 57% 22% 12% 9% 1.3 0.3 74% CDU DOL KING 73%

TrauEM 888 52% 53 7.5 19% 16% 13% 52% 1.5 0.4 63% CDU CDU KING 54%

UroEM 135 39% 51 7.2 13% 19% 16% 53% 1.7 0.6 47% EAU KING CDU 60%

WomHEM 150 100% 30 1.9 53% 26% 10% 11% 1.4 0.6 65% GAMB SAM CHAM 89%

CardEL 41 32% 73 3.4 5% 59% 15% 22% 2.0 1.2 27% zzz FLEM CCU 90%

GastrEL 109 51% 72 3.0 5% 66% 11% 18% 2.2 1.6 13% DSU SAM HARV 98%

GenMdEL 402 57% 58 1.9 83% 4% 4% 9% 1.3 0.2 79% AMBU zzz WDU 95%

GenSgEL 535 49% 66 3.8 11% 50% 10% 29% 2.2 1.3 10% SAM DSU zzz 94%

GynEL 317 100% 50 2.7 15% 38% 31% 16% 1.9 0.9 16% SAM DSU PENN 97%

PaedEL 45 58% 7 2.9 36% 24% 24% 16% 1.0 0.0 100% DOL AMBU AMBU 100%

RespEL 101 53% 65 2.8 79% 4% 3% 14% 1.1 0.0 88% DSU LOCK AMBU 91%

SurgEL 193 78% 55 2.2 21% 64% 9% 7% 2.1 1.0 8% SAM DSU DOL 98%

TrauEL 875 55% 61 4.4 15% 21% 11% 52% 2.0 0.9 10% SAM DSU TYE 98%

UroEL 510 23% 67 3.2 12% 44% 18% 27% 2.1 1.0 8% SAM DSU DOL 97%

Grand Total 19068 55% 57 5.0 31% 21% 10% 38% 1.6 0.6 52%



Table 2: Data characteristics - PAHT wards Distributions were identified and appropriate statistical models fitted for each day and each speciality by day of the week. Some examples are included below:

Figure 4: PAHT admission profile - Day Figure 5: PAHT admission profile - Day – Elective General Surgery - Emergency General Medicine

Figure 6: PAHT Length of Stay model Figure 7: PAHT Length of stay model – Elective General Surgery - Emergency General Medicine

Admitting Ward #Spells %Female Age (yrs) LOS (days) %sameday %1night %2nights %3+nights #Wards #Transfers %SglWard Spec1 Spec2 Spec3 %Top3

EAU 5392 53% 67 7.2 13% 20% 11% 56% 2.0 1.0 24% GenMdEM GeriEM GenSgEM 87%

CDU 3801 55% 51 1.9 69% 18% 4% 9% 1.3 0.3 75% A_and_E GenMdEM GenSgEM 88%

SAM 2192 54% 62 3.6 13% 37% 16% 34% 2.1 1.1 6% TrauEL UroEL GenSgEL 78%

AMBU 1584 60% 58 1.4 88% 5% 2% 4% 1.2 0.1 83% GenMdEM GenMdEL GenSgEM 97%

DOL 1133 44% 5 3.3 17% 34% 20% 29% 1.0 0.0 99% PaedEM GenSgEM TrauEM 88%

KING 531 55% 55 6.6 7% 20% 17% 57% 1.3 0.3 76% GenSgEM TrauEM GenMdEM 85%

PENN 440 100% 52 6.1 8% 21% 18% 53% 1.3 0.2 75% GenSgEM GynEM GenMdEM 80%

SAUN 436 14% 58 6.2 6% 25% 16% 54% 1.3 0.2 77% GenSgEM GenMdEM GenMdEM 88%

STRO 368 60% 74 9.8 5% 10% 13% 72% 1.2 0.2 83% GenMdEM GeriEM GastrEM 98%

GPAS 336 55% 62 4.6 43% 18% 7% 32% 2.0 1.1 38% GenMdEM GeriEM CardEM 97%

DSU 323 59% 55 2.3 37% 40% 7% 15% 1.6 0.6 41% RespEL GastrEL SurgEL 56%

zzz 269 49% 58 5.8 19% 30% 12% 39% 1.9 0.9 21% GenMdEM GenSgEM GeriEM 48%

HRLD 258 68% 79 16.0 2% 5% 4% 90% 1.4 0.4 72% TrauEM GenMdEM GeriEM 97%

SSAU 257 53% 53 4.1 12% 32% 16% 40% 1.5 0.4 65% GenMdEM GenSgEM GeriEM 85%

GAMB 240 100% 31 1.6 66% 22% 6% 6% 1.3 0.4 72% GynEM WomHEM GynEL 100%

LOCK 206 37% 69 8.8 4% 10% 12% 74% 1.3 0.3 79% GenMdEM GeriEM RespEL 97%

WINT 170 74% 66 9.3 4% 14% 14% 68% 1.3 0.3 75% GenMdEM GeriEM GenSgEM 98%

FLEM 154 52% 68 6.4 5% 26% 8% 61% 1.5 0.7 56% CardEM GenMdEM CardEL 95%

RAY 148 37% 74 10.2 3% 9% 11% 77% 1.2 0.2 81% GenMdEM GeriEM GastrEM 99%

MOOR 139 43% 83 12.5 1% 6% 5% 88% 1.3 0.4 78% GenMdEM GeriEM GenSgEM 96%

LIST 125 80% 75 9.6 5% 10% 10% 76% 1.2 0.3 78% GenMdEM GeriEM GenSgEM 98%

HARV 118 50% 64 8.7 6% 12% 14% 68% 1.4 0.4 64% GenMdEM GeriEM GastrEM 93%

CCU 114 39% 68 8.7 6% 16% 10% 68% 2.1 1.6 35% CardEM GenMdEM GeriEM 94%

ARK 72 46% 58 10.4 10% 14% 10% 67% 2.1 1.3 35% GenMdEM GeriEM GenSgEM 93%

TYE 68 62% 70 5.0 10% 32% 15% 43% 1.4 0.4 72% TrauEL GenSgEL TrauEM 75%

BU 49 100% 37 2.8 16% 39% 18% 27% 1.7 0.6 41% GenSgEM GynEM SurgEM 86%

HDU 42 52% 62 7.8 2% 12% 14% 71% 1.9 1.1 26% GenMdEM GeriEM GenSgEM 98%

CHAM 29 100% 28 2.1 28% 59% 3% 10% 1.0 0.0 97% GynEM WomHEM PaedEM 97%

SCBU 25 44% 0 12.6 0% 8% 12% 80% 1.2 0.3 84% PaedEM A_and_E A_and_E 100%

ORLA 23 43% 60 6.0 4% 9% 9% 78% 1.2 0.2 87% GenMdEM A_and_E GeriEM 100%

MAFU 11 100% 30 1.5 91% 0% 0% 9% 1.5 0.5 73% WomHEM GynEM GenSgEM 100%

WDU 6 67% 65 2.7 17% 50% 0% 33% 1.2 0.2 83% GenMdEL UroEL A_and_E 100%

ICBU 5 20% 0 33.6 0% 20% 0% 80% 2.4 1.4 0% PaedEM A_and_E A_and_E 100%

HDBU 4 0% 0 44.5 0% 0% 0% 100% 1.8 1.3 25% PaedEM A_and_E A_and_E 100%



As can be seen from the steps shown in section 2.1.4, the statistical model used in each step was often based on the actual data rather than a pre-determined statistical distribution. Alternatives were explored such as:

The Geometric distribution for Length of Stay/Contact Duration. Most of the time this was a

good fit but in the end, we rejected this as when it was not a good fit, the errors were

unacceptably large which could have distorted the simulations.

The Beta distribution for time of admission & discharge. Again this was a good fit most of

time but it did struggle with scenarios when spikes occur such as rush hour. The two

examples below show one with a good fit, Emergency General Medicine on the left, but one

with a poor fit, Emergency Cardiology on the right.

Figure 8: PAHT Discharge time beta distribution Figure 9: Discharge time beta distribution

- Emergency General Medicine - Emergency Cardiology.

Both distributions are good candidates should we want to be able to vary length of stay,

time of admission and time of discharge specific days & weeks.

By using actual distributions, this means that the quality of simulations is greatly dependent on the quality of the actual data provided.

2.4. Modelling and simulation Overview of Methodology

Our estimates of required capacity are derived using Monte Carlo Simulation. Simulation is a common approach when the underlying system consists of a series of interactions such as a flow through a hospital. The steps undertaken are broadly the same for both acute and community but there are a few differences so they are listed separately. For each step, the assumptions and caveats are also listed.



# Step Statistical Model Used Comments

1 For each day & Specialty, simulate the number of admissions.

A separate Poisson distribution for each Specialty, week & day of week

Statistical tests show that a Poisson distribution is a reasonable fit.

2 For each simulated admission, simulate the Length of Stay

Actual distribution by LOS for each Specialty & day of week.

Average Length of stay is assumed to be the same for each week.

3 For each simulated admission, simulate the hour of admission

Actual distribution by hour for each Specialty.

Average Time of admission is assumed to be the same for each week & day of week

4 For each simulated admission, simulate the admitting ward

Actual distribution of Wards for each Specialty.

Ward distribution is assumed to be the same for each week/day of week

5 For each simulated admission, simulate ward transfers

Actual transfer rates for each specialty.

Transfer rates assumed to be the same for each week/ day of week.

6 For each simulated admission, simulate hour of discharge

Actual distribution by hour for each specialty.

Average Time of discharge is assumed to be the same for each week & day of week

7 On completion of steps 1 to 6, count the number of admitted patients still in hospital for each hour, Specialty & Ward

N/A Acute simulation is currently independent of external drivers e.g. lack of care services for discharges. In effect, each simulation is a simulation of process flow without external constraints.

8 Record counts from step 7 and then run another simulation using steps 1 to 7. In total between 300 & 500 simulations are run.

N/A In certain circumstances, a simulation run of 100 simulations is adequate to get a broad overview.

Table 3: Acute data characterisation

# Step Statistical Model Used Comments

1

For each day & Specialty, simulate the number of contacts.

A separate Poisson distribution for each Specialty, week & day of week

Statistical tests show that a Poisson distribution is a reasonable fit.

2 For each simulated contact, simulate the Duration

Actual distribution by Duration for each Specialty & day of week.

Average Duration is assumed to be the same for each week.

3 For each simulated admission, simulate the referring “Ward”

Actual distribution of “Wards” for each Specialty.

Ward distribution is assumed to be the same for each week/day of weekk

4 On completion of steps 1 to 3, sum the total duration of contacts for each day, Specialty & Ward

N/A Care simulation is currently independent of external drivers.

8 Record counts from step 7 and then run another simulation using steps 1 to 7. In total between 300 & 500 simulations are run.

N/A In certain circumstances, a simulation run of 100 simulations is adequate to get a broad overview.

Table 4: Community data characterisation

Once we have a created a data set of simulations with recorded counts by Specialty, Ward and week and day, we can then summarise the results. Our approach has been to calculate the % of



simulations for each day where the counts exceed a predefined threshold such as 85% of capacity. This percentage is therefore our estimate of the risk of that threshold being exceeded.

2.5. Issues Flow mapping The flow diagrams tend towards an “idealised” flow that does not capture all of the failure points, workarounds and alternative pathways that affect patients going through the pathways. As a result, actual data does not completely match the diagrams. This causes difficulties such as missing beds or patient who appear to flow into non-existent wards. Acute data: 1. Time of admission was only available for about 5% of all acute spells. We looked at using

A&E discharge time as the ward admitting time but we could only match these in 17% of

cases.

2. Admitting wards often used codes that were not part of the official list of wards. About 5%

of spells had such wards.

3. Approximately 20% of admissions were recorded as being to the CDU ward which we

understand does not have any beds. Whilst the majority were same day admissions, there

were still some patients recorded as having stayed 1 or more nights.

4. The A&E data showed a significant proportion of patients who were admitted as Elective

patients.

5. The length of stay data showed a strange drop in the average length of stay in May 2014. We

excluded this data from our models.

6. Acute data did not usefully identify discharge location

Community data 1. The SEPT community data was constructed in ‘contact minutes’ which we did not have any

denominator values for, requiring the construction of a methodology to accommodate this.

2. The lack of a common identifier between acute and community data sets prevented linking

at the patient level. Therefore a true assessment of flows between the acute and community

systems could not be undertaken and impacts had to be inferred from the correlation of

system behaviours.



3 Developing outcomes through scenario generation

3.2. How the model generates scenarios

The Phase 2 model uses the inputs as described above together with parameters set by the user to simulate the system over a set period of time, producing a set of outputs describing the behaviour of the system on a day-by-day basis. Finer grained outputs can be produced if required. Generating scenarios is a two-stage process. During the first stage the simulation the model uses the probabilities defined in the inputs to generate a database that can then be used by the Scenario Generator tool to produce the final scenario outputs. The first stage requires significant computational power and time as it employs multiple simulation “runs” to generate a database that will give reliable and consistent outputs. The second stage allows parameters such as bed numbers and allocations to be changed between runs of the Scenario Generator tool. This tool produces outputs relatively quickly (in the order of 5 minutes per run). Outputs are currently based on a period of 30 weeks, and if desired the user can change the starting date to examine seasonal effects. The Scenario Generator tool delivers scenario outputs in graphical and tabular formats, which facilitates further analysis such comparisons between simulations, as described below.

3.3. Setting parameters for the scenarios

Manipulating parameters in the Scenario Generator tool allows the user to examine how changes in system configuration affect performance. In general, to begin with a single variable should be modified on its own to help understand how sensitive the system’s behaviour is in relation to that parameter. Where significant interactions between variables are suspected parameters may be changed in tandem but this is a more involved and intensive process and may produce outputs that are harder to interpret. A key output the Scenario Generator tool provides is a view of how heavily system capacity is used. For beds this is measured through occupancy rate; where services are being modelled the Scenario Generator tool uses overall service capacity, measured in time, number of visits, etc. as most appropriate. For occupancy rates the user can set three "threshold" values to visualise the performance of the system. These are not fixed but we have used the following setting across all of the scenarios presented here for consistency: – 85%, which is generally accepted as an ideal occupancy rate for NHS “deep” wards 92%, a level at which the system is stressed and starts to experience significant pressures,

requiring operational and tactical adjustments to allow an organisation to cope 99%, at which point the organisation is operating at crisis level



3.4. Principal outputs

In relation to bed usage and capacity there are four key graphical outputs that allow the user to gain an immediate impression of the system's performance. A Manhattan chart showing the likelihood of each of the three capacity thresholds being

exceeded on a particular day in the time period. The taller the bar the more likely that capacity will be exceeded.

A “bed usage” chart which shows the likely number of beds needed by day over the time period. As well as the median value confidence limits at 5% and 95% are shown

These two charts can be set to display the results for all beds, elective or non-elective beds, for individual specialties (both elective and non-elective where applicable) and individual wards. Finally, two “heat maps” showing the likelihood of exceeding a given threshold occupancy on

a given day in the simulation period. The first shows this for specialties (split to show elective and non-elective admissions) whilst the second covers wards

For the Manhattan chart and heat maps the following colour scheme is used: – Yellow where the lowest threshold (set at 85% in our current analysis) is exceeded Orange if the middle threshold is exceeded (92%) Red where the high threshold (99%) is reached Green indicates the system is operating at below the lowest threshold

Figure 10: PAHT Elective Capacity Manhattan chart



Figure 11: PAHT Elective Capacity bed numbers

Figure 12: PAHT Elective Capacity heat map chart The Scenario Generator also provides a threshold probability table listing how likely each of the three thresholds will be exceeded on any particular day. This permits further analyses and collation of information from runs with different parameter values and has been used extensively in the analyses presented below.

3.5. Performance analysis Single runs of the Scenario Generator tool generate a quantified output but only as a single snapshot of how the system is likely to behave for a given bed base or total service capacity. The charts give a qualitative overview of the situation which although providing useful insights do not reliable answer complex questions such as that of bed requirement. Therefore, the approach taken to address these was to generate a set of scenarios using different settings. Using the threshold probability table it is possible to determine how frequently a particular capacity is likely to be exceed over the sample time period. This is achieved through calculating the percentage of days that each threshold is exceeded by a set probability (we have used 5%). The results for runs of the Scenario Generator tool using different parameter settings are then plotted to give a characteristic "reverse-S" shaped chart. This gives a more sophisticated insight as it permits immediate "what if" visualisation and comparisons, as described in relation to individual questions below, but it is relatively time consuming due to the multiple runs required.



4 4. Key Findings

4.1. Question 1: How many beds do we need, by organisation across

the local health economy?

4.1.1. PAHT To address this question for PAHT a set of scenarios was generated by incrementing the total number of beds whilst keeping other parameters such as bed allocation constant. The threshold probability tables from the scenarios were used to generate the reverse-S chart shown below.

Figure 13: PAHT total beds reverse-S chart

The figure plots the total number of beds against the risk of exceeding capacity over the time period of the simulation – this is the percentage of days that PAHT is likely to have a bed occupancy level over a particular value. The red curve shows the number of days that are over 99% occupancy Orange indicates 92% occupancy Green is 85% occupancy The horizontal lines on the chart indicate risk appetites of exceeding capacity one day in seven and one day in 30 (the line for 1-in-90 days has not been shown here for clarity; it lies very close to zero). In the figure below two blue vertical line have been added to show the relationship between a given bed base and the three occupancy thresholds.



Figure 14: PAHT total beds reverse-S with vertical guides

(Note: we are aware that the vertical guides on Figure X above do not appear to be vertical to the eye. They are, this is a visual illusion due to their overlying to the curved S charts making them appear to slope slightly top right to bottom left.) Using the chart we can estimate the likely bed base that will achieve a given risk appetite at a given occupancy, as shown in the table below.

Total bed capacity

Occupancy rate

Risk appetite 85% 92% 99%

1 in 7 days 507 473 437

I in 30 days 525 490 455

1 in 90 days 545 500 470

Table 5: PAHT bed requirements at a range of risk and occupancy

We estimate, on the basis of the historical data provided and at current demand levels, that to avoid exceeding 92% capacity more than once in 7 days 473 beds would be need to be available and in use. At a bed complement of 473 beds the risk of exceeding 99% occupancy is between 1 in 90 and 1 in 60 days, and 64% of days will run at over 85% capacity. This is shown by the blue vertical line on the right (at 473 beds). However, the blue line on the left (at 437 beds) shows that although the bed base is used more efficiently (94% of days at 85% occupancy or over, where it crosses the green line) the risk of being over 99% capacity rises to one in seven days.



This approach allows the user to easily visualise the balance between the risks of reaching a critical point (99% occupancy) against the efficient use of the bed base (the percentage of days where capacity use falls below 85%). In addition, the slope of the lines shows how sensitive the system is to a change in the bed base. For instance: – Reducing the bed base by 17 from 473 to 450 increases risk of exceeding 99% from <1% to 4% Reducing the bed base by 13 from 450 to 437 increases risk of exceeding 99% from 4% to 15% In conclusion, the Scenario Generator can be used to provide a sophisticated view of the impact of changes in the bed base. It estimates the risk of exceeding given bed occupancy thresholds but also provides an indication the balance between risk and efficiency. CAVEATS: It should be noted that a range of factors affects the accuracy of these outputs. In particular any behaviours that are not fully captured or apparent from the data may not be fully accounted for in the simulation. Additionally, when pools are split and examined in more detail the model shows how quite dramatic differences can be but how the impact of this is lost when aggregating to a whole-organisation level: – Allocation of beds by type against demand. This is examined in more detail in the next section Flexible use of beds in ways that are not well represented in the data The impact of pools and sub-pools may be underestimated. These includes wards, bays and

gender-specific beds

4.1.2. SEPT

The same methodology was applied to the community bed pool data as the acute pool, resulting in a capacity vs risk ‘S-chart’ as below:

Figure 15: Community bed pool S chart



This generates the likely bed base that will at a given risk appetite at a given occupancy, as shown in the table below.

Table 6: Community bed capacity by occupancy threshold and risk appetite

The same trade-off between efficiency and service availability as in the acute site can be considered here. We estimate, on the basis of the historical data provided and at current demand levels, that to avoid exceeding 92% capacity more than once in 7 days 153 beds would be need to be available and in use, some 8 more than currently available.

4.1.3. Rehabilitation

The rehabilitation service currently runs with a capacity of 40 beds. Modelling with patient level data generates the following capacity – risk appetite chart:

Figure 16: Rehabilitation service S chart

This generates a capacity table, trading off risk appetite with occupancy thresholds below:

Community bed capacity

Occupancy rate


1 in 7 days 155 153 134

I in 30 days 209 193 180

1 in 90 days 218 205 189



Table 7: Rehabilitation bed capacity by occupancy threshold and risk appetite

We estimate, on the basis of the historical data provided and at current demand levels, that to avoid exceeding 92% capacity more than once in 7 days 60 beds would be need to be available and in use, some 20 more than currently available.

Community bed capacity

Occupancy rate


1 in 7 days 64 60 56

I in 30 days 70 68 60

1 in 90 days 74 70 70



4.2. Question 2: Number and type of bed needed and the length of stay for each bed.

We present below outputs for the number and type of beds needed. The length of stay is an input into the simulation. For all work in this report we have used length of stay as described in section 2.3 and itemised in tables 1 and 2. Users of the simulation models will be able to adjust this to assess variant scenarios.

4.2.1 Acute

We have developed estimates for the numbers of beds required for PAHT split first by elective and non-elective pathways and then by major specialties. The approach we have used is similar to that for total bed numbers. First, reverse-S charts were prepared plotting the risk of exceeding occupancy thresholds against bed numbers. Using these the projected beds required in order to avoid exceeding a bed occupancy threshold of 92% at a risk that this may happen of 1-in-7 was determined. Firstly, we have split acute site bed capacity into the two main pathways, elective and non-elective. Although ward beds are designated at a more granular level, the elective/non-elective split largely mirrors how the Trust operates, flexing capacity significantly in order to manage demand. The differences between "existing" and "projected" bed requirements are in part a demonstration of the current reality where elective wards flex considerably to take emergency flows but are not necessarily designated as such at ward level. Secondly, we have analysed bed requirements according to major specialties (again split by elective and non-elective pathways) using reverse-S charts in the same way. It is noted that some difficulties arise in estimating the occupancy levels and bed requirements where numbers are small because of the increased impact of variability and lower statistical reliability. The following reverse-S charts plot the risk of exceeding occupancy thresholds against bed numbers for elective and non-elective bed bases. The charts for specialties are provided in the appendix.

Figure 17: PAHT Non-Elective beds reverse-S chart Figure 18: PAHT Elective beds reverse-S chart



The tables below summarise the outputs, showing projected bed numbers against the existing bed base (ward bed lists, December 2014). As well as individual elective and non-elective results the tables show totals derived from individual specialties. However, it is likely that in practice there are interactions or overlaps between specialties that may influence the simulation. Although the reliability of individual specialty estimates may be acceptable, aggregating these to produce an overall total becomes much less so. Non-Elective

The first table shows the speciality and Trust-wide change in number of beds required to meet existing non-elective demands. Overall this shows that the present stock of 332 beds identified as non-elective would need to be increased by 116 to 438 in total to satisfy a 1-in-7 day risk appetite and 92% occupancy threshold. Summing the individual specialty changes brings this number to 578, an increase of 246. The bulk of this change lies within General Medicine, which is to be expected, and in geriatric medicine. Some of the changes are of greater magnitude than would be expected, such as the reduction in women's health, general surgery and trauma. Where numbers are in red font, additional caution should be exercised as the demand seen is relatively low and therefore the bed pool required to service the demand is small, resulting in the modelling becoming unstable, with small and unstable S charts generated where a small change has an excessive impact.

PAHT

Specialty description Speciality

name Existing number of beds

Projected number of beds

Change in bed number

Non-elective beds by pathway

332

Non-elective beds by specialty

A_and_E A_and_E 22 25 3

CardEM Cardio 20 33 13

GastrEM GastroEnt 14 7 -7

GenMdEM Gen Med 82 288 206

GenSgEM Gen Surg 52 65 13

GeriEM Geriatric 28 62 34

GynEM Gynae 20 11 -9

PaedEM Paediatric 14 27 13

SurgEM Surgical 4 3 -1

TrauEM Trauma 15 47 32

UroEM Urology 4 7 3

WomHEM Women's H 57 3 -54

Total by specialty 332 578 246

Table 8: PAHT non-elective bed base modelled requirements using individual specialties



Elective The second table below shows the speciality and Trust-wide change in number of beds that would be required to meet existing elective demands. Overall this shows that the current stock of 128 beds identified as elective could be reduced to 97, using the risk appetite and occupancy rate thresholds of 1-in7 and 92%.

PAHT

Specialty description Speciality

name Existing number of beds

Projected number of beds

Change in bed number

Elective beds by pathway

128

Elective beds by specialty

CardEL Cardio 4 1 -3

GastrEL GastroEnt 6 3 -3

GenMdEL Gen Med 33 12 -21

GenSgEL Gen Surg 12 20 8

GynEL Gynae 8 8 0

PaedEL Paediatric 6 1 -5

RespEL Respiro 28 2 -26

SurgEL Surgical 7 5 -2

TrauEL Trauma 12 31 19

UroEL Urology 12 14 2

Total by specialty 128 97 -31

Table 9: PAHT Elective bed base modelled requirements using individual specialties

The figures in the above tables show marked disparities between current bed allocation and what would be needed for relatively low risk performance in relation to occupancy breaches. We make the following observations about the factors that may be contributing to the apparent anomalies in the numbers of projected beds: – The small throughput volumes and bed numbers in some specialties distort the calculations Beds and patients appear to be allocated to specialties in different ways in the datasets

provided. As a result there will seem to be mismatches between cohorts. The model will respond to these differences but can sometimes amplify the mismatch.

Coding effects appear to be affecting the results to a greater extent than was expected. It seems that there are more non-elective General Medicine patients than the number of beds would suggest there should be.

Disparities such as that in non-elective General Medicine patient and bed numbers tend to cause an amplification of the extent of change when the simulation runs (a positive feedback effect occurs, which is necessary to some extent to prevent excessive averaging and smoothing). Where a specialty requires a major shift in bed numbers this results in over-estimation of the beds required.

The reverse-S charts are produced using a 5% threshold for exceeding a given capacity. This is necessarily cautious to reflect the consequences associated with exceeding capacity limits,



and is designed to bias the results in favour of safety. However, this effect is amplified where pools are successively broken down and is additive across multiple small pools.

The simulation assumes similar scheduling patterns and queuing behaviours for elective and non-elective pathways. Although this is generally true, the assumption starts to break down where volumes are small or turnarounds rates are high. The effect can be to underestimate the number of beds that are required for elective patients.

Bed flex patterns are very difficult to model accurately as they are inherently non-linear responses to bed pressures and the data does not tell us when such changes occur. It is probable that the large increase in the non-elective General Medicine bed base is an illustration of how extensively flexing actually occurs at present.

This chart illustrates the small-number effects that can occur.

Figure 19: PAHT Emergency General Surgery rev-S chart

Where activity volumes or capacity utilisation vary widely over the course of an extended modelling time frame the balance between risk avoidance and efficiency becomes even more difficult to achieve without significant flexing of bed use. Our analysis of the model’s outputs is geared to avoid “averaging effects” of the kind seen in many conventional approaches, so runs of high pressure with increased risks of breaching occupancy thresholds will increase projected bed requirements. This effect is increased in those specialities experiencing greatest seasonal variation, where the “averaging effect” would be most keenly felt.

PAHT ward level modelling

We modelled and assessed ward level bed requirements for PAHT. This was extremely challenging as the available information on wards and that encoded in the data did not match usefully. Also many of the ward pools as listed are very small which makes the model relatively unstable.

4.2.2. Hospital at Home / ORLA



We have included the ORLA service as a separate section as though this service is located in the community it is provided by the acute Trust. The data received shows that the service managed 31 patients in the 7 month period in focus. Modelling of this based on actual patient data shows the following service demand:

Figure 20: Manhattan capacity chart for the ORLA service. This demonstrates that the service requires capacity to manage 2 beds at any one time and the risk appetite methodology does not need to be applied as the service capacity delivered is very stable.

4.2.3. Community The bed based community services have been presented above in section 4.1.2. The data provided for non-bed based community services was very granular and presented ‘contact minutes’ per contact. A total of 52,282 minutes of care is recorded. At this time we do not have any information on the available ‘capacity’ of these services. This would need to be developed and agreed with each service, understanding the style of service delivery, staffing and how resources are deployed. In theory this would enable us to develop a typical day, for example, it may be that a community dietician delivers 10 appointments of 30 minutes each a day, so that 300 contact minutes can be mapped to one individual and 10 clients. Without this understanding we have taken a different approach. We have made the assumption that at some point in the 7 month period each service was operating at its maximum possible capacity. We have set that as the ‘service capacity’ and then determined the risk that the service will need to deliver more than this on any one day. Unsurprisingly this shows that capacity is largely able to meet demand though there are some services where even this is not the case.



Figure 21: Community service contact minute capacity overview heatmap at 85% maximum. The heatmap above shows community services, across the health economy with colour values showing that many of them are running all or most of the time at greater than 85% of maximum capacity. Those particularly stressed are: continence service, Epping ICT, cardiac nursing, physiotherapy, respiratory, the south Essex stroke service and Tissue viability. If the capacity threshold is increased to 92% of maximum, as below the continence service and physiotherapy remain fully over committed, with respiratory and tissue viability still pressurised. Other services do not appear to run at this level however.

Figure 23: Community service locations capacity overview heatmap at 92% maximum Focussing on these services we see that the continence service ran at full capacity initially, but there is a steady downward trend through the period, perhaps indicating the loss of staff or the increase in capacity of an alternative option:

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18681 TOTAL

18641 South Essex278 Continence

559 Diabetes

547 Dietetics

3609 ICT-Epping

2073 ICT-Harlow

1474 ICT-Uttsford

335 Nursing-Cardiac

58 Nursing-MS

159 Nursing-PD

1509 Orthotics

6044 Physio

1185 Podiatry

172 Podiatric Surgery

151 Rapid Access

231 Respiratory

275 Speech therapy

206 Stroke S.Essex

94 Tissue Viability

184 Wheelchair

40 Stroke W.Herts

Probabilty of

exceeding 85% of

capacity shown

20/04/15 27/04/15 04/05/15 11/05/1529/12/14 05/01/15 12/01/15 19/01/1503/11/14 10/11/14 17/11/14 24/11/14 01/12/14 08/12/14 18/05/15

PAHT Simulation Model - Overview by Specialty (Green indicates 0% probability, Yellow indicates 50% probability & Red indicates 100% probability)09/03/15 16/03/15 23/03/15 30/03/15 06/04/15 13/04/1526/01/15 02/02/15 09/02/15 16/02/15 23/02/15 02/03/1515/12/14 22/12/14

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18681 TOTAL

18641 South Essex278 Continence

559 Diabetes

547 Dietetics

3609 ICT-Epping

2073 ICT-Harlow

1474 ICT-Uttsford

335 Nursing-Cardiac

58 Nursing-MS

159 Nursing-PD

1509 Orthotics

6044 Physio

1185 Podiatry

172 Podiatric Surgery

151 Rapid Access

231 Respiratory

275 Speech therapy

206 Stroke S.Essex

94 Tissue Viability

184 Wheelchair

40 Stroke W.Herts

Probabilty of

exceeding 92% of

capacity shown

20/04/15 27/04/15 04/05/15 11/05/1529/12/14 05/01/15 12/01/15 19/01/1503/11/14 10/11/14 17/11/14 24/11/14 01/12/14 08/12/14 18/05/15

PAHT Simulation Model - Overview by Specialty (Green indicates 0% probability, Yellow indicates 50% probability & Red indicates 100% probability)09/03/15 16/03/15 23/03/15 30/03/15 06/04/15 13/04/1526/01/15 02/02/15 09/02/15 16/02/15 23/02/15 02/03/1515/12/14 22/12/14



Figure 24: Continence service manhattan chart and capacity charts. There are complex patterns indicating service changes in other services as well, with physiotherapy showing a small but powerful shift over the year end 2013, that reduced the pressure in the service to manageable levels:

Figure 25: Physiotherapy service manhattan chart charts. The same approach applied to service locations, including services delivered at Clinics, Home, Hospital, Residential care and by rehabilitation services shows that only clinic services appear to run at significantly below maximum capacity.

Figure 26: Community service locations capacity overview heatmap at 85% maximum

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TOTAL 18681 TOTALClinic 10143 Clinic

Home 6705 Home

Hospital 1797 Hospital

Residential 45 Residential

WestHerts 40 WestHerts

Probabilty of

exceeding 85% of

capacity shown

PAHT Simulation Model - Overview by location (Green indicates 0% probability, Yellow indicates 50% probability & Red indicates 100% probability)03/11/14 10/11/14 17/11/14 24/11/14 01/12/14 08/12/14 15/12/14 22/12/14 16/03/1529/12/14 05/01/15 12/01/15 19/01/15 26/01/15 02/02/15 09/02/15 16/02/15 23/02/15 02/03/15 09/03/15 04/05/15 11/05/15 18/05/1523/03/15 30/03/15 06/04/15 13/04/15 20/04/15 27/04/15



4.5. Question 3: Does the acute system have enough beds to meet required standards – emergency and elective?

Much of the NHS struggles to achieve the standards, and PAHT is no exception. Understanding cause and effect that determines whether both the in-patient pathway and the Emergency department to achieve these mandated targets is extremely complex requiring on the ground process mapping, decision analysis, bed state point of care studies and a wide ranging review project. That is not what we have undertaken in our response to this question. We have looked at the data available and attempted to answer the question based on flows and volumes by time of day, day, week, and month. In theory, if there is available capacity on the system to accept a flow of patients then whether either of these targets is met is not constrained by capacity but by a myriad of factors such as speed of pathway transition, communication, resource input and so on. Therefore we have undertaken an analysis to ascertain if there is sufficient capacity. One way to demonstrate this is to understand if there is a correlation between the different parts of the system when performance deteriorates and capacity becomes heavily utilised.

4.5.1. The Emergency standard – 95% in 4 hours The emergency Department 4 hour target performance dropped below 95% in Q3 13/14 to 92.9% and has deteriorated since then:

Figure 27: PAHT 4 hour ED performance Source: Methods Stethoscope We have modelled flows through the emergency department. The input data shows that the likelihood of achieving the four hour standard is heavily influenced by the eventual disposal of the patient, with admitted patients much more likely to spend more than four hours in the department, at 24% than other patients. This would suggest that either the bed base capacity or the ability to process those patients and move them into a bed is a major factor. Incidentally the table also shows the accuracy of fit of the simulation, with the actual Trust data and output of the simulation being a close match:

Outcome %4h Actual %4h Predicted Count

A&Eclinic 13.9% 13.9% 396

Admitted 24.0% 24.2% 14766

Discharged 3.9% 3.9% 28721

Fractureclinic 1.8% 1.8% 2310

Left 2.9% 2.9% 1787

Other 0.2% 0.3% 5582

Referred 6.8% 6.5% 4848

Grand Total 8.8% 8.9% 58410

Table 10: ED 4 hour target performance by disposal

In assessing the relationship between emergency department 4 hour target performance we have split this into two – the risk the target will not be met for all patients (dark blue) and the risk it will not be met for admitted patients (light blue), and show the correlation with the risk the acute bed



base will not meet required demand. We have done this three times at the three bed base occupancy thresholds used throughout this report.

Figure 28: Heatmap of ED 4 hour target performance by day against PAHT bed base at 85% occupancy

Figure 29: Heatmap of ED 4 hour target performance by day against PAHT bed base at 92% occupancy

Figure 30: Heatmap of ED 4 hour target performance by day against PAHT bed base at 99% occupancy These show that the great majority of the time there is a large risk that admitted patients will not meet the 4 hour standard, which is also largely true for all ED patients. The effective increase in hospital bed capacity as occupancy threshold increased does not effect this. Statistically a Chi squared test between the chance of meeting the 4 hour target on any one day and the risk the acute bed base will not be able to meet required total demand results in a test p=0.16 which does not indicate a significant correlation. This suggests that the bed base being ‘full’ does not have a strong bearing on the risk that a patient being admitted through the ED will breach the 4 hour target. This is further explored in the chart below, which shows the relationship between ED 4 hour target risk and acute bed occupancy being greater than threshold risk:

Figure 31: ED 4 hour target risk and acute bed occupancy greater than 85% risk

This shows that risks correlate best at weekends when both A&E and Acute capacity risks are lower. We note that A&E performs worst at the beginning of each week and it seems that this creates the greatest pressures (the purple bar chart always starts the week high and tapers down). On Fridays and weekends both perform better with lower risk of breaches (the deep saw tooth pattern).

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95%<4Hrs A&E Admissions

95%<4Hrs A&E

460 beds Acute

Probabilty of exceeding 85% of

capacity shown20/04/15 27/04/15 04/05/15 11/05/1529/12/14 05/01/15 12/01/15 19/01/1503/11/14 10/11/14 17/11/14 24/11/14 01/12/14 08/12/14 18/05/15

PAHT Simulation Model - Overview by Organisation (Green indicates 0% probability, Yellow indicates 50% probability & Red indicates 100% probability)09/03/15 16/03/15 23/03/15 30/03/15 06/04/15 13/04/1526/01/15 02/02/15 09/02/15 16/02/15 23/02/15 02/03/1515/12/14 22/12/14

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460 beds Acute


capacity shown

PAHT Simulation Model - Overview by Organisation (Green indicates 0% probability, Yellow indicates 50% probability & Red indicates 100% probability)03/11/14 10/11/14 17/11/14 24/11/14 01/12/14 08/12/14 15/12/14 22/12/14 16/03/1529/12/14 05/01/15 12/01/15 19/01/15 26/01/15 02/02/15 09/02/15 16/02/15 23/02/15 02/03/15 09/03/15 04/05/15 11/05/15 18/05/1523/03/15 30/03/15 06/04/15 13/04/15 20/04/15 27/04/15

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PAHT Simulation Model - Overview by Organisation (Green indicates 0% probability, Yellow indicates 50% probability & Red indicates 100% probability)03/11/14 10/11/14 17/11/14 24/11/14 01/12/14 08/12/14 15/12/14 22/12/14 16/03/1529/12/14 05/01/15 12/01/15 19/01/15 26/01/15 02/02/15 09/02/15 16/02/15 23/02/15 02/03/15 09/03/15 04/05/15 11/05/15 18/05/1523/03/15 30/03/15 06/04/15 13/04/15 20/04/15 27/04/15

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A&E and Acute Risks

P(Acute>85%capacity)

P(<=95% within 4 hours, All A&E)



Both of these findings support the hypothesis that there are factors both internal to the department and to the process of the emergency pathway that impact on the 4 hour target performance more strongly than the availability of acute beds. Interestingly, there was a statistically positive correlation with 4 hour performance in ED was with the non-bed based community services, with a Chi squared test of p=0.01 suggesting a strong correlation. Correlations were also present with the availability of West Essex bed based services.

P-Values Acute Care Contacts

SEPT WEPT

A&E 16% 1% 30% 4%

Table 11: Chi Squared correlation between A&E 4 hour performance and risk of bed base exceeding available capacity

4.5.2. The Elective standard In the three years, 2012, 2013 and 2014 PAHT achieved the 18 week standard until Q4 13/14, above the national cohort, after this performance dropped below 90% and has remained there. There are also a number of other factors that demonstrate the acute bed base is under stress:

18 week – if capacity meets demand then meeting target is flow not beds Figure 32: Waiting times and associated metrics for PAHT Q2 2014/15. Source: Methods Stethoscope These data demonstrate that PAHT’s rate of elective surgical cancellations has trended up over the last three years, peaking in Q3 13/14 at 22.7 per 1000 procedures and returning to closer but still outlying the national cohort since then. Performance against the 62 day cancer target is also poor, mirroring the national average in the mid eighty percent range from Q4 2012/13 to Q3



2013/14 and since then dropping off to mid to high 70% range. It must be noted that this is still in line with the national cohort. It is clear from section 4.2.1 that the bed base capacity identified as nominally elective is not in fact used for that purpose, the emergency pathway over-running its allocation. In order for PAHT to be able to meet the elective standard then focus on managing the emergency pathway is paramount. At a service level, the elective streams are demonstrated in section 4.2.1, Table 9 suggesting that required capacity could be delivered with a bed base of 97 beds. However, it must be taken into account that a significant volume of elective care is commissioned by West Essex CCG from the private sector. In the 7 month period in focus this amounts to close to £4m of activity (£6.8m annualised). This is a very significant spend that could be available to PAHT if it could deliver the capacity and had the relevant services available.

Table 12: count of activity and cost by type Source: Methods bespoke analysis SUS PbR

Full details, by specialty and provider are available in Annex 2.

Row Labels Sum of Count Sum of PbR

Day case 1967 £ 1,402,493

In patient 408 £ 1,199,392

Out patient 15488 £ 1,364,704

Grand Total 17863 £ 3,966,589



4.6. Question 4: Does the community have enough beds to enable the acute system to function effectively?

We have used the same correlation approach as in our response to Q3, but posing a slightly different question: Does the hospital run into trouble when the community bed base is full? In reviewing the following section it must be borne in mind that the community providers of bed based services have significantly greater control than the acute site, with no emergency pathway flows as such. Therefore it would be surprising if demand as measured by activity outstripped capacity, as the service provider can refuse access. It is likely that over-spill demand devolves on the acute sector and hence the interest in the relationship with both ED and the in-patient bed base.

Figure 33: Heatmap of system performance by day against PAHT and community bed base at 85%

occupancy At the 85% occupancy level the acute system shows a high risk of exceeding available occupancy through most of February and March, however the Emergency department, highlighted with a blue oval, is already failing to meet the required target in late November and through December. This pattern is repeated later in the modelled period, with the acute bed base recovering but ED still under stress.


occupancy

With higher occupancy, the acute bed base is no longer under pressure, but ED remains unable to meet demand and achieve the mandated target.


occupancy

At all three occupancy levels the Stroke rehabilitation service shows a high probability of exceeding available capacity, most evident in early January and again in early March. The SEPT bed base, as modelled, can meet required demand at all occupancy levels until very late in the period. Looking at the statistical correlations between these services, by day, we can see that there are some strong relationships.

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95%<4Hrs A&E

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52282 Mins Care Contacts

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145 beds SEPT Community

18/05/15

PAHT Simulation Model - Overview by Organisation (Green indicates 0% probability, Yellow indicates 50% probability & Red indicates 100% probability)09/03/15 16/03/15 23/03/15 30/03/15 06/04/15 13/04/1526/01/15 02/02/15 09/02/15 16/02/15 23/02/15 02/03/1515/12/14 22/12/14


capacity shown20/04/15 27/04/15 04/05/15 11/05/1529/12/14 05/01/15 12/01/15 19/01/1503/11/14 10/11/14 17/11/14 24/11/14 01/12/14 08/12/14

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09/03/15 04/05/15 11/05/15 18/05/1523/03/15 30/03/15 06/04/15 13/04/15 20/04/15 27/04/1502/02/15 09/02/15 16/02/15 23/02/15 02/03/15Probabilty of exceeding 92% of

capacity shown

PAHT Simulation Model - Overview by Organisation (Green indicates 0% probability, Yellow indicates 50% probability & Red indicates 100% probability)03/11/14 10/11/14 17/11/14 24/11/14 01/12/14 08/12/14 15/12/14 22/12/14 16/03/1529/12/14 05/01/15 12/01/15 19/01/15 26/01/15

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95%<4Hrs A&E

460 beds Acute




09/03/15 04/05/15 11/05/15 18/05/1523/03/15 30/03/15 06/04/15 13/04/15 20/04/15 27/04/1502/02/15 09/02/15 16/02/15 23/02/15 02/03/15Probabilty of exceeding 99% of

capacity shown

PAHT Simulation Model - Overview by Organisation (Green indicates 0% probability, Yellow indicates 50% probability & Red indicates 100% probability)03/11/14 10/11/14 17/11/14 24/11/14 01/12/14 08/12/14 15/12/14 22/12/14 16/03/1529/12/14 05/01/15 12/01/15 19/01/15 26/01/15



Table 13: Chi Square correlations between organisational capacity models It is important not to make too much of these correlations, as they do not imply causation. They also do not imply any directionality, whether the acute is influenced by the community or if the influence is in the opposite direction. It is in fact likely any relationship is complex. However it would be possible to posit sensible reasons for the relationships shown:

Acute / Care contacts: NEGATIVE: there is no correlation between the acute bed pool and community service capacity, which is somewhat surprising, as it might be expected that the lack of availability of community services (not beds) would impact on the ability to discharge from the acute site and could possible increase the chance of some emergency admissions. Inversely, if the acute site is very busy there may be greater demand on community services to take on activity to support the acute site.

Acute / SEPT beds: POSITIVE: the availability of community beds in SEPT, or lack thereof, indicated by a high probability of demand exceeding capacity is strongly correlated with the acute site running into similar trouble. This could be due to the inability to discharge to a community bed or people who could be managed in a community bed entering the acute sector. Inversely, if the acute site is very busy there may be greater demand on community beds to take on activity to support the acute site.

Acute / WEPT: POSITIVE: as above but the inability of the acute site to discharge to rehabilitation services may lead to increased acute site bed occupancy.

Care contacts / SEPT beds: POSITIVE: it may be that the lack of care services means patients may be admitted to community beds who could otherwise be managed without a bed. Inversely the lack of a bed may mean patients are managed be a non-bed base service who would normally be admitted to a community bed.

Care Contacts / WEPT: NEGATIVE: the lack of relationship is not surprising as these services do not cross cover

SEPT beds / WEPT: POSITIVE: it may be that these services cross cover one another or the other is pressurised.

P-Values Acute Care Contacts

SEPT WEPT

Acute 73% 3% 0%

Care Contacts 0% 34%

SEPT 2%



4 5. Summary

Demand for beds and services in both the acute and community is very unevenly distributed. This is both by day of the week and week and month of the year. This makes identifying a single number for any bed pool challenging as it is a balance between having enough capacity to meet peak demands without there being inefficient slack in the system much of the time. The approach we have taken, showing ranges of occupancy and risk appetite aims to enable the reader to appreciate the large impact this unevenness in demand induces. Because of this we would propose that the single key message we identify is that smoothing the capacity / demand profile by aggressively adopting the NHS England 7 day standards would be highly beneficial. Actual beds are not the restraining element for much of the variation we see in the data. What is restraining capacity is the ability to flow smoothly – create throughput in the system. The model is based on historical activity and other than the above comments it does demonstrate numbers which can be used to inform planning:

Question 1: How many beds do we need, by organisation across the local health economy? Acute Modelled bed numbers at the acute site overall range from 470 to 545 depending on risk appetite and modelled occupancy rate. At a mid point, modelling at 92% occupancy with an acceptance of requiring more beds than are available at this level one day a week 473 beds are required. Community bed pool Using the same approach the range for community beds is from 134 to 218, with a mid point of 153. Rehabilitation Using the same approach the range for community beds is from 56 to 74, with a mid point of 60. Question 2: Number and type of bed needed and the length of stay for each bed. Acute This shows a major change in bed requirements for elective and non-elective bed requirements when modelled at a specialty level. These numbers do not match those above as the more granular modelling is challenging for small bed pools, so these must be treated with caution. Elective: The estimated requirements for the elective bed pool indicate it could be reduced to 97, using the risk appetite and occupancy rate thresholds of 1 in 7 and 92%.



Non- Elective Overall this shows that the present stock of 332 beds identified as non-elective would need to be increased by as many as 116 to 438 in total to satisfy a 1-in-7 day risk appetite and 92% occupancy threshold. Hospital at Home / ORLA The ORLA service appears to be under-utilised and could be delivered with capacity to service 2 beds. Community bed pool Using the same approach the range for community beds is from 134 to 218, with a mid point of 153. Rehabilitation Using the same approach the range for community beds is from 56 to 74, with a mid point of 60. Question 3: Does the acute system have enough beds to meet required standards – emergency and elective? We have considered the ability of the acute system against two standards: 4 hours emergency department waiting time – 95%

It does not appear that the bed base capacity of the Trust has a direct relationship with the ability of the Emergency Department to meet the 4 hour standard. 18 week referral to treatment – 90%

It is clear that the Trust’s ability to meet the 18 week RTT standard is severely compromised by the pressure on beds due to the emergency pathway. Alone the elective pathway requires in the order of 100 beds at most. If these are available, and theatre, outpatient and support services capacities can meet the demand this level of throughput requires this bed base can enable the Trust to meet the standard. We have not modelled the impact of potentially repatriating the significant private sector commissioned activity by West Essex CCG, this could be done and may demonstrate the capacity required to bring this close to £7m annual income to the Trust. Question 4: Does the community have enough beds to enable the acute system to function effectively? There are strong relationships between the capacity available in the SEPT community beds, the rehabilitation service and the acute site. Though correlation is not causation it is highly likely that this indicates that increased capacity in the community bed base will relieve pressure on the acute site. We note that there is also a strong relationship between the SEPT bed based and community based services, suggesting one or both cannabilises resources of the other, either through cross cover of staff or care delivery.



6. Appendix A: The Capacity Model

6.1. Model features The model can provide information on the following: – Current occupancy status in emergency and elective bed pools, by ward (or lack thereof). Current capacity usage in community services (or lack thereof). What parts of the healthcare system have spare capacity, and what parts are above capacity

(and by how much). Reporting on occupancy rates based on any occupancy rate threshold (programmable by the

model user). Information provided on the estimate of a capacity shortage by day and week (we have called

this the ‘Risk Appetite’. A heat map of the overall ‘occupancy health’ of the system (which should be used to

reallocate beds and services to aid flow). Admissions by ward at the 5%, 50% and 95% percentiles measured against the user defined

maximum occupancy rate (this should be used to gauge under/ over capacity) The number of patients admitted by specialty by day of the week. The number of patients admitted on a Monday by specialty by week. The average length of stay by specialty by week. The cumulative length of stay in days by specialty. The admission time distribution by specialty. The cumulative admission time by specialty. The time of discharge by specialty. The cumulative time of discharge by specialty. Admission volume by ward.

6.2. How to use the model to test a capacity scenario. In this example, the model is run with PAHT bed capacity for emergency cardiac admissions (CardEM). We will use this approach to run subsequent queries that will enable the model to build a projected capacity model for PAHT

Box 1

What do I want to do? How do I use the model to do this?

What does the model show?

Test whether the current bed capacity of 20 is sufficient.

Go to the ‘USER_CONTROLS’ tab in the Excel model and enter ‘CardEM’ into cell B10. Do not click ’Generate All Chart Data’, as this is only used when we re-run the model simulation upon

The model will now display several items in the ‘C_detail’, ‘OverallSpecialities’ and ‘OverallWards’ tabs.



changing bed capacity number(s)

The information contained in these tabs is explained below.

C_detail – This tab displays two charts. Firstly, the probabilities of exceeding capacity at

certain pre-set capacity levels. Red is above 99% capacity, orange is above 92%, and yellow is above 85% capacity. These can be changed in the ‘USER_CONTROLS’ tab if needed. This coloured chart displays the chances of breeching these capacities by day of the week and by week over time. There is also a ‘Risk Apetite’ line, this is a line to represent the percentage of the time when the area of investigation is operating above capacity. For example the risk appetite of 1 in 7 days displays a line at 14.28% (i.e. 1/7) of the time, which is added to the probability of running above capacity. The user can specify a risk appetite of their choice, depending on the level of risk adversity desired. The second graph in the tab shows the number of beds occupied by day and week, at the 50% percentile (the 5% and 95% percentiles show the distribution about this plot, and can be interpreted as error bars). This graph may be used to estimate the required bed capacity needed when making changes. The capacity bar in black shows the capacity (in this case 85% of 20 beds is 17 beds).

OverallSpecialities – This page displays a heat map showing the probability of exceeding capacity over a projected time frame. A green area indicates a 0% change of exceeding capacity at the selected occupancy rate (in this case 85%). Yellow indicates a 50% probability, while red indicates 100% probability. There are varying shades of these colours to represent transitions in states from an unlikely scenario to a likely one.

OverallWards – This displays a similar heat map to ‘OverallSpecialities’ but by ward.

Box 2



Use the model to inform on current capacity status, and possible alterations to optimise efficiency

After following the instruction in ‘Box 1’, ensure that the Risk Appetite and % capacity is defined. Then examine the following tabs: ‘C_detail’, ‘OverallSpecialities’ and ‘OverallWards’

The ‘CardEM’ bed pool requires a larger number of beds as the risk appetite is breached more often than desired. Explanations follow below and in appendix 5.1.

Making a conclusion based on the output of the model will now be explained. In the C_detail tab, it is shown that based on the number of admissions in this patient group, the risk appetite is breached above the set limit of 1 in 7 days, and that the likely number of required beds may be about 25 (an increase of 5 beds). Screenshots are shown in Appendix 5.1.1. The screenshot located in appendix 5.1.2 shows the heat map for all clinical pools with the highlighted CardEM graphic. Through time, the trend shows areas where the probability of



breaching capacity limits (set in this case at 85%). It is observed that there are areas of red and yellow, as well as green. ‘OverallWards’ will not be commented on at this point, however the heat map is similar to that shown for ‘OverallSpecialities’, but by ward bed pool.

Box 3



Having viewed the model outputs, ascertain a likely bed requirement to meet demand for this admission pool.

Alter the number of beds by examining the model outputs as discussed. Re-run the model, by clicking the ‘Generate All Chart Data’ button. A new output will take about three minutes.

The number of beds to satisfy the risk appetite of 1 in 7 days is between 24 and 25.

The model predicts that the size of this bed pool is between 24 and 25 beds, thus an increase of 4-5 beds is required to satisfy the risk appetite. Screenshots are shown in appendix 5.1.3. It is important to state that the required bed capacity will change depending on the desired risk appetite and % occupancy rate. In order to answer the questions asked by this report, we will run the model with the following settings:

Risk Appetite for a specialty bed pool: 1 in 7 days (once per week) (other than ED, which will be 1 in 30 days).

Risk Appetite for the entire emergency bed pool: 1 in 30 days (once per month).

Risk Appetite for the entire elective bed pool: 1 in 30 days.

Risk appetite for the entire system (PAHT): 1 in 90 days (once per financial quarter). The occupancy rate will be 92% across the whole system.

6.3. Outputs from the Model

‘C_Detail’ for CardEM bed capacity calculation (risk appetite of 1 in 7 days). The probability of exceeding certain capacity rates is high relative to the selected risk appetite for the CardEM admission group.



The existing bed capacity would suggest that the number of beds should be increased to above 20 (at the 50% percentile).

‘C_Detail’ for CardEM bed capacity calculation (risk appetite of 1 in 7 days). The heat map shows what bed pools in the emergency system at PAHT. The black box shows the CardEM bed pool. It can be seen that the probability of increasing capacity at the 85% level is between 50% (yellow) and 100% red for large parts of the time period. An increase in bed capacity will help to reduce the risk of being above the specified occupancy target.

Screenshots for revised ‘CardEM bed requirements. The increase of 5 beds to the bed pool reduces the probability of exceeding capacity in this bed pool to a level acceptable to the chosen risk appetite.



The increase in beds shows that the system runs within set capacity for the majority of the time at the 50% percentile level. The heat map below indicates that the increase in bed capacity has mitigated the majority of the risk to exceeding occupancy limits.



1. Appendix B: Privately commissioned activity

Activity and cost by type and speciality

Activity and cost by provider

Please note: blank values for some items are present in the raw SUS PbR data and may mean that this service is not paid via PbR, this will have to ascertained locally.

Row Labels Sum of Count Sum of PbR Row Labels Sum of Count Sum of PbR Row Labels Sum of Count Sum of PbR

DC 1967 1,402,493£ IP 408 1,199,392£ OP 15488 1,364,704£

ANAESTHETICS 3 2,282£ ENT 26 45,281£ ANAESTHETICS 5 328£

COLORECTAL SURGERY 1 603£ GASTROENTEROLOGY 8 6,359£ AUDIOLOGICAL MEDICINE 3 355£

ENT 58 67,922£ GENERAL SURGERY 39 49,135£ AUDIOLOGY 7 -£

GASTROENTEROLOGY 734 333,999£ GYNAECOLOGY 101 208,947£ CLINICAL NEUROPHYSIOLOGY 2 424£

GENERAL SURGERY 120 133,141£ HEPATOBILIARY & PANCREATIC SURGERY 23 35,659£ CLINICAL PHYSIOLOGY 13 -£

GYNAECOLOGY 134 112,530£ MAXILLO-FACIAL SURGERY 1 1,917£ COLORECTAL SURGERY 14 1,373£

HEPATOBILIARY & PANCREATIC SURGERY 12 16,633£ OPHTHALMOLOGY 1 1,867£ DIAGNOSTIC IMAGING 296 19,368£

MAXILLO-FACIAL SURGERY 7 5,242£ SPINAL SURGERY SERVICE 21 114,342£ ENT 1286 123,372£

NEUROSURGERY 1 643£ TRAUMA & ORTHOPAEDICS 154 686,829£ GASTROENTEROLOGY 2406 278,891£

OPHTHALMOLOGY 130 95,264£ UROLOGY 34 49,056£ GENERAL MEDICINE 1 103£

PAIN MANAGEMENT 263 174,835£ GENERAL SURGERY 906 90,978£

PLASTIC SURGERY 3 2,141£ GYNAECOLOGY 1901 187,691£

SPINAL SURGERY SERVICE 44 33,730£ HEPATOBILIARY & PANCREATIC SURGERY 159 17,535£

TRAUMA & ORTHOPAEDICS 250 326,702£ INTERMEDIATE CARE 11 -£

UPPER GASTROINTESTINAL SURGERY 1 382£ MAXILLO-FACIAL SURGERY 35 2,863£

UROLOGY 206 96,444£ NEUROLOGY 2 -£

NEUROSURGERY 32 -£

OCCUPATIONAL THERAPY 2 -£

OPHTHALMOLOGY 581 38,690£

ORAL SURGERY 34 3,525£

PAIN MANAGEMENT 1023 89,906£

PHYSIOTHERAPY 483 -£

PLASTIC SURGERY 4 128£

PODIATRY 10 141£

RHEUMATOLOGY 1 100£

SPINAL SURGERY SERVICE 536 40,797£

TRAUMA & ORTHOPAEDICS 3776 222,982£

UPPER GASTROINTESTINAL SURGERY 3 270£

UROLOGY 1956 244,884£

Row Labels Sum of Count Sum of PbR Row Labels Sum of Count Sum of PbR Row Labels Sum of Count Sum of PbR

DC 1967 1,402,493£ IP 408 1,199,392£ OP 15488 1,364,704£

ASPEN HEALTHCARE LIMITED 230 248,627£ ASPEN HEALTHCARE LIMITED 70 203,417£ ARKANUM 31 2,279£

BMI HEALTHCARE 26 24,780£ BMI HEALTHCARE 7 11,895£ ASPEN HEALTHCARE LIMITED 2671 283,200£

NUFFIELD HEALTH 2 1,598£ RAMSAY HEALTHCARE UK OPERATIONS LIMITED 301 887,844£ BENENDEN HOSPITAL 7 464£

RAMSAY HEALTHCARE UK OPERATIONS LIMITED 1661 1,058,601£ SPIRE HEALTHCARE 30 96,236£ BMI HEALTHCARE 184 15,316£

SPIRE HEALTHCARE 48 68,887£ CARE UK 94 9,244£

HORDER HEALTHCARE 1 100£

INHEALTH LIMITED 20 -£

NUFFIELD HEALTH 458 31,359£

PROVIDE 19 105£

RAMSAY HEALTHCARE UK OPERATIONS LIMITED 11548 982,187£

SPIRE HEALTHCARE 441 40,450£

SUFFOLK COMMUNITY HEALTHCARE 11 -£

VIRGIN CARE SERVICES LTD 3 -£

Agenda Item No: 6

Date of Meeting: 4 March 2015

Operational Delivery Day

Paper Title: End of Life Ambition Position Paper

Decision Discussion Information Follow up from last meeting Report author: Trudi Southam

Martina Vogel-Matthews Purpose of the paper: To outline all elements of the EoL work programme which

address the CCGs strategic ambition

Conflicts of Interest involved:

N/A

Recommendations to the Board / Committee

To review the work plan and agree priorities for 2015/16

1. Background

1.1 Strategic Context The national End of Life Care Strategy for England (2008) set out a whole systems approach to the commissioning and delivery of end of life care. The strategy recommends a care pathway approach for commissioning services and delivery of integrated care for individuals. The care pathway involves the following steps:

• Identification of people approaching the end of life and initiating discussions about preferences for end of life care;

• Care planning: assessing needs and preferences, agreeing a care plan to reflect these and reviewing these regularly;

• Coordination of care; • Delivery of high quality services in all locations; • Management of the last days of life; • Care after death; and • Support for carers, both during a person’s illness and after their death.

Actions for End of Life Care: 2014-16, published in November 2014, recognises the need to revisit and refresh the strategy and sets out NHS England’s commitments to end of life care for adults and children.

The NICE quality standard for end of life care for adults defines clinical best practice, covering all settings and services in which care is provided by health and social care staff to all adults approaching the end of life. The quality standard for end of life care for adults is described through 16 quality statements. Since 2012/13 the NHS Commissioning Board draws on NICE quality standards to translate the national health outcomes into outcomes and indicators that can be applied at a local level. These are used to hold clinical commissioning groups to account for their contribution to improving outcomes, and will be set out in the NHS commissioning outcomes framework. The NHS Outcomes Framework 2015/16 includes ‘Improving the experience of care for people at the end of their lives (domain 4, indicator 4.6).

1.2 Local Context ENHCCGs Local Ambition Area 3 focusses on End of Life. The CCG seeks to promote the use of proactive management plans for patients identified as ‘End of Life’ to enable death to be achieved in the preferred place and fewer hospital episodes in the final month of life.

The Operational Commissioning Plan 2014 – 16, Chapter 4, states that by December 2014 the local health and social care providers will have a common approach to end of life care resulting in a reduction in the number of hospital episodes within the final 30 days of life to no more than 1,500 and increasing the percentage of dying people who die in the place of their choice to 60% by 2019.

Due to the complexity of end of life service provision across the health economy, this has taken time to develop. The CCG has now drawn up an implementation plan designed to deliver effective whole system End of Life care, focussing on patient choice and shared decision making. In September 2014, the implementation plan was reviewed at PMO and key work streams agreed. These have been further reviewed and updated (see figure 1).

1

Figure 1 End of Life Work Plan 2015

Workstream Key Milestones

Sep-

14

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-14

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-14

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-14

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1 EoLC Plan on a Page

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oLC

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Complete

2 Develop EoLC Pathway Formal Sign-off

3 Implement EoLC Pathway Map commissioned services against pathway

4 Review Current EoL Commissioning Arrangements

Advanced Care Planning review & analysis

5 Revise & Plan EoL Commissioning Complete review of commissioned services

6 Develop EPaCCS Solution Phase 1 PID sign-off 13th

7 HomeFirst CNS Pilot Pilot scheme commences 9th

8 CHC Fastrack Pilot Reduce to 1 Bed and develop framework agreement

9 PEACE Discharge Summaries to Care Homes

Part of improved discharge summary CQUIN project

10 Improve Discharge Summaries to GPs

CQUIN scheme to be finalised

11 Workforce Development Plan Sign off commissioning intentions 4th

Phas

e 2

12 Re-commission / Procure EoLC Services

13 Create DOS for EoLC Services 14 Implement EPaCCS Solution

Phas

e 3 15 Mobilise 24/7 Advice & Co-

ordination Service

16 Monitor & Evaluate During Q4 2014 progress on the development and delivery of all work streams was reported via monthly progress reports and the programme was discussed at the February 2015 Organisational Performance and Delivery Day.

2. Status Update & Next Steps The current position of each work stream, interdependencies, options regarding next steps and strategic direction of travel are outlined below.

2.1 Work Stream Status Updates

2.1.1 Plan on a Page Version 1 of the Plan on a Page was approved by PMO in September 2014 and subsequently shared with stakeholders at the ENHCCG EoL Ambitions workshop. The concept of a one page document outlining the CCGs overarching strategy and objectives was well received. Stakeholders provided a number of very specific feedback items which have been addressed in version 2 of the document (see Appendix 1)

2.1.2 Pathway Development & Implementation The initial draft of the End of Life Care Map (Appendix 2) was produced by a small number of clinicians representing a range of providers and services. The work was led by Rachel Joyce and the draft Care Map was introduced to the wider stakeholder community at the ENHCCG EoL Ambitions workshop.

The pathway aims to provide guidance to inform palliative care provision with clearly defined roles and responsibilities across all care settings. It will include links to the relevant guidance and templates, standardising record keeping such as Message in a Bottle (MIB) and holistic needs assessment (PEPSI COLA Aide Memoir).

Interdependencies: • Development of phase 1 of the EPaCCS solution, which will deliver templates for the recording and managing

of EoL information in SystmOne and Emis. • Review and consolidation of currently commissioned EoL services

2

Next Steps: • Refine pathway documentation and secure formal sign-off • Review current commissioned services working with contract leads to ensure they support key components of

the pathway. Focus areas are district nurse capacity, out of hours service provision and 24/7 advice and co-ordination.

Future Plans: • Develop business cases for future service provision following robust needs assessment. Future investment to

be identified as part of business case development. • Publish Pathway on Map of Medicine

2.1.3 EoL Commissioning Arrangements Palliative care for patients residing in ENHCCG is currently commissioned from a range of providers (Appendix 3) and through a number of different mechanisms. CQUINS to support and improve the delivery of end of life care are in place with ENHT, PAH and HCT (Appendix 4). ENHCCG Commissioning Framework has two specific indicators relating to EoL patients.

Commissioning Framework: Objective 3 of the Commissioning Framework: Working to improve outcomes for the frail elderly, patients with Long Term Conditions and those patients identified to be within the last 12 months of their life.

‘End of Life’ (EoL) commissioning was identified by the majority of locality commissioning groups. February 2015 figures show 31% of patients dying in their preferred place of death (with a further 16% not expressing a preference, 8% not met and 45% status not advised) and evidence suggests that proactive EoL care can improve patient outcome and be more cost effective. 40% of acute hospital beds are used by patients in their last 12 months of life. Proactive EoL registers are necessary to effectively commission the right care at the right time. GPs also need to review the effectiveness of EoL commissioning arrangements, to inform on-going management.

The commissioning framework identifies the following targets:

Advanced care planning (ACP) is initiated to support patients’ decisions and choices which will facilitate high quality end of life (EOL) care. The statistics regarding end of life care have been reviewed in order to improve the provision of EOL care. About 1% of the population die each year: 85% of deaths occur in people over 65 and 75% of deaths are from non-cancer conditions. The RCGP provides guidance for practice teams to identify their 1% via http://www.goldstandardsframework.org.uk/cd-content/uploads/files/Primary%20Care/RCGP%20Matters%20of%20Life%20Death%20-%20Jul12.pdf Each locality is required to initiate an ACP for 0.75% of their practice population. (For example, a list size of 5K population should be able to identify 50 patients) Identifying those patients who may be nearing the end of life is the important first step for practices in each locality. Evidence suggests that, if patients are identified and included on the ACP register, then they are more likely to receive proactive, well-coordinated care. At a national level, about 60–70% of people do not die where they choose. Only 35% are home deaths (18% home, 17% care home) and 54% die in hospital. Indicator: Resources: Advanced Care Planning is initiated for 0.75% of the practice population. A READ code will be added to the clinical notes to indicate Advance Care Planning initiated or otherwise indicated.

Advance Care Planning process. The Gold Standards Framework stepwise approach for identification of patients in the last 6 to 12 months of life. Prognostic Indicators for Long Term Conditions.

A Post Death Audit (PDA) is completed for a minimum of 50% of all practice deaths. A review of deaths will be undertaken at the monthly multi-disciplinary review meeting (as outlined in 4.3.2). The completed PDA will be submitted quarterly via the locality manager, for review by the EOL GP clinical commissioning lead, to support practice learning.

Post Death Audit form

Source: East & North Hertfordshire CCG Planning for Patients 2014 – 2019, Operational Commissioning Plan 2014 - 2016

Next Steps • Undertake detailed review of advanced care planning in GP practices, identify issues and barriers to success.

This work will be managed via the LTC group, who will identify a range of practices in their respective localities to take part.

• Audit current reporting information in relation to performance against commissioning framework targets.

3

• Map existing commissioned services against the EoL Pathway. • Review and, where appropriate, revise service specifications for currently commissioned palliative services. • Review the current provision of telephone advice & co-ordination for EoL patients and consider options for

future commissioning, including the commissioning of a single 24/7 advice line.

2.1.4 Electronic Palliative Care Co-ordination System (EPaCCS) SystmOne has been identified as the IT platform for recording and managing palliative care information. In February 2015 HBL ICT have been commissioned to undertake the first phase of EPaCCS development. Key deliverables of phase 1 have been agreed as follows:

• Identify and collate S1 templates and forms currently used in GP practice and HCT for the recording of End of Life relevant information.

• Define, develop and agree a standard set of templates for the recording and managing of EoLC core information in S1 and EMIS. Templates must be developed in accordance with the EoL Care co-ordination Core Content Standard Specification (ISB1580).

• Set up reports, summary views and letter templates and agree business process to support information transfer to OOH

• Define and agree an implementation plan for deployment of Summary Care Record (SCR) v2 with inclusion of the EoL core data set.

• Undertake preliminary scoping for phase 2 rollout. This will include options for information sharing with acute trusts and hospices and IT specification for the envisaged 24/7 advice & co-ordination hub.

Interdependencies: • GP IT resource to support Summary Care Record Rollout to all ENHCCG GPs • Re-procurement of OOH service provision will include IT / clinical systems requirements to ensure access to

the SystmOne record, including palliative care information. Interim arrangements will be considered as part of phase 1 rollout.

Next Steps: • Provide clinical leadership and assurance to EPaCCS phase 1 • Monitor rollout of phase 1 deliverables and manage issues / feedback • Develop a specification for phase 2, to be reviewed and agreed by OPD

2.1.5 HomeFirst CNS Pilot The HomeFirst CNS Pilot business case was ratified at the Planned Care Programme Board on the 24 July 2014. The pilot will run for one year, at a cost of £69,866, to test the benefits of integrating palliative care services into the HomeFirst Plus initiative in the LLV Locality Group.

The aim is to demonstrate improved patient outcomes at end of life, in particular avoidance of hospital admissions, by a combination of:

• Integrated working with HomeFirst staff to address the aspects of Advance Care Planning for those patients with Long Term Conditions (to include planning for crisis where possible);

• Streamlined access to the full range of hospice services; • Advice for and support of patients directly in advance care planning • training and support of healthcare staff in end of life palliative care skills

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The pilot start date has been confirmed as 9 March 2015 on a secondment basis from Isabel Hospice The finalised KPIs will be presented at the HomeFirst Steering Group on 20 February 15. The pilot will be monitored via existing HomeFirst management structures.

Next Steps: • EoL Workstream to receive reports / evidence on efficacy of the pilot Future Plans: • Homefirst to make recommendations on future commissioning

2.1.6 CHC Fastrack Pilot The purpose of the Rapid Response Hospice EoL Care Bed Based Pilot is to ensure that individuals with a rapidly deteriorating condition that are entering a terminal phase are supported in their preferred place of care as quickly as possible. Patients are identified by an assessment in the acute hospital by the Integrated Discharge Team, and Marie Curie Discharge Liaison Nurses.

The scheme was originally approved to commence on the 1st Oct 2014 but was rescheduled to the 6th Oct. The first patient was admitted into the service on the 16th Oct. The 2 beds are funded on an agreed fixed price (£1,000) per bed per week, irrespective of whether they are occupied by patients or not. There is a 4 week notice for changes.

Total E&NHCCG spend from 13th Oct 2014 to 15th Jan 2015 is £38,000.

From pilot commencement to the beginning of Feb 2015 bed occupancy rate has been 65%.

Next Steps: • Give notice to the provider to reduce the number of beds from 2 to 1 based on current patient demand • Develop a service specification to meet the needs of complex patients • Develop a framework agreement to call off beds at Isabel and Garden House Hospice and at two nursing

homes (to be confirmed) • Give notice on the one remaining bed once the framework agreement is in place

2.1.7 Discharge Summaries Improved discharge summaries to GPs and Nursing / Care Homes were identified as a key component to achieving our strategic ambition on EoLC. ENHCCG is working with colleagues from ENHT to develop an enhanced discharge summary CQUIN (see Appendix 4)

Nursing / Care Home Discharge Summaries It is intended that PEACE (Proactive Elderly Advance CarE) discharge summaries are trialled at nursing / care homes identified to receive input and support from the Interface Geriatricians as part of the Interface Geriatric Service Pilot scheme.

PEACE was developed at two acute hospital sites for care home (nursing) residents prior to discharge. Patient's preferences are documented, or in the cases of mental incapacity, best interests decisions are made, to give clinical advice and escalation decisions for future medical care. The document is sent on transfer back to nursing homes, as agreed with GPs and care homes (Source: BMJ Support Palliat Care 2011)

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Interdependencies • Quality Team – CQUIN development Next Steps • Clarify process and timescale for implementing the GP Discharge Summary CQUIN • Monitor and evaluate enhanced GP Discharge Summaries from ENHT

2.1.8 Education & Workforce Development The Bedfordshire, Hertfordshire and Luton End of Life Education Workforce Plan 2015 was commissioned by ENHCCG, HVCCG, Bedfordshire CCG and Luton CCG in response to the CCGs strategic objectives for end of life care. This was to identify future educational needs and current training provision for multidisciplinary health and social care staff across all settings. It was developed during the autumn of 2014 and the final version delivered to ENHCCG on the 8 December 14.

The workforce plan identifies a number of education programmes currently delivered by specialist providers, including the ABC Programme and recommends the commissioning of a sustainable rolling programme of education, based on the training options outlined in the plan.

ABC Programme In 2011, NHS Health Education East of England funded a fixed term end of life education programme across the East of England. Funding included the development of the education material, educator, project lead and administration salaries as well as independent evaluation costs. This funding ceases as planned on the 31st March 2014.

The ABC programme combines face to face teaching or eLearning with mentorship to support the learning in practice. Its strength is experienced palliative care educators situated in hospices providing accessible, flexible relevant learning and support. There are 7 core modules:

• Overarching principles • Assessment and care planning • Comfort and well-being • Communication skills • Advance care planning • End of life tools and • Care in the last days of life In Bedfordshire, Hertfordshire and Luton, the education is delivered by hospice seconded end of life educator facilitators (from the Hospice of St Francis, Peace Hospice Care, Isabel Hospice and Keech Hospice Care) who combine teaching with follow up mentorship for the learners to implement learning into practice as well as champion follow up workshops and a Train the Trainer project to enable learners to carry on the learning to others in their work setting. It has also worked closely with Herts Care Providers Association (HCPA) and Hertfordshire County Council.

The hospices involved were keen to continue the education programme and six months funding continuation (£40,348) was agreed by the Out of Hospital Programme Board on 8 May 2014. SLAs for this work were agreed via the contracts team and the work was delivered July – Dec 14 at Isabel Hospice and Jan – April 15 at Garden House Hospice (Garden House did not have resources in place to deliver the programme from July). No further funding has been agreed going forward.

In January 2015 Sue Plummer, CEO Peace Hospice Care, submitted a business case for the commissioning of the ABC education programme for Nursing, Residential and Domiciliary Staff in East & North Hertfordshire. However,

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the CCG have reviewed training resources and funding streams currently available to nursing / care home staff identified a range of currently available resources:

• HCPA in partnership with Local Authority make contributions towards training costs for a range of mandatory and specialist courses. Care of the Dying / Palliative Care is specifically identified as one of those specialist areas

• The Complex Care Premium, a weekly enhanced rate of £70 paid on completion of an in-depth staff training programme, will facilitate high quality care provision for care home patients with complex needs. It is funded by the Better Care Fund

• NHS Health Education England provides e-Learning for Healthcare (e-LfH), a free of charge online training facility for the healthcare profession. End of Life Care for All (e-ELCA) is an e-learing project available to health and social care staff involved in delivering end of life care (http://www.e-lfh.org.uk/programmes/end-of-life-care/)

• The Hospice of St Francis offers the European Certificate of Essential Palliative Care, an 8 week distance learning course, at a cost of £450. The course covers a variety of topics including symptom management, palliative care emergencies, ethical issues and communication skills. Assessment involves the completion of a portfolio, a written exam and an oral assessment. Continuing Professional Development credits available and this course can be accredited at level 6 or 7.

The aspect of ABC notably not covered by other training resources is mentorship, which is a key component of the ABC programme.

EoLC Training Aspects of CQUINs A number of EoL CQUIN schemes are currently under way or in the process of being finalised. These schemes also contain elements of staff training – see Appendix 4 Recommendation: OPD are asked to consider whether training and development should be provided from resources currently available with no further investment in additional programmes. Next Steps • Review impact of training programmes for future provision

2.2 Governance The work programme is supported and directed by the ENHCCG EoL Working Group. The group meets fortnightly to discuss progress, manage issues and ensure the appropriate commissioning of End of Life services.

Clinical leadership to the work programme has been provided by Rachel Joyce following the departure of the contracted GP Lead (Anita Ray-Chowdhury). The working group recognises that wider clinical input and leadership is needed to shape specific areas of work, such as the EPaCCS development. The ENHCCG Long Term Conditions Group have agreed to support this work stream.

There is wider stakeholder engagement for the EoL work programme via the EoL Stakeholder Forum. The initial meeting took place in January 15 and quarterly meetings have been scheduled going forward.

Next Steps: • Provide an EoL work programme overview to the LTC group and agree specific details of the groups input • Host EoL Stakeholder Forum in April 15 • Agreement of a job profile (Appendix 5) and to secure a clinical lead for the End of Life workstream

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http://www.e-lfh.org.uk/programmes/end-of-life-care/

http://www.e-lfh.org.uk/programmes/end-of-life-care/

3. Priorities 2015/16 OPD are asked to agree the priorities detailed within this document and to support the key elements of the work plan. These are:

EoL Service Commissioning: Work Plan Priority: Outcome: Refine pathway documentation and secure formal sign-off The national End of Life Care Strategy for England

(2008) recommended care pathway approach

Review current commissioned services with input from contract leads and map against the EoL Pathway to ensure they support key components of the pathway.

Commissioning and delivery of integrated care for individuals, as set out in the End of Life Care Strategy (EoLC Strategy 2008)

Audit current reporting information in relation to performance against commissioning framework targets, e.g. Post Death Audit (PDA).

Support practice learning to facilitate high quality end of life care (Commissioning Framework Objective 3)

Review and, where appropriate, revise service specifications for currently commissioned palliative services.

Improved quality of care and support for carers caring for EoL patients (Plan on a Page)

Review the current provision of telephone advice & co-ordination for EoL patients and consider options for future commissioning.

A reduction in the number of hospital episodes within the final 30 days of life (Operational Commissioning Plan, Local Ambition 3)

Secure agreement to provide training and development from resources currently available with no further investment in additional programmes.

Review impact of training programmes for future provision.

Better use of resources / demonstrate value for money

Give notice to the provider to reduce the number of CHC Fastrack beds from 2 to 1.

Better use of resources / demonstrate value for money

Develop a service specification and framework agreement to call off beds at Isabel and Garden House Hospice and at two nursing homes.

Patients and carers achieving their preferred choice of place of death (Operational Commissioning Plan, Local Ambition 3)

Give notice on the one remaining CHC Fastrack bed once the framework agreement is in place.

Equity of access for ENHCCG patients

Quality / Service Improvement: Work Plan Priority: Outcome: Undertake detailed review of advanced care planning in GP practices, identify issues and barriers to success.

Promote the use of proactive management plans for EoL patients to enable death to be achieved in the preferred place and fewer hospital episodes in the final month of life (Operational Commissioning Plan, Local Ambition 3)

Provide clinical leadership and assurance to EPaCCS phase 1

Monitor rollout of EPaCCS phase 1 deliverables, manage issues / feedback and develop a specification for phase 2

A robust register of patients nearing the last year of life to enable all care providers to be aware of enhanced end of life care to meet the needs of patients (Operational Commissioning Plan, Local Ambition 3)

Receive reports / evidence on efficacy of the HomeFirst CNS pilot

Improved quality of care and support for carers caring for EoL patients (Plan on a Page)

Clarify process and timescale for implementing the GP Discharge Summary CQUIN

Patients and carers achieving their preferred choice of place of death (Plan on a Page)

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Appendix 1

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Appendix 2

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Appendix 3 EOL Service Mapping 2014/15

Organisation Description Comment

ENHT

7 Day CNS Working Palliative care cost and volume contract from 1/10/14 at local tariff price. Services to QE2 and Lister - specialist advice and support regarding palliative care and end of life needs ie; complex symptom management, holistic needs assessment, advanced care planning, preferred place of care & death. Service is provided 7 days per week - Monday to Sunday 9-5pm face to face contacts and, out of hours is supported by a telephone advice line Mount Vernon Cancer Centre Michael Sobell Specialist Palliative Care Unit - All patients have access to an holistic/multidisciplinary team of health professionals 24 Hour Advice & Support LIne - 24 hour, 7 day per week, 365 day per year, specialist advice and support line Specialist Palliative Care Service to Mount Vernon Cancer Centre

Hospital Support Team – Specialist Palliative Care PA - SPC Consultant Lecturer / Practitioner in Oncology SPC Social Worker – ENHCCG Element Admin Support- Eastwood 4 x PA Consultant Sessions at MVCC Clinical Nurse Specialist for Mount Vernon Cancer Centre MVCC 24 Hour Telephone Helpline Network Specialist Palliative Care Audit Lead

MDT Co-ordinator

PAH

End of life Discharge Facilitator as part of the PAHT Discharge Team currently vacant, new post holder appointed, anticipated start date Feb 15 2 WTE Specialist Palliative Care CNS MDT Co-ordinator (For Palliative Care, Not specifically for EOL) Consultant sessions - 4 sessions per week, 2 as previously plus 2 from an acute medicine consultant in the trust who has completed a diploma in palliative medicine.

On average 2 - 4 patients seen per session in addition to education role and attending MDT's.

Palliative Care Helpline - Trust access the St Clare and Isabel Hospice advice line at no cost Records of calls will be held by the Hospices

B&CF

HCT

Specialist Palliative Care Service Contracted to provide patient contacts. Marie Curie Overnight Nursing service separated out.

Integrated Community Teams

There will be a lot of EOL care being undertaken. 600 patients in contact with the ICTs with a referral reason of EOL care between April 13 and Feb 14. Overall, these patients had 7,272 contacts between April 13 and Feb 14. Activity being verified

SEPT Tier 4 Clinical Psychologist for Cancer and Palliative Care Garden House Hospice

12 inpatient beds at their location in Letchworth and also operates a Hospice at Home Service. The service also delivers day care support and a range of other support services such as bereavement groups. Hospice also provides a Telephone advice line

CCG commission inpatient beds and hospice at home service. They are also commissioned by Bedfordshire. NHS Contract in place.

Isabel Hospice

12 inpatient beds at their location at the QE2 Hospital site and also operates a Specialist Palliative Care nursing service and a Hospice at Home Service. The service also delivers day care support and a range of other support services such as bereavement groups and satellite day clinics and services in community locations. Hospice also provides a Telephone advice line Isabel Hospice also covers its catchment area for Specialist Palliative Care (Clinical Nurse Specialists) in the Community whereas HCT cover the rest of the County. This is purchased directly by the CCG separate from the contract with HCT

CCG commission inpatient beds, hospice at home, telephone advice line, and specialist palliative nurses. NHS Contract in place CHC Fast Track Beds Pilot in progress - 2 beds

St Clare Hospice

Provides access to inpatient beds on the outskirts of Harlow, primarily as a localised catchment area for Hertfordshire patients living on the Essex / Herts border. ENHCCG commissions only inpatient beds from the service. Hospice provides hospice at home, telephone advice line and bereavement support for patients and their family's.

CCG commission inpatient beds. Majority of Herts patients are from Bishops Stortford. They are also commissioned by Essex. NHS Contract in place.

Keech Hospice

Childrens hospice services

Marie Curie Overnight Nursing service (Hertfordshire wide) provides planned overnight home visits for patients (9hrs at a time, either HCA or RGN) identified by their key worker when other planned services are not available, nursing care to facilitate a patient’s choice to stay at home, and Carer support immediately after death. The service is jointly commissioned under one contract with East and North Hertfordshire CCG leading and Herts Valleys CCG as an associate.

This service was previously part of the HCT block contract up until November 2013. Developing a Contract and potentially a cost by case figure for contracting for 14/15.

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Marie Curie Marie Curie Discharge Liaison nurses work out of both QE11 and Lister. The post holders will identify service users in any acute Hospital settings and coordinate the discharge of service users at the end of life. The service will facilitate the timely and orderly discharge of service users who are within the last 12 months of life, from hospital to a preferred place of care as identified through (but not exclusively), advance care planning and (where applicable) AMBER care bundle processes. The service will also contribute to the reduction of length of stay and number of deaths in hospital and increase the numbers of service users with an advance care plan thus facilitating individual choice through achieving preferred place of care / death. Whilst the service is jointly commissioned under one contract with East and North Hertfordshire CCG leading and Herts Valleys CCG as an associate, the service has been split 50 / 50 (with one full time post in East and North Herts and one in Herts Valleys , (the posts are both for Band 7 Discharge Liaison Nurses).

NHS Contract in place.

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Appendix 4

End of Life Care CQUIN Schemes:

Current Schemes: ENHT

Implementation of individual care for patients at the end of life - The Hertfordshire palliative care network has developed an individual care plan for the dying person to help professionals to deliver high quality care. The care plan was piloted in the Trust during 2014 with a view to implementation on all wards by early 2015. The scheme includes a comprehensive education programme for all levels of medical and nursing staff within the Trust who are involved in the care of dying patients. Training is likely to require multiple media e.g. e-modules, lectures and seminars, micro-teach sessions, literature, ward based and multi-professional learning.

PAH This scheme builds on the collaborative end of life work undertaken by St. Clare, SEPT and PAH in 2013/14. The focus of this CQUIN is the continued education and training of all patient-facing staff in End of Life Care and Advance Care Planning (ACP) and the continued use, and further roll-out of, ACP. The CQUIN requires EoLC training to be completed by agreed groups of staff, with quarterly milestone targets for numbers and % of staff successfully completing the specified training modules. From April 1st 2014 all new clinical staff who have regular contact with end of life patients and their families / carers will complete the East of England End of Life Education Programme 10 module e-learning or attend direct teaching based on the East of England End of Life Education Programme within 6 months of starting with the trust. 3 hour training. Non clinical staff who have regular contact with End of Life patients and their families / carers to attend direct teaching (content to be confirmed). 1 hour training to include communication, basic non-clinical symptom control, advance care planning, signposting to services.

Proposed Schemes: HCT Appropriately identifying patients likely to be entering the last year of life. Delivering

training to give confidence to staff to appropriately identify patients and to work collaboratively with hospice partners to ensure delivery of the agreed education training programme.

PAH Creation of a palliative care discharge liaison post. The post holder is intended to work at PAH and liaise closely with St Clare’s Hospice to improve transfer of care and links into the relevant community teams.

ENHT The enhanced discharge summaries to GPs CQUIN is being developed by Fiona Sinclair, Governing Body GP and Emma Lines, Elderly Care and GIM Consultant at ENHT. Work is under way to agree EoL information to be included in the discharge summary. Agreement is required from the clinicians on the level of detail that is relevant, appropriate and logistically achievable.

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Appendix 5

Role Description

Job Title: Clinical Commissioning Lead for End of Life Care

Accountable to: TBC

Time commitment: 2 sessions per month (flexible working required)

Job summary

Provide the clinical leadership to lead a local health system change, to develop a better sustainable system in which patients nearing their end of life (and the population) feel supported and have access to the most appropriate cost effective services to meet their needs.

The primary aim is to improve End of Life Care (EOLC) and to ensure that the needs of dying patients and those closest to them are paramount whilst services are commissioned and developed around them.

This role requires an integrated and system-wide approach, therefore relationships need to be established with multiple providers to ensure a collaborative care model is commissioned and provided.

The focus of this role will be on the frail elderly, long term conditions, who is identified as End of Life by their clinician and carers.

Context

The ENHCCG have prioritised EOLC with a view to apply evidence-based best practices consistently across the system.

End of life care is one of the greatest challenges we face as a society and a health system.

• 1% of the population dies each year in the UK. • 85% of deaths occur in people over 65. • 40–50% of those who died in hospital could have died at home.1 • 70% of people do not die where they choose. • £3200 – the cost of every hospital admission – average three in final year.

1 National Audit Office report, 2009 14

Whilst some steps have been taken to improve the quality of care for EOLC patients, there is a belief that more could be done in this space2. Therefore, NHS Mandate has identified EOLC as one of the first priorities.

Objectives

• Support the implementation of all recognised end of life care tools • Work with the health and social care professionals who deliver end

of life care to ensure best practice is available

• Improve working across organisational boundaries in sharing best Practice • Improve clinical co-ordination between services and raise awareness of end of life issues • Contribute to the process of long term sustainability of the end of life care service developments. • Provide expert professional advice and support to other health professionals.

Responsibilities

To support the delivery of the EOLC implementation plan by:

• Engagement of key stakeholders with a view to improve awareness and confidence of GPs and community nurses in the new pathway

o To establish relationships with multiple providers to ensure a collaborative care model is implemented.

o To promote information sharing. o Communicating issues with key stakeholders.

• Breaking down of barriers to adoption of the pathway o Through patient engagement. o Working with groups from community services and other providers relevant to this

process. • Implementation of recommendations for improvement

o Through input into EPaCCs development. o By the development a directory of services, which is universal and easy to use to support

the EoL pathway implementation. o To provide expert opinion on the delivery of training packages. o To input into the review and development of current service specifications for

commissioned services. o To provide clinical input into Business Cases for the recommissioning of services. o To provide clinical leadership in the engagement with the EoL Forum.

Key relationships will include:

• Locality forums, GPs and practice teams • Patient groups • Carers • Community hospitals and hospices • Hertfordshire County Council ( • Hertfordshire Community Trust • Hertfordshire Partnership Foundation Trust • Local Authorities • East & North Herts Hospital Trust • Pharmacy and other healthcare professionals • Other NHS and Independent Sector providers

2 RCGP Commissioning Guidance in End of Life Care, Prof. Keri Thomas and Dr David Paynton

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General Clinical Commissioning Lead roles are expected to:

• adhere to organisational policies and procedures and relevant legislation including the requirements of any professional bodies

• to maintain satisfactory personal performances and professional standards and to achieve agreed objectives for their role

• attend mandatory training as identified by the organisation

Person specification

The person specification is based upon an assessment of the core competencies required to lead in this area of work. They are split between behavioural competencies (a set of behaviours that successful candidates for the post are capable of demonstrating) and technical competencies (those professional and technical skills required to specific aspects of the post).

Behavioural competencies

• Ability to motivate teams and individuals by harnessing their energies and talents through: o Releasing talent and empowerment; o Inspiring teamwork and enabling effective teams; o Articulating a clear vision for the Team.

• Making sound judgments and bringing in new ideas in complex situations including: o Demonstrating analytical insight; o Being able to establish key elements and priorities in complex situations; o Encouraging innovation.

• Understanding the strategic context of the organisation and demonstrating environmental

sensitivity

• Be highly regarded as a clinical leader, beyond the boundaries of a single practice;

• Personal resourcefulness to lead change and reach goals: o Acting as a champion of change; o Willing to be bold and take the initiative; o Focusing on critical key issues; o Embodying the values of the NHS and the public sector; o Achieving delivery of results.

• Working across organisational, professional and cultural boundaries and achieving results through

collaboration, co-operation and communication; o Willing to take a high profile both internally and externally; o Using both formal and informal communication networks; o Sharing information and exploring opportunities; o Building and developing relationships; o Able to influence change.

Technical competencies

• Evidence of negotiation and influencing skills outside your own practice • Proficient in change management and service redesign • Ability to work with a range of clinicians in a complex and changing environment • Excellent communication skills

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7. Map of Medicine

Description: Implementation of a referral management tool Map of Medicine that will support practices to manage referrals in a way that will standardise patient referrals in line with localised best practice, manage variations and realise savings in line with QIPP targets.

PROJECT SUMMARY

KEY MILESTONES

Date of Update: 23.02.15

RISKS AND ISSUES

ACHIEVEMENTS/COMMENTARY • Agreed Regal Chambers and Central Surgery to trial MoM from

beginning of January . Some problems experienced in Regal Chambers updating MoM on system during log in process . Older version of MoM had been installed . Updates made to system in November 2014. Required update of MoM across whole system. Delay of two weeks in rollout while this was completed.

• N. Herts Locality Implementation of MoM commenced in this locality from 10th Feb however practice managers suggested this was not the right time to start the roll out as they were busy with end of year deadlines e.g. QOF. Would prefer to begin rollout in April.

• Consulted with locality managers, Fiona Sinclair , Robin Christie and Trudi Southam after this feedback and was agreed those that wish to delay implementation can do so until beginning of April. However those Locality managers who are willing to participate can continue. Remaining Localities

• Asked Locality managers to consider approach to rollout process. • Asked Locality managers to ensure Locality leads engaged and on

board prior to rollout • 29 pathways on system at end of February • Over 80 referral forms on system with more to be added as rollout

progresses • Recently recruited Programme Office team admin to be trained on

MoM to ensure continuity in absence of current project support. • Clinical leadership currently provided by Gokul Krishnamoorthy. He is

leaving his practice in March . Another Clinical champion needs to be identified.

Objectives: Reduce unwanted variation in care; local health professionals work to the same protocols and care pathways Implement best practice through links to NICE and other clinical evidence based guidelines. Support CCGs to deliver improvements against quality indicators e.g. QOF, CCG outcomes indicator set etc. Enabling patients to be treated closer to home reducing inappropriate referrals to secondary care through improved primary care and community care assessment and management

Risk:

Previous Current Forecast

RAG Risk/Issue Description / mitigation

Postpone activating system on PC’s because of work load of practice managers at year end

Agreed to delay N. Herts and further implementation across remaining localities. Any practice within N Herts willing to proceed with implementation asked to continue and provide feedback

• Establish Clinical Pathways Assurance Group • Train Programme office staff in MOM • Roll out MOM access to GP practices starting with Regal

Chambers and N. Herts IT Upgrades • System upgrade and MoM software/ Installation of Integrated

word on System One /MOM- December 2014. This is IT up grade only. GP practices to access MOM will need login details and password which will form part of phased rollout .

Completed by: Fiona Oliver Project Lead

7. Map Of Medicine ( part 2)

Localised pathways

Date of Update:23.02.15

Fiona Oliver

Name When Published Abdominal Hernias Dec-14 Acute Knee Pain Feb-15 Back Pain Jan-15 Bunions Dec-14 Carpal Tunnel Syndrome (CTS) Dec-14 Cosmetic Breast Surgery - Female Dec-14 Cosmetic Breast Surgery/ Gynaecomatia - Male Dec-14 Dermatology for >16 years old Feb-15 Divarication of Recti Dec-14 Division of Ankyloglossia (Tongue Tie) Feb-15 Drug Titration and Review of Neuropathic Pain Feb-15 Dupuytren's Contracture - Assessment Dec-14 Focal Knee Swelling Feb-15 Fungal Nail Dec-14 Ganglion Dec-14 Glue Ear Dec-14 Hand and Wrist Pain Jan-15 Initial Management of Neuropathic Pain Feb-15 Knee Pain Management Feb-15 Management of Acute Knee injuries presenting to A&E Feb-15 Management of Haemorrhoids Feb-15 Management of Snoring in Adults Dec-14 MRI Knee Feb-15 Shoulder Pain Jan-15 Suspected Cancer for <16 yrs (ENHT) Jan-15 The Management of Overweight & Obese Adults Jan-15 Tonsillitis and Tonsillectomy Feb-15 Varicose Veins Dec-14

7 . Map of Medicine

Date of Update: 23.02.15

:

Completed by: Fiona Oliver

ENHCCG Map of Medicine Internal Governance Flowchart

Governance Currently pathways are approved for MoM initially by Rachel Joyce and then by Robin Christie who is acting as second approver in the absence of a clinical lead for the project. A more robust governance process has been developed and will be introduced once a clinical lead has been identified for the project.

ENHCCG require a pathway to be developed/

review date reached

Identify appropriate project group

Consider allocation to Map of Medicine Editor

Agreed localised pathway and the local information to be attached

Map of Medicine Editor to complete sign off form and checklist

Review of localised pathway and local information by Clinical Pathways

Advisory Group (CPAG)

Final clinical approval by two members of CPAG

Publication of localised pathway

Map of Medicine Publisher to add into the localised pathway the local

information nodes and referral forms

Users of map or medicine can get support with any problems with Map of

Medicine

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