East London Health and Care Partnerships Population Health ... Digital Roadmaps within each geography of the STP outline the digital ambitions across ELHCP. These individual health

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East London Health and Care Partnerships

Population Health Analytics Assessment – Final Report February 2018

This report and the work connected therewith are subject to the Terms and Conditions of the G-Cloud Order Form dated 19 September 2017 between East London Health and Care Partnership (ELHCP) and Deloitte. The report is

produced solely for the use of ELHCP for the purpose of assisting management with their assessment of the Population Health Analytics. Its contents should not be quoted or referred to in whole or in part without our prior written consent except as required by law. Deloitte LLP will accept no responsibility to any third party, as the report has not been prepared, and is not intended for any other purpose.

Deloitte Confidential: Public Sector – For Approved External Use

2 Deloitte Confidential: Public Sector – For Approved External Use

Contents

1 Executive Summary 3

2 Introduction 6

3 Summary & Recommendations 12

4 Key Observations 29

Appendix A – Scope and Approach 36

Appendix B – Population Health Analytics Maturity Matrix 38

Appendix C – Interviewees 39

Appendix D – Interim Operating Models 41

Appendix E – Glossary of Terms 43

Statement of Responsibility 45


1 Executive Summary

Based on the work undertaken, we conclude that the current population

health data platforms within the East London Health and Care Partnership

provide a maturing capability with the potential to enable advanced

population health analytics going forward. However, current analytics

capabilities are significantly less mature. Analytics capability will be

essential to releasing benefit for the Partnership through the identification

of population health insight to drive change in clinical care delivery. An

integrated approach should be taken to developing these capabilities,

based on population health need and efficacious use cases.

1.1 Overall conclusion

Population health analytics capabilities are recognised as being essential to enable the implementation of Accountable

Care Systems (ACSs). Significant progress has been made by ELHCP in establishing the east London Patient Record

(eLPR), providing a shared care record through the integration of Cerner and EMIS systems within the STP.

Additionally, the Discovery Programme has established a new data service for the local geography, collating data

from primary and secondary care to enable analysis for purposes of improving patient care and outcomes. Sharing

and aggregating data in this way is bringing to patients and clinicians.

Based on the work undertaken, the existing digital strategies, activities and platforms provide a good foundation for

the further development of population health analytics capabilities across the East London Health and Care

Partnership (ELHCP). These capabilities now need to be further developed and disbursed across the partnership.

The use of data within ACSs in the future will be fundamentally different to the way in which data is currently used

in the delivery of healthcare. Currently, data is primarily used as a tool to support the existing operating model

requirements of contract monitoring and performance management. The complexity of the clinical data sets, and the

sophistication of analyses required to determine population health needs, and to measure improvements in outcomes

for patients and service users, is a significant change, and greater than that which currently exists within health

systems across ELHCP.

In an ACS, data will be used to drive service delivery and support service improvement. As the commissioner and

provider separation is removed, both the local datasets and data models change to enable analysis, drive clinical

workflow and promote patient activation. The oversight and assurance role of bodies within a partnership responsible

for both providing and commissioning services changes from a process of contract management over healthcare

providers to an assessment of how to use available resource to enable the best outcomes. The maturing governance

structures, digital capabilities, leadership vision and clinical aspiration of the ELHCP health system provide a strong

foundation for delivering these future state requirements. However, significant change is required to align analytical,

operational, clinical and financial capabilities on a systematic basis to develop strong population health capabilities

to support frontline care delivery within ELHCP.

“Access to the system is the best thing since sliced bread! The dark shadow of what was

going on at the hospital has been lifted and there are many times when tests are not sent

down the link but are on the system which shows a huge amount of time in not having to

contact the hospital.” – Waltham Forest and East London EL GP


1.2 Key observations

Existing data platforms provide an effective foundation for population health analytics

In developing the eLPR and the Discovery platform, ELHCP have established an excellent foundation upon which

further population health analytics capabilities can be built. Additionally, other datasets have been developed within

the STP footprint (refer to section 3.1.3 below), including analytics within Tower Hamlets Vanguard on patient centric

data sets and a data cube within the ELHCP transformation programme, which provide further utility for population

health requirements.

However, inconsistent knowledge and understanding of the data platforms and their capability was identified, limiting

the extent to which data platforms are being effectively used across ELHCP at present. Clinical adoption of available

datasets is also currently limited, and there is a risk of duplicative activities where existing capabilities and data are

not aligned with the vision for the STP.

Efforts are being made to engage clinical, operational and financial leaders across the STP to support the development

of understanding and capability, however the penetration of engagement has been limited to date. There is a need

to broaden the discussion regarding both the data platforms, and population health analytics capabilities, in order to

ensure a consistent understanding of the utility of data platforms available.

The existing operating model, within which data is used primarily for contract monitoring and performance

management purposes, there is an understandable focus on ensuring existing requirements can be met. This has

limited the extent to which data is both available and shared on the existing data platforms. Additionally, we

identified cautious behaviours with regards to sharing data. While it is recognised that collaboration is essential, and

the necessary direction of travel, there remains a reluctance to share data across organisational boundaries,

particularly in community and mental health services, where service tendering and consequent competition is more

prevalent.

There is a need to enhance the use and adoption of data platforms, moving from transactions and collection of data,

to using data to inform the delivery of services for the improvement of outcomes and realisation of cost efficiency.

The ability to apply the principles of a Learning Health System (refer to section 2.1 below) will be essential to support

ELHCP in the development of delivery enhancements, and sharing identified improvements across the STP.

Analytics capabilities are under-developed

Capabilities demonstrated are mature within the current operating model (refer to section 3.1), however are not

focussed on population health requirements. Analytics teams across the STP are focussed on delivering against the

current operating constructs and requirements for analytics, resulting in limited capacity and capability to focus on

the analysis of population health datasets, and the rich clinical data contained therein.

A strategic approach to developing analytics capability, focussed on specific use cases and priority patient cohorts

should be adopted. Benefits of such an approach would be further enhanced in the use of principles from Learning

Health Systems, to identify and analyse data to test clinical interventions that would improve the health of specific

patient cohorts. Engaging analytics leaders across the STP in the developing this strategy should also address

duplication in datasets, evident between existing CCG, STP structures and CSU functions.

Local Digital Roadmaps within each geography of the STP outline the digital ambitions across ELHCP. These individual

health economy plans can now be developed as a single STP-wide plan. An essential part of this forward plan will

be to ensure a co-ordinated STP-wide plan is developed to enhance the maturity and adoption of clinical information

systems across health and social care organisations. This will be foundational to developing enhanced clinical

workflow and patient activations capabilities across ELHCP. Current contractual levers or mechanisms could be

developed further to encourage improved data coverage and data quality.

The understanding and measurement of resource utilisation at patient level is a necessary aspect of population health

analytics, particularly where the financial impact of new clinical models requires assessment. There is a requirement

to focus on developing the enabling capabilities, specifically patient level costing across patient pathways, to enable

the reform of financial flows as well as developing the incentive and payment mechanisms themselves.


1.3 Summary Recommendations

Complex data systems, such as the one that will be required to enable population health and place-based care across

ELHCP, require definition and design. Through the Digital Enablement Programme, ELHCP should take the lead in

establishing the analytics delivery approach and enabling mechanisms to ensure the development of enhanced

population health analytics capabilities across the STP, while also considering the opportunity to provide a broader

leadership role for population health analytics across London. We have described (at Section 3.2) a possible future-

state approach to the use of data that may assist in realising the benefits of data analysis to identify population

health priorities, measure the impact of new care models and contribute to sustainable patterns of resource

utilisation.

Detailed recommendations to support ELHCP in progressing towards the implementation of population health

analytics have been captured in Section 3.3 of this report. In the implementation of recommendations, further

enhancements to the current programmatic approach will be required.

The scope of our work was limited to the health technology and analytics functions of ELHCP, yet we recognise that

the ability to deliver population health analytics for ACSs will be dependent on the support and collaboration of

organisations outside of the direct influence of ELHCP, such as NEL CSU, and the London Digital Programme, all of

which have a role in supporting population health analytics capabilities going forward.

Phased approach to implement population health analytics capabilities

A phased approach and indicative timing to support in implementing the future-state approach is outlined at Figure 1. This approach would be based on Friedman’s Learning Health system, incorporating with regular review, feedback and amendment cycles. Change should be implemented through interim operating models (IOMs) as summarised

below.

Figure 1: IOM highlighting the phased approach to implementing changes


2 Introduction

2.1 Context

The East London Health and Care Partnership seeks to deliver on the

principles outlined in the ‘Five Year Forward View’ by improving patient

outcomes, through partnership working and collaboration across north east

London.

The ‘Five Year Forward View’ (FYFV) called for improved integration across health and care settings. New care models

seek to improve the sustainability of the NHS, making the best use of available funding at a population-level.

Sustainability and Transformation Plans (STPs) have been developed to outline the plans for the delivery of health

and social care services, focussed on a population within a defined geographical footprint.

Additionally, the ‘Next Steps on the Five Year Forward View’ outlines the desire to accelerate and support local NHS

commissioners and providers to build upon and strengthen STPs to support the establishment of Accountable Care

Systems (ACSs). ACSs bring together NHS organisations and local authorities to take collective responsibility for the

resources necessary to deliver population health and improve outcomes.

Originally established as the north east London STP, the East London Health and Social Care Partnership (ELHCP)

has the aim of measurably improving health and wellbeing outcomes for the people of North East London. In order

to achieve its aim, ELHCP recognises the requirement to develop new models of care focussed on prevention and

out-of-hospital care, working in partnership with organisations across the STP to commission, contract and deliver

safe and efficient services.

ELHCP brings together three distinct systems across north east London, to progress system reform. The systems

within ELHCP are City & Hackney (C&H), Waltham Forest and East London (WEL), and Barking Havering and

Redbridge (BHR). A programme has been established to progress the vision of the STP, and deliver the system

design components and workstreams outlined below:

Figure 2: ELHCP Programme workstream structure


Digital leaders have commissioned a review of the population health analytics capabilities that are in place to support

the delivery of the STP’s transformation programme. Our assessment was undertaken during September and October

2017, and considered a current state assessment to enable the development of strategic and tactical

recommendations to further support the development of both digital and analytics capability within the Partnership.

Digital Enablement workstreams have been established for Shared Records, Patient Enablement, Advanced system-

wide analytics and digital infrastructure, with structures established across each geography within the STP, as

outlined below:

Figure 3: ELHCP Digital Enabler Governance structure

Significant progress has been made by ELHCP in establishing the eLPR, providing a shared care record through the

integration of Cerner and EMIS systems within the STP. Functionality enabled by the eLPR includes Acute hospital

access to GP records, secondary care appointments and results available to GPs, while also providing the mechanism

cross-organisation for approval and sign-up to data sharing. Work is continuing to expand the systems and

organisations from which data within the eLPR is collated, to further enhance data sharing and interoperability across

the STP.

Within the ELHCP geographic footprint, Newham CCG, City & Hackney CCG, Waltham Forest CCG and Tower Hamlets

CCG are working in collaboration with the Endeavour Healthcare Charity on the Discovery Programme. The Discovery

Programme has established a new data service for the local geography, collating data from primary and secondary

care to enable analysis for purposes of improving patient care and outcomes.

Data within the Discovery Programme includes EMIS extracts from in excess of 100 GP practices as at August 2017

(to be updated for October 2017 figures). Additionally, admissions, discharges, and transfers (ADT) data is being

received by Discovery from both Homerton Hospital and Barts Hospital. It is the intention of the programme to

broaden the scope of the data sets collected by Discovery (including mental health data and local authority data),

while also broadening the footprint beyond the current CCGs, to cover the whole of the STP providing the basis for

an effective population health data platform.

The ability to share data and learn from good practise across the three geographies within ELHCP will be critical, and

is a key requirement for the delivery of effective population health analytics capability. Population health analytics

plays a crucial role in identifying, enabling and measuring the changes in care models necessary within effective


accountable care systems. This assessment has therefore consider the potential of the data platforms and digital

strategy available within ELHCP to deliver this capability effectively.

To ensure effective engagement in and use of the existing data platforms, the Digital workstream within ELHCP aims

to establish and benefit from the principles of a Learning Health System1. Learning health systems adopt cyclical

improvement approaches, through the use of technical and social approaches to learn and improve with every patient

who is treated across the partnership. This approach therefore informs the implementation process to test and

develop the population health platforms within the Partnership. The principles of a health learning health system,

as outlined by Professor Charles P. Friedman, are reflected below:

Figure 4: Friedman’s Learning Health System Cycle

According to Professor Friedman, any Learning Healthcare System has the following three components (Friedman 2015):

1. Afferent (Blue) side: Assemble the data from various sources Analyse the data by various means Interpret the findings

2. Efferent (Red) side:

Feeding findings back into the system in various formats Changing practice

3. Scale: Can be institutional, national, international

In order to align with this approach we have considered our findings in the context of this learning cycle and

recommendations identified make reference to this improvement method.

2.2 Methodology and approach

The maturity of population health analytics capability can be considered against the six core capabilities, highlighted

below. Deloitte have used this taxonomy, developed through extensive use in US health systems, to inform our

assessment of health analytics capabilities across ELHCP.

Interoperability,

Integration, HIE

Connects healthcare information and data via Application Programming Interfaces (APIs), Health Information Exchange (HIE) or messaging protocols across the ACSs for clinicians and patients to access.

Data Aggregation and Management

Aggregates data from disparate sources to improve transparency across the ACS

1 Charles P. Friedman, 2014 - http://www.learninghealthcareproject.org/section/background/learning-healthcare-system


Analytics (including Risk

Stratification) Enables insight-driven analysis that is both descriptive and prescriptive

Reporting Delivers a self-serve solution for performance management across the ACS

Clinical Workflow Orchestrates the execution of activities from disparate systems constituting the care continuum and ACS

Patient Activation Enables the patient to manage their own care needs and drives

required clinical workflow.

Component activities that enable increased population health analytics maturity within each of the six capabilities

are outlined in Figure 5 below.

Figure 5: Population Health Analytics capability maturity

To further support in the definition of maturity for population health analytics capabilities, the delivery outcomes of

the capabilities outline in Figure 5 are described in their mature state below:

Clinical Operational Financial Technology and Data

People

Real-time visualisation of patient interactions with services across the care system, and

personal patient

technology

Near real-time visualisation of

resource use to enable demand profiling and system-wide variation

identification

Near real-time cost allocation and visualisation of

consistent metrics across the care system

Consistent, high quality data collection, data architecture and security across the

care system

People and teams understand the

operational and clinical requirements of data collections and can

enact the analytics requirements, to

generate meaningful

insights


Drawing upon our population health analytics maturity framework, we considered population health analytics

capabilities across four key lines of enquiry enabling the practical linkage of the concepts outlined in Figure 5 with

service delivery within ELHCP:

i. Operational: capability to operationalise place-based health analytics to embed data analytics into

day-to-day working, enable the delivery of new clinical workflows and support patient self-help and

direct engagement in their care;

ii. Clinical: capability to harness health analytics to enable governance and delivery of clinical care and

associated research requirements through technology-enabled place-based care models;

iii. Financial: capability to use health analytics to understand and create mechanisms to manage

financial flows and payment mechanisms to support the achievement of place-based care outcomes;

iv. Technical: capability of technology, analytics and associated governance frameworks to deliver and

scale to provide the technology infrastructure required to support place-based care.

In completing our assessment of population health analytics capabilities, we met with 58 Operational, Clinical,

Financial, and Technical stakeholders from across ELHCP, through both interviews and workshops. Three workshops

were held with Operational, Financial and Technical leads to enable consideration of leading practise, barriers, and

future ambitions for population health analytics within ELHCP. A list of all stakeholders we met with in performing

our assessment has been captured in Appendix C below.

An outcome-based population health analytics maturity matrix which outlines the mature state capabilities against

each of these lenses can be found at Appendix B.


2.3 How to use this report

To aid the reader, we have outlined below how the report has been developed, and how it should be read in figure 6

below.

Figure 6: How to read this report

2.4 Acknowledgement

We would like to thank all staff from across ELHCP for their co-operation during this assessment. A list of the staff

involved during the assessment is included at Appendix C.


3 Summary & Recommendations

3.1 Current state assessment

The functions and structures associated with health analytics across ELHCP are complex. Analytics functions and

capabilities are dispersed across multiple organisations, within primary, community, mental health and secondary

care, commissioning support unit and local authority organisations. They are represented diagrammatically in Figure

7 below.

Figure 7: ELHCP Organisations

Analytics functions and capabilities in each organisation within ELHCP are aligned with current organisational

requirements and reporting priorities focusing on the financial, statutory and performance reporting requirements.

Additionally, North East London Commissioning Support Unit (NEL CSU) provide analytics support to organisations

across the ELHCP, but are not a formal member. Based on their understanding of the analytics capabilities across

the Partnership, it is recognised by ELHCP Digital Leadership that current operating model will not support the delivery

of effective population health analytics and that enhanced analytical capability is required going forward.

3.1.1 Population Health analytics capability maturity

In performing our assessment, we sought stakeholder perspectives on the relative maturity of their analytics

capabilities within ELHCP constituent organisations. The population health analytics capability curve was used in

order to assess maturity in a consistent manner.

Organisational assessment

The current-state assessment presented in Figure 8 below, informed by discussions with stakeholders across

organisations within the STP, aggregates maturity by geographical region within the STP. In doing so, variances in

maturity across local geographies within the STP are highlighted.

The maturity presented within Figure 8 below has been determined on the following basis:

Self-assessment of maturity, informed by discussion with stakeholders;

Assessment of maturity considers the current operating model for analytics, and the extent to which current

maturity supports its delivery; and

Provides an organisational view of maturity, aggregated by STP geography.


Figure 8: Population Health Analytics capability maturity within current operating model, informed by stakeholder discussions

STP Leadership assessment

Digital enablement workstream leaders also considered maturity against the same assessment framework, and

determined an additional view of current state maturity across each geography in the STP, based on the ability to

aggregate and analyse data across patient pathways, in accordance with the desired future geographical constructs.

Maturity considered against a future operating model is presented in Figure 9 below:

Figure 9: Population Health Analytics capability maturity, considered against the target operating model, informed by Digital

Enablement workstream leadership (* BHR assessment to be completed by digital leadership)


The assessment (Figure 8) indicates participant’s views of the maturity of their analytics capabilities within the

current operating model. As such, capability maturity reflects the existing programmes of work within the local

geographies and organisations. The current maturity also reflects the beginning of the adoption of digital platforms

that enable population health activities across the all the geographies in the STP. Specifically the assessment

indicates:

1. Comparatively high levels of maturity for interoperability and integration capabilities as evidenced in the use

and adoption of the eLPR and Discovery platforms. Notably the core building blocks of patient master index

and data security architecture are present providing a firm foundation for other activities.

2. Developing capabilities in data aggregation and reporting capabilities reflecting the use of data to support

the commissioning and performance accountability frameworks. However it was noted by participants that

this maturity assessment reflects the ability to aggregate data according to the current organisational

constructs and does not indicate that data can be aggregated at a patient level across care pathways or for

a specific patient cohort within a geography.

3. The analytics maturity assessment aligns closely with the views expressed by interview and workshop

participants. It indicates that the ability to use and gain benefit from the existing datasets through analytical

techniques such as risk stratification, patient cohort identification and actuarial modelling, are developing.

Whilst maturity in reporting appears higher, it is important to note that the reporting and visualisation

capabilities need to display data analysed using these techniques is yet to be developed systematically.

4. In common with other health economies, where we have undertaken similar assessments, the clinical

workflow and patient activation capabilities are still maturing. Good practice examples exist and demonstrate

an emergent, higher level of maturity. For example, the algorithms for reduced use of NSAID in people with

CVD, the increased use of high intensity statins in people with CVD and increases in anticoagulation therapies

for AF (and reduction in aspirin monotherapy) in primary care are examples drawn from a number of analyses

using primary care data that are currently influencing clinical practice and benefitting patients. In secondary

care proactive identification of acute kidney injury and major limb trauma, provide use cases that have

succeeded in delivering actionable, near real-time insights to clinicians using the data currently collected

within the health system. These areas of good practice indicate the potential to further develop this capability

and, aligned with service improvement initiatives, to generate further use cases that can demonstrate direct

benefit for patients.

5. Through discussion with operational and financial leaders, the need to use data within existing data platforms

to inform service planning and commissioning decisions across the STP was highlighted. The potential for

clinical and care patient variation analysis is significant and could be realised at pace given the integrated

datasets in place. Plans in place to increase the volume and scope of these datasets by the end of the

calendar year 2017 will further enhance the potential.

6. Patient activation capabilities offer significant transformative value to the health system. Examples of good

practice, such as the development of the ‘My Mind’ application in North East London Foundation Trust

(NELFT), indicate that the technologies can be applied effectively within specific care models. Improving

availability of on-line scheduling and access to medical records in primary care is a clear example of the

benefit of digital patient engagement within their healthcare record. The challenge now is to consider the use

of these patient enabling technologies in service re-design and quality improvement initiatives, whilst

continuing to develop the underlying infrastructure and capabilities (for example a patient health record) to

deliver the value of this change for patients.

Interesting variation exists when the organisational focus to the maturity assessment is compared with the STP

perspective. Specifically:

Maturity in data aggregation capabilities is reduced, reflecting a further requirement to share and link data

between institutions, as opposed to collecting and holding data at an organisational level. This indicates that

the foundations for data sharing are in place, and that further opportunity to share and link data sets should

be explored.


Analytics and reporting capabilities are assessed as developing, but no capabilities are assessed as being in

a mature state across the STP as a whole, reflecting the need to develop and use analytical techniques

beyond the specific existing clinical use cases identified in Section 3.1.1.

Clinical workflow and activation capabilities are identified as more mature that the perspective of participant

organisations, reflecting the leadership’s knowledge of capabilities displayed through advanced use cases. It

is acknowledged that whilst these capabilities exist in defined clinical areas, there are developing mechanisms

to scale this good practice to reach a mature state, such as Primary Care improvement supported by CEG.

It is very encouraging to see clinical teams engaged in data analysis in these specific areas and using the

insight gained from analysis to impact and improve care delivery.

We compared to the view of digital leadership, as evidenced in this maturity assessment, operational leaders

considered maturity in the analytics capabilities to be lower, citing the need to improve the quality of analysis

to inform resource utilisation decisions and the need to triangulate data sets, particularly public health data

sets with available clinical data to achieve a fuller picture of opportunities to improve care or reduce costs.

This difference of opinion is explored further in Section 3.3 Recommendations.

The maturity assessments and associated interviews have led to the development of the following key observation

regarding health analytics capability across ELHCP.

3.1.2 Discovery and eLPR platforms and capability

The Discovery and eLPR demonstrate mature capability to interoperate and aggregate data across the health

and social care geographies. There are clear plans in place to extend their data coverage and capabilities

going forward. Furthermore, increasing clinical use of the eLPR is being evidenced month on month (increase

in views of eLPR in September from approximately 60,000 to 70,000).

Clinicians interviewed were able to articulate the benefits of the eLPR in their everyday practice, specifically

valuing the tool as a mechanism of communication between healthcare organisations. It also allows clinicians

to make decisions with a wider breadth of knowledge and clinical history, thereby reducing the need for

additional telephone conversations, repeat patient visits and diagnostics. As further data sources from mental

health and community providers are added the transformational capability of the aggregated data set was

recognised and welcomed.

There is a recognised need to spread the adoption and use of the eLPR across the partnership. Some clinicians

expressed the need to have a summary of patient activities as the information available on patients at first

use was reported to be difficult to navigate, effecting the inclination to adopt the system within clinical

practice.

The Discovery platform is recognised within the partnership, and across London, as offering the capability to

undertake advanced population health analytics. Discovery also has the capability to support the further

development of specific clinical use cases through the identification of priority patient cohorts. In doing so,

additional or changed clinical interventions could positively impact the aetiology of disease or reduce the

requirement for resource usage in care delivery.

Knowledge of the Discovery platform and how to navigate the processes to access the data held within the

system were well understood within the research and secondary care clinical community. There was

significant support for use of the data to inform specific clinical use cases and improve care delivered to

related patient cohorts. However the process to access and design use cases and specific question sets to

enable access to the Discovery platform was not well understood across the ELHCP transformation

workstreams, with participants unclear as how to access or analyse the data source available to them.

In discussion with teams outside the immediate digital enablement workstream and practicing clinicians,

there is an inconsistent knowledge of the data platforms and their capability, and value to developing care

and payment models. Specifically the analytics role within the ELHCP digital workstream was not well

understood by interviewees.


Communications are perceived as well-led but there is concern that key messages are not consistently

understood within constituent organisations. Interviewees also observed that communication about the

progression of the digital plan could be improved, particularly within social care, to allow for alignment of

activities between the sectors.

3.1.3 Analytics capacity and capability

The digital and analytical capabilities within the transformation workstream are often elided. Enacting these

capabilities requires different skills and tools, particularly for population health analysis. However, these

capabilities are under-developed when compare with the digital capability in evidence.

Aggregated data sets in place form a good basis for undertaking analytics that can inform clinical care

delivery. However there is the danger of duplication with the development of a number of data sources that

could be used as the basis for this analysis. Data management systems were identified as in development

or use include:

The Discovery Platform;

A data cube within ELHCP transformation programme;

NELIE within NEL CSU;

Analytics work within Tower Hamlets Vanguard on patient centric data sets; and

‘Health Analytics’ platform within the BHR health system.

There was concern that scarce resources were duplicating work in establishing and running different data

management services and there was opportunity to identify a single dataset and realign analytics resources

to progress the use of the single dataset at a faster pace.

Capability is limited by the current constructs and requirements for analytics, with focus being applied to

contractual reporting, finance and performance within healthcare and statutory requirements within social

care. Clinicians expressed concern that limited analytics capability is therefore available to analyse the rich

clinical datasets that are available, hampering the ability to gain insight and triangulate data sources to

predict or measure the impact of clinical intervention. Analytical capability also exists within NEL CSU. These

capabilities were not being actively engaged in the development of system analytics capabilities and there

was a perception that the resource and capability was not well aligned to the requirements of the system to

develop population health analytics.

There is a recognised need for additional skills to progress predictive and actuarial modelling skills. Currently

this is being sourced as needed by organisations across the STP, with methods and tools chosen for specific

requirements.

There is a lack of alignment between identified population health needs, the aims and intentions of

transformation programmes and the data sources, data items and data coverage required to measure

progress effectively.

3.1.4 Application mapping, data governance and coverage

The applications in use across ELHCP provide a high-level of commonality, as highlighted in Figure 9 below.

We were informed of quality improvement activities undertaken to enable consistent data capture within

EMIS through the use of standardised templates and data fields linked to codified data structures that enable

the use of datasets comparatively across populations. This provides a strong platform for further use of data

from native systems through aggregation in population health platforms.

However during interviews with both CIOs and transformation practitioners, issues were identified with the

multiple instances and software versions, limiting the value of aggregated data. This was further

compounded by the differing levels of adoption and methods of use of the systems. For example the same

field in the same version of the software may be used to enter different data in hospitals treating the same

patient cohort, thereby making basic activity comparisons between providers challenging.


Figure 10: Core clinical applications across primary and secondary health care in ELHCP, including eLPR integration

There was recognition of the need to progress work relating to data lineage, governance, coverage and

assurance as part of the work to progress digital and analytical maturity across ELHCP. Participants identified

that there was opportunity to do this in support of specific clinical initiatives, thereby increasing clinical

engagement in the definition, collection and use of the data recorded as part of the patient pathway. The

aspiration for this alignment of purpose and process was clearly in evidence, although practitioners were

struggling to enact their aim, referencing lack of governance forums and processes on data quality and data

recording as a concern.

The Barking, Havering and Redbridge system demonstrated increased multiplicity of primary care systems,

with the majority systems indicated in Figure 10 above representing only 50% coverage, with the use of

Vision in 40% of practices. This variation in core systems could lead to the generation of datasets that are

not comparable and require additional data manipulation to create useable datasets. The Medway patient

administration system (PAS) is in use at Barking, Havering and Redbridge University Hospitals NHS Trust

(BHRUT). Designed as an administration system, it may not have the breadth and depth of functionality

required when compared to an integrated EPR. This indicates that in the long term further investment in

clinical systems may be required to collect the rich clinical data sets through the process of delivering care

that will enable mature population focused analytics across this health system.

3.1.5 Alignment of digital, analytics, transformation and commissioning capability to achieve benefit

for patients

The history of innovation and partnership working in geographies across the Partnership, particularly in the

work of Tower Hamlets Together vanguard initiative, has generated an enthusiasm for and commitment to

improvement and change. Specific examples of improvement that have the potential to utilise the benefits

of the existing digital platforms include the social prescribing initiatives in place across Tower Hamlets CCG

and the quality improvement programmes within primary care that are being rolled out across all Partnership

CCGs.

Evidence of quality improvement teams accessing the rich datasets held with the existing data platforms was

not identified. Such data could be used to assess the impact of changes implemented and provide useful

data to inform service evaluation. Opportunity exists to increase the access to the existing data platforms


and undertake fast-paced analyses, or ‘sprints’, to identified patient cohorts where changes in clinical practise

could improve care and reduce cost. There was an appetite to undertake these activities, recognising that it

would be possible to identify further use cases quickly and consider how best to implement them using

existing improvement initiatives. Even if implementation was not possible for some use cases, testing data

quality through analysis will further highlight opportunities to improve quality and generate further

opportunities to improve patient care.

3.1.6 Digital maturity and adoption

Digital maturity and adoption vary significantly across providers, particularly secondary and community

providers. As such the rich clinical data needed to progress population health analytics and link findings to

clinical outcomes will be missing from the data sets held within both the eLPR and the Discovery platform.

Each constituent organisation has plans to improve maturity in their digital capability, with progress achieved

at the Homerton Hospital NHS Foundation Trust and Barts Health. However, workshop participants articulated

that resource constraints will delay digital maturity and consequently the ability of clinical teams to collect

clinical data in structured formats to enable sharing of consistent data sets to realise the benefits of population

health analytics in its fullest extent.

Community and mental health providers were also demonstrating increased use of technology in the

recording of clinical care. However they also cited the relative immaturity of national datasets, definitions

and contractual mechanisms as a reason why the data captured focused primarily on the recording of clinical

care activities, rather than the collection of diagnostic, care planning or procedural data.

Variation in digital maturity and adoption will impact the ability of the Partnership to leverage the value of

the population health platforms they have developed. However there is the clear will and aspiration to make

incremental improvements of digital capability within provider organisations which will create a good

foundation for the progression of population health analytics around specific patient cohorts and in

partnership within relevant clinical teams.

3.1.7 Financial flows development

ELHCP are taking active steps to consider the future of financial flows in their partnership and determine how

resource allocation could be undertaken differently within an ACS. A consultation securing the views of

participant organisations has recently closed, and information is being collated to form the basis of forward

plans

In support of this initiative a workshop and interviews where held with senior finance leaders across the

partnership. The workshop focussed on understanding the maturity of the datasets underpinning existing

financial mechanisms and the plans to progress the maturity of these datasets to support the development

of population-based resource allocation. Informed by the workshop and interviews, a maturity assessment

of financial data sets is shown below:


Figure 11: Population health financial flows maturity assessment

In completing this assessment financial leaders were of the opinion that increasing maturity of clinical

datasets was the priority for the Partnership and that associated financial data sets could be built upon good

quality clinical data. However in assessing their current maturity, participants considered that:

1. Existing financial flow mechanisms and supporting datasets were mature in their use within the

current operating model across provider settings;

2. A number of provider organisations identified progress in the development of service line reporting

and patient level costing. Maturity was therefore differential across providers, with underlying data

capture, consistency of costing methodologies and capacity of finance and analytics teams to

support the development of capability, particularly PLICS, identified as rate limiting factors; and

3. Whilst the concept of resource profiles at patient level was well understood and agreed, capacity

and capability within costing functions was not sufficient to progress this requirement or to consider

linking datasets between organisations to progress pathway costing. Care pattern variation or

assessment was recognised as providing an excellent basis for such work and the need for a

consistent costing methodology and approach at pathway level was seen as an important

requirement to progress this requirement.

The maturity assessment outlined the following key observations regarding relationship between financial

flows requirements and health analytics capability across ELHCP:

1. Clear commitment to leveraging data to modernise approaches to commissioning. The current

consultation process was seen as a good basis upon which to build a future consensus of opinion

and develop an agreed strategy. However, concern was expressed about the financial impact of

any changes and the potential to shift demand pressures and create financial instability if moves to

capped or capitated models were undertaken too swiftly.

2. There are currently no contractual or performance mechanisms that encourage resource utilisation

at a patient level. A consistent view was expressed that effort should be focused on developing a

common approach to the use of existing capabilities such as SLR and PLICS in developing the


underlying data sets to inform the development of new payment models. Designing and agreeing

a consistent costing method was seen as important by provider organisations of all types to enable

the development of patient pathway costing over time. Without a common agreed method, data

sets and resource allocations would be inconsistent between organisations and therefore not

comparable when linked across pathways.

3. Participants observed that the existing accountability mechanisms within the contract for services

that focus on data quality where not employed. This is resulting in a lost opportunity to focus

services on the collection of data that would inform both clinical care and the development of

financial flows mechanisms into the future.

4. Opportunity exists to develop a progressive approach to financial flows that focus on engaging

clinicians and organisations in improving data collection and data quality. Current CQUINS

mechanisms were identified as an opportunity to incentivise the collection of clinical data and

associated activity data items within and between organisations across an identified patient

pathway. Applied effectively this mechanism could help to address issues of data coverage and

data quality, encouraging organisations to agree data items across patient pathways and focus on

continually improving the accuracy of data collection.


3.2 Future state

It is important to consider the desired future state for ELHCP and the delivery of population health analytics in order

to progress the recommendations and next steps identified in the assessment. Informed by discussions with ELHCP

management, and the Transformation Programme, we have identified the following key characteristics of the future

state for population health analytics across ELHCP:

Single integrated clinical data view, populated from all organisations within the STP, and available to view,

update and analyse in near real-time by clinical and non-clinical staff;

Active approach to population health analytics to drive improvements in patient care and health and wellbeing

outcomes, using a common, accessible visualisation platform;

Progress and activities aligned with the needs of patients and service users, based on an informed

understanding of population need;

Support the enactment of an effective learning health system, in which data are used to inform the

development of best practise which can be tested and shared across constituent organisations to improve

outcomes for patients.

ELHCP digital leadership have identified through the workshops with colleagues their aspirations for their future

analytics capabilities using the maturity model. The aspirations for capability by 2021 across the STP are highlighted

below:

Figure 12: Future state aspiration for STP aggregate maturity

Presented in the table below, Digital leadership identified the outputs of the enabling digital and analytical capabilities

both now (highlighted as orange below) and in the future (highlighted as green below), describing the forward

aspiration of the partnership.


In considering the current state assessment, future-state capability aspirations and the defining characteristics of

digitally-enabled care, we have developed a future-state approach to the use of data that may assist in realising the

benefits of data analysis to identify population health priorities, measure the impact of new care models and

contribute to sustainable patterns of resource utilisation.

Figure 13: Illustrative future state structure

The maturing governance structures, digital capabilities, leadership vision and clinical aspiration of the health system

provide a strong foundation for delivering this future state. Specifically future state system characteristics would

include:

1. Further development of the integrated data view, with the additional navigational and visualisation tools at

patient and population level to allow clinicians and service planners to access the use the data more readily.

Over time, consideration should be given to merging the eLPR, Discovery, and other data platforms as richer

clinical datasets are available in near-real time and the appropriate analytics techniques develop to allow

interrogation of the data.

2. Aligning the service improvement activities that are the focus of the transformation workstreams with the

local population health need identified in the localities. This will enable the creation of priority patient


populations or clinical use cases, where the combined capability of clinical expertise, analytics and quality

improvement can be combined to improve patient care.

3. Ensuring that the governance model in place to lead ELHCP adopts, promotes and resources the priorities

and builds multi-disciplinary teams around the specific clinical use cases. Analytical capability across the STP

should be co-ordinated to design, test and measure the impact of new care models on an iterative basis in

line with the principles of Friedman’s Learning Health System.

4. Aligning incentives to encourage the collection of data, improvement in the quality of care and the use of

data in clinical care will be an important mechanism to enable the measurement of the impact of new care

models. There is an opportunity to reward improving data coverage and data quality and thereby incentivise

organisations and clinical teams to collect data as part of the process of care. Particular benefit could be

achieved by aligning these incentives across a patient pathway.

5. Consider the development of specific clinical and business processes that are aligned with delivery support

functions and oversight and assurance functions. Clarity in the use of identified and de-identified data and

the purposes for which these data will be used will be critical to building and maintaining public confidence

in the way patient level data is used to deliver and plan healthcare. As confidence in the use of data builds

it will be important to consider carefully how those data are used for oversight and assurance purposes. Data

must continue to be seen as a tool to improve patient care and not an instrument that is used punitively to

compare performance of organisations. The focus on improvement, reducing variation and meeting

population health needs will lead to improved performance if service models are effectively enacted.

6. Enabling capabilities are required and should be systematically developed to allow the process of using the

available data to be transacted effectively. Examples include codifying and reusing information governance

policies and procedures, applied consistently across the health economy, developing consistent data

definitions and standards and creating (or using) effective verification services for patients and staff are all

critical to continuing to mature the population health analytics capabilities within the Partnership.

7. A formalised quality improvement method based on Friedman’s Learning Health System, which builds on the

existing afferent capabilities, creating additional analytical capability to identify patient cohorts and interpret

the findings of analysis. Efferent processes to design, test and evaluate care models that provide an

efficacious method of changing clinical practice, possibly as part of existing quality improvement

methodologies, could then be deployed to embed change and provide the basis for scaling benefit across the

partnership. Developing a capability; people, skills, tools, methods and processes, that can define, refine and

repeat this method and train others to use it, will be an important vehicle for using the insights from

population health data and achieving tangible resulting change.


3.3 Recommendations

In performing our assessment, and in analysis performed by ELHCP2, practical benefits for patients and staff have

been acknowledged by clinicians as a result of work undertaken to date with the adoption of eLPR have been

highlighted:

Further work is necessary to continue to support the vision for ELHCP and deliver further benefits for clinicians and

patients in improving outcomes. The following recommendations have been identified, structured according to the

current state assessment in section 3 above. Collectively they represent the key areas required to progress towards

the future state approach identified in Section 3.2:

# Recommendations

Discovery and eLPR platforms capability

1

Raise awareness of digital platform functionality and capabilities

A clear articulation of the ELHCP digital infrastructures and platforms that hold data should be developed to raise the awareness of the platforms more broadly across the STP. Functionality across digital platforms should be differentiated from the analytics capabilities required to use the available data in order to gain insight into the healthcare needs and resource usage across the economy.

To further support in raising awareness, digital infrastructure and platform capabilities should be articulated alongside implemented use cases across operational, financial and quality improvement teams within the Partnership.

2 Analysis undertaken by ELHCP, captured within “Benefits study - evaluation of the East London Patient Record, version 1.1”


# Recommendations

2

Define the ELHCP analytics delivery approach

The forward analytics delivery approach should be defined and documented, outlining aspiration for the

use of a single data platform and visualisation capability across ELHCP. This approach will avoid

duplication of data platforms and maximise the available investment in existing platforms.

Consideration should be given to drawing together analytics capabilities across constituent organisations

including NEL CSU, to focus specifically on enabling clinical practice change through data-generated

insight. Embedding this capability within a multi-disciplinary team of clinicians and research could lead

to significant progress in developing use cases and impacting clinical pathways at pace.

3

Establish an enabling mechanism for access to and assessment of the data in the Discovery

Programme

Define and implement a mechanism, supported by ELHCP, to enable multi-disciplinary and services teams

to access the data held within the Discovery Platform to support the completion of population needs

analysis. Specific focus should be given to understanding patient cohort service usage to enable the

development of appropriate hypotheses and question sets that can be used to design data queries.

4

Support a broader London role in population health analytics

Build upon the capabilities developed within East London, and position the STP to work in partnership

with the London Digital Programme. Consider the ability of the Discovery Platform, eLPR and in the

future, the East London Data Repository, to exemplify the use of a longitudinal patient record to support

enhancements in care delivery, outcomes and research and consequently be the basis for further

development of the concept within East London and across a broader geographical footprint.

Analytics capacity and capability

5

Establish governance mechanisms to develop and agree the analytics strategy

Governance structures and processes should be established to enable the analytics strategy for ELHCP

to be developed. The structure should ensure cross-organisation analytics community engagement and

input in order to consider and agree the following:

Consistent data platforms, data management services, structures, data sets, analysis,

application sets and reporting, including where and how existing digital platforms should be

linked, or developed separately, to draw on benefits of each and avoid undue duplication in the

system;

Data quality improvement requirements and standards;

Opportunities for sharing expertise, resource and reporting approaches across clinical pathways

and organisations (see Recommendation 2);

Development of a skills framework, outlining the required analytical capabilities required and an

associated implementation and training plan; and

Identification of population health use cases to ensure data collected are complete and

comparable.

Developing frameworks based on holding health systems to account collectively for health

outcomes, will be required in the future and the effective implementation of the analytics

strategy will be essential to enact this accountability

The STP can support this aspiration by defining the wider governance systems and processes

through which revised accountability frameworks would be enacted.


# Recommendations

Alignment of digital, analytics, transformation and commissioning capability to achieve benefit for

patients 6

Assess, design, implement and measure outcome improvement initiatives

Priority patient cohorts in each of the three geographies of the STP should be determined. Specific

analytical resource should be identified to undertake a needs and care pattern assessment, in order to

design an improvement intervention.

Based on Friedman’s Learning Cycle, specifically focusing on developing efferent processes, activities

should include the following:

Population needs assessment completed by the three geographies across the STP;

Each geography within the STP should propose priority patient cohorts aligned with the services

offered, population health need and future demography;

Design outcome measures, metrics and data sets that can form the basis of a process to measure

outcomes;

Measure the impact of interventions, including both (proxy) outcomes and resources utilisation.

Consideration could be given to aligning areas of intervention with existing clinical transformation

programmes, such as primary care quality improvement or the Transforming Services Together

programme; and

Trial advance analytical techniques of actuarial modelling and predictive analysis in these areas

and consider aligning this work with a new mechanism for financial remuneration that rewards

the delivery of reduced care costs across a pathway.

This approach will enable the limited resource available to be focused on clinical areas of high priority

which are aligned with the clinical and service improvement capability required to design and deliver

change and secure an impact in each geography. Iterating this process, based on clinical use case, will

enact the principles within Friedman’s Learning Cycle, build change capability and importantly impact

patient experience and outcomes over time.

The implementation of recommendations should be phased and iterative, supported by regular review,

feedback and amendment to enable responsiveness to patient and staff needs. To assist the Partnership

in progressing to the future state approach, we have developed a series of interim operating models

(IOMS) identifying required activities over time. This approach associated tasks is outlined in detail at

Appendix D

Application mapping, data governance and convergence

7

Work with providers to further enhance common application landscape

In order to support integration and interoperability, common applications should continue to be used

where possible, across the STP. ELHCP should work with provider organisations within the STP to agree

an approach to migrating to a common version of applications in use. Specifically within the BHR system,

consideration should be given to developing a migration path to increase the commonality and

interoperability of system usage, and the functionality of systems in use to focus on the capture of clinical

data sets.


# Recommendations

8

Definition of agreed data sets and data definitions, aligned with patient cohorts

A minimum dataset and common data definitions should be agreed for specific patient cohorts. ELHCP

should establish cross-provider groups, based around identified patient cohorts, in order to agree

minimum data sets and data definitions to enable specific service improvement interventions across the

STP.

Additionally, to develop data sharing for the East London Data Repository, commissioners should work

with GPs and Local Authorities to enable sharing of patient-level data on activity, spend/cost and

outcomes (where data is available).

These groups should work under the strategic guidance of an analytics leadership group that defines the

parameters of their work in terms of the analytical tool sets and visualisation applications in use to enable

the display and interpretation of data.

Digital maturity and adoption

9

Incentivise the adoption of improved data collection and quality

In order to support the quality of data at source, existing contractual mechanisms should be used to

focus healthcare provider organisations on data quality. Specific outcome measures, metrics and data

items should be aligned to the use of contractual mechanisms such as CQUINS. By improving data

collection processes at source, data quality and data convergence across providers and patient pathways

will be improved.

Additionally, consideration should be given to aligning increased digital maturity and adoption of

electronic care records with STP accountability and reward mechanisms. This could be agreed in a

progressive manner over time, beginning with incentives to collect data, progressing to reward for

improved data quality, consistency of data item definition and then the analysis and use of data to

provide insight.

10

Develop a plan and pathway to increased digital maturity

An STP-wide plan should be developed to understand the path to increased digital maturity for existing

providers to assess the pace at which full clinical, financial and outcomes data sets will be available to

enable mature population health analytics.

Financial Flow Mechanisms

11

Develop a consistent method and approach to patient level costing across constituent

organisations within the Partnership.

Consistency in patient-level costing should be established. ELHCP should develop a strategy and

approach to the development of patient level costing capability across the partnership. Focus should

shift from setting prices and remunerating care on a fee for services basis. Instead an assessment of

costs in different care settings, and ideally across care pathways, should inform the remuneration of

care.


# Recommendations

12

Datasets should align resource to activities at patient level

The development of patient pathway clinical data sets to improve patient care should be prioritised. This

should consider how to align these data sets with the allocation of resource to activities at a patient level.

Such an approach will have the following benefits:

Create a data set of manageable size and complexity to allow for resource identification at patient

level;

Encourage organisations to collaborate on the definition of cost allocation methodologies and use

them consistently across a patient pathway; and

Create the ability to model the financial impact of new models of care through the application of

predictive techniques based on clinically endorsed models of care.

In the absence of open book accounting, system partners may use commissioning spend as a proxy for

cost. Systems should work jointly develop apportionment methodologies at patient level where possible,

and use these to enable initial cohort and pathway analysis.


4 Key Observations

We have identified the following observations in performing our assessment, and presented these on a thematic

basis. Associated recommendations, as captured in the Executive Summary and Section 3.4, have been aligned with

each observation. Recommendations have been developed to address multiple observations in some cases.

# Observation

4.1 Inconsistency in understanding and capabilities in respect of population health analytics

4.2 Maturing workstream and STP governance

4.3 Existing datasets may not be complete

4.4 Existing capability is aligned with, and limited by current analytics constructs

4.5 Limited incentives to capture and share data

4.6 Variation in the adoption and use of clinical data platforms

4.7 Challenge identified in the adoption of changes

4.8 Understanding and engagement within the STP on the nature and content of digital platforms remains limited

4.9 Information Governance

4.1 Inconsistency in understanding and capabilities in respect of population health analytics

Observation

The current understanding of population health analytics within stakeholder groups across ELHCP is varied. This

includes variances in both the understanding of the capability requirements for population health analytics, and of

the data platforms and functionality available to ELHCP.

Current understanding of population health analytics is also represented by the difference in the use of analytics

across organisations within the STP. Varying degrees of maturity with respect to population health analytics were

articulated by stakeholders. When considered against the population health analytics maturity framework (section

3.1.1 above, and Appendix B), we have identified variation across geographies in the STP, both in terms of maturity

and understanding of population health analytics capabilities within stakeholder discussions.

Inconsistent knowledge of the available data platforms and their capability was articulated by participants, including

the value to developing care and payment models. Specifically the analytics role within the ELHCP digital workstream

was not well understood by interviewees.

Further impacting the current-state understanding and capabilities is existing operating model for analytics. Current

constructs focus analytics capabilities on transacting commissioning activities and monitoring constitutional and

regulatory performance requirements (as outlined in observation 4.4 below).

Implication

The clarity and depth of understanding of population health analytics requirements, and of the data available, will

inform the approach taken towards the development of population health analytics within constituent organisations

or geographies across ELHCP. The pace of change, and the extent to which efforts are focussed on activities that

will have the greatest impact, may therefore be limited.

# Recommendation

1 Raise awareness of digital platform functionality and capabilities

3 Establish and enabling mechanism for Discovery access and assessment

5 Establish governance mechanisms to develop and agree the analytics strategy


4.2 Maturing workstream and STP governance

Observation

Tiered governance mechanisms and structures have been defined for the ELHCP Transformation Programme,

including those of the Digital workstream. Structures are defined for digital enablement groups in each of the

geographies, reporting into the STP-wide Digital Strategy Group. Additionally, requirements for considering and

approving initiatives have been defined, which outline that consideration should be given to the alignment of proposed

initiatives against the strategy, priorities and principles of ELHCP.

However, systematic use of the multiple datasets available to the STP to determine population health need was not

evident. The ability to identify the needs of the population within each geography, or the needs of patients and

service users within constituent organisations, will be essential when determining the priority areas of focus for

population health analytics activities and initiatives.

As identified in 4.1 above, the knowledge and understanding of population health analytics varies across stakeholder

groups and geographies. The governance for the programme continues to mature, and further development is

required to enhance the knowledge and understanding of population health analytics across ELHCP, to effectively

enable population needs assessments.

It was identified that organisational governance supporting the development and direction of analytics is not currently

aligned across the STP. For example, supporting organisational governance structures are aligned with the financial

constructs for existing analytics capabilities (observation 4.4 below), as opposed to the broader application of

population health analytics being developed by ELHCP.

Additionally, we were informed that consistent standards for structured data are not yet defined for use by clinical

services and constituent organisations across ELCHP, leading to the inconsistent the collection and collation of data

items. This effects the ability of ELHCP programme teams to compare data items and data sets across institutions

and has consequent impact on the validity of any comparative analysis or care pattern assessment using these data.

Pockets of good practise and innovative activities were highlighted within workshops and discussions with ELHCP

stakeholders. However, the governance currently in operation does not enable a structured approach to sharing

experiences, mature practise and learnings across constituent organisations for the benefit of the STP and population

health and social care outcomes.

Implication

The implementation of a coherent vision for population health analytics within the STP will require effective

governance, to enable stakeholder engagement and buy-in across organisational boundaries, and support in

establishing common standards within the STP. Additionally, where experiences, learning and good practise is not

effectively shared, there is a risk that improvements and benefits are not made available in a timely and structured

manner to organisations and the population across all geographies.

Furthermore, where population needs are not assessed on a consistent basis, supported by a complete and consistent

dataset, there is a risk that the priority areas for focus cannot be identified. Valuable resources may not be used in

areas that would support the greatest benefit within each organisation geography within the STP.

# Recommendation

2 Define the ELHCP analytics delivery approach


10 Develop a plan and pathway to increased digital maturity

4.3 Existing datasets may not be complete

Observation

There is an alignment of clinical applications across ELHCP, including those in use within both the primary and

secondary care settings. A summary of core clinical applications, and integration with the eLPR is outlined in Figure

10 above. This provides a strong basis for further use of data from native systems through aggregation in population

health platforms.


While there is a degree of commonality of systems across the STP, a number of challenges were identified which

impact the effectiveness of the datasets available, including:

Availability and completeness of data within datasets: Data is not collected from all organisations within the

STP, whether in the East London Shared Care Record, or in the Discovery platform. Specifically, access to

local authority data remains limited, and in the early stages of aggregation. There is a recognition that the

ability to access local authority data would enable improvements in the delivery of services

Comparability of data: CIOs and transformation practitioners identified challenges as a result of multiple

instances and software versions across the common applications, limiting the value of aggregated data. This

was further compounded by the differing levels of adoption and methods of use of the systems. For example

the same field in the same version of the software may be used to entre different data in hospitals treating

the same patient cohort, thereby making basic activity comparisons between providers challenging.

Linkage of data sets to identify population health need: In addition to availability, the ability to link local

authority data with health care data is challenging, where a common identifier is not available.

Implication

The ability to perform effective population needs assessments, and inform the priority areas of focus for

population health analytics will be limited where the data available to support such assessments is not complete.

There is a risk that areas of focus are not the most appropriate for the population of East London, reducing the

potential impact and improvements in care delivery and outcomes. Additionally, the benefits that may be realised

through collective datasets and analysis will be limited across the STP as a whole, where data from all

organisations is not included.

# Recommendation

7 Work with providers to further enhance common application landscape

8 Definition of agreed data sets and data definitions, aligned with patient cohorts


12 Datasets should align resource to activities at patient level

4.4 Existing capability is aligned with, and limited by, current analytics constructs

Observation

The structure of current analytics functions, associated capability, and supporting data is typically aligned with current

contractual reporting requirements. This includes both statutory reporting for Public Health and social care, and

finance and performance reporting requirements for provider Trusts.

Mature capability was articulated by stakeholders with respect to interoperability and data leakage, and data

management and aggregation, as outlined in figure 8 above, aligned with the current operating model. However,

the development of more advanced population health capability has been limited by the constraints of the current

operating model. We were informed by both operational and clinical stakeholders that limited analytics capacity and

capability is available to support the analysis of the available datasets, including supporting capability provided by

NEL CSU. We were informed that CSU capability was also aligned with the current operating model, and were not

sufficient for future requirements of the STP.

The capacity of the ELHCP programme to deliver population health requirements is limited, placing reliance on

organisations to support in the development of priorities and population health capabilities. However, our assessment

also identified that the capability of both resources and technology across organisations within the STP varies. This

has impacted bot the consistency with which data is collected and available and the extent to which integration and

interoperability is currently possible (as identified in observation 4.3 above).

Organisational capability has impacted the extent to which population health analytics, and the use of data for

purposes outside of the current operating model has progressed within organisations. For example, priority within

Barts Health is given to ensuring the technology infrastructure is maintained, with the adoption of digital working


often varying by clinician or service, due to the limitations of available resource and professional capacity to increase

digital maturity.

Implication

The development of capability and availability of rich clinical data is limited by the focus of analytics on financial and

statutory reporting requirements. Consequently, the ability to gain insight and triangulate data sources to predict

or measure the impact of clinical intervention may be further limited. There is a risk that the current capacity is

disproportionately focussed on delivering existing operating model requirements, and that capability is not sufficient

to support the development of population health analytics across all organisations within the STP.

# Recommendation


6 Assess, design, implement and measure outcome improvement initiatives

9 Incentivise the adoption of improved data collection and quality

11 Develop a consistent method and approach to patient level costing across constituent organisations within

the Partnership

12 Datasets should align resource to activities at patient level

4.5 Limited incentives to capture and share data

Observation

Capability and capacity for analytics is focussed primarily on ensuring contractual and performance requirements for

reporting can be achieved, and to ensure payments for services being delivered are received (as identified in 4.4).

Stakeholders across health and social care recognised the possible benefits of capturing broader data, there are

currently limited incentives for organisations to capture and report outside of the current analytics constructs and

financial incentives.

Concern was expressed in how broader data, outside of current contractual and reporting requirements, may be used

punitively against provider organisations, resulting in a reluctance to capture and share richer data more broadly. It

is recognised by organisations that there is a need to work collaboratively to enable effective population health, and

support the improvement of outcomes across the STP.

However, current incentives focus on the performance of individual organisations against defined contractual

requirements. Coupled with the fact that organisations are also competing to win and provide services often within

the same geographic footprint, further impacts ability to effectively adapt to a mind-set of collaboration and

openness. This is particularly applicable within the community and mental health settings, where service contracts

are typically competed for on a more frequent basis, both with NHS and private health providers.

Consequently, data, associated analysis and outcomes can have a commercial and competitive ‘value’ which may

impact on the willingness to share data across the STP. Organisations who commented on this situation, recognised

the need to share data to inform population health needs assessment and were keen to do so, but were cognisant of

the possible impact on their ability to compete for service contracts in the future.

Implication

Where incentives are not aligned with embedding data quality improvement and availability of data, the willingness

of health and social care organisations to share information, and to dedicate the necessary capacity and capability

to doing so is likely to be limited.

# Recommendation

5 Develop a data quality improvement strategy


4.6 Variation in the adoption and use of clinical data platforms

Observation

The Discovery platform and eLPR demonstrate mature capability to interoperate and aggregate data across the health


and social care geographies within the STP. Furthermore, there are clear plans in place to expand the data coverage

and capabilities going forward, including across mental health and local authority data. Work is ongoing to support

the further development of Discovery, including the implementation of Artificial Intelligence (AI) tools to be provided

by the University of Pennsylvania.

The STP has seen significant adoption and use of the eLPR, with approximately 75,000 views of patient data in

September 2017. The majority of views are recorded across GPs, with Acute, Mental Health and Community Trusts

also accessing the record. GPs stakeholders interviewed as part of the assessment have commented that access to

a broader patient history has enhanced their ability to inform and engage with patients, both in the planning and

delivery of their care. Existing platform provide a good basis for both analysis and reporting and clinical workflow

and patient activation.

However, the eLPR and the Discovery data platforms are not used universally across the STP, including both in the

collection and provision of data, and in accessing the platforms to inform clinical care decisions. While it is understood

that access to the Discovery Data platform has been limited to date, with further work necessary before the platform

and data is made available broadly, the eLPR is available to organisations across the STP. As identified in observation

4.4 above, it is recognised that adoption and use of clinical data platform to inform care decisions is impacted by the

current operating model and reporting requirements.

Implication

Variation in the adoption of the available clinical applications across the STP will contribute to the risk of an incomplete

dataset. While the ability to interpret and act upon data and information will rely on the maturity of clinical capability

within organisations, where datasets are not adopted and used, the ability of the Partnership to leverage the value

of the population health platforms will be limited.

# Recommendation



4 Support a broader London role in population health analytics

7 Work with providers to further enhance common application landscape



4.7 Challenge identified in the adoption of changes

Observation

It is recognised that East London is innovative, and demonstrating progress in the use of data as an enabler for

delivering population health and improved outcomes for patients and service users. However, the willingness and

ability to adopt changes was identified by participants as a significant barrier to enhancing the consistency with which

data, analytics and technology are currently used to support care delivery within the STP.

Challenges contributing the varied adoption of change have been identified in a number of areas, including:

Patient access to data and information is varied within the STP, with City & Hackney identified as the 3rd

worst region in London for accessing GP online.

There is a perception amongst stakeholder groups that those patients and service users in greatest need are

the least likely to be able to access their data and consequently least willing to engage with health services

digitally.

Varied clinical adoption of technology within Trusts (as identified in 4.4 above). Additionally, some clinicians

expressed the requirement for summary information, as the current information available was, at first use,

difficult to navigate. As such, the inclination to adopt the system was impacted;

Concern over duplication of activities with the STP has limited progress and adoption at local level (for

example, rolling out a patient portal at NELFT, and broader engagement in local authority data sharing –

both of which are perceived as an ELHCP responsibility).


While the adoption of change was identified by stakeholders as a barrier for both clinical and patient use of data,

there is currently no systematic and consistent approach to engaging with patients and service users across the STP.

Additionally, there is a need to develop a mechanism to consistently share and learn from good practise across the

STP, in order to support the case for change.

Implication

The ability to collect and use of data will be essential to identify opportunities to adapt the model of care delivery,

and improve outcomes for service users and patients. Where data, analytics and technology are not used in a

consistent manner, the ability to achieve benefits across the STP, focussed on the high priority services (identified

within each region) will be limited.

While there may be a reluctance from patients to use technology, benefits achievable will remain unknown, where

the use of data, analytics and technology is not made available to the population in a consistent manner.

# Recommendation



6 Assess, design, implement and measure outcome improvement initiatives


4.8 Understanding and engagement within the STP on the nature and content of digital platforms

remains limited

Observation

Efforts have been made by the STP to engage with stakeholder groups across organisations, however the penetration

of engagement is more limited than expected. While communications are perceived to be well-led, Stakeholders

interviewed demonstrated a varied level of understanding and engagement with the Digital Enablement programme

on the nature and content of the digital platforms.

Variance has been apparent between stakeholder groups, and also across organisations as a result of inconsistent

understanding across organisations and stakeholder groups. In particular the level of understanding and engagement

with the programme from the Finance stakeholder group, was lower than amongst the CIO group. While this may

be expected given the technical nature of the changes being progressed, it is by no means a technical project, and

broad engagement across all stakeholder groups will be essential.

We were informed that communications and engagement at leadership level are strong, however there is limited

consistency in ensuring programme information is filtered effectively through organisations within the STP. Local

Authority engagement was also identified as a limitation currently.

Implication

While geographic and historic factors may impact the level of engagement across the STP, it is essential to ensure

the vision for the digital workstream within the ELHCP programme, and progress being made is communicated

effectively to those impacted by the change, and those who will be critical in adopting and driving forward associated

changes. Where this is not the case, there is a risk that benefits will not be realised to the extent expected, or in a

consistent manner. Additionally, valuable input into further development of priority initiatives may not be obtained

where engagement by the STP is not sufficient.

# Recommendation



4.9 Information Governance

Observation

Progress has been made to enable information sharing across the STP, as demonstrated by the eLPR and Discovery


platforms. Stakeholders recognised a clear focus on the requirements of Information Governance (IG), including

obtaining patient consent for their data to be used.

However, IG requirements were also identified as a challenge when seeking to share information across the STP. We

are informed that there remains a lack of clarity regarding the role of data controller, where data is shared across

organisational boundaries. In particular, the willingness of GPs to share patient data has been impacted, where there

is a perception that patients are not willing to share their data further.

There is an inconsistent understanding in the best approach to ensuring IG requirements can be met, while also

sharing identifiable patient data across organisational boundaries.

Implication

The ability for organisations within an STP to share and use data at a local level may be limited where IG requirements

are not fully understood and managed effectively to enable the necessary sharing of information. Consequently, the

pace at which data is shared and used to support population health analytics initiatives may be impacted.

# Recommendation

2 Define the ELHCP analytics delivery approach



Appendix A – Scope and Approach

Scope

B. Assess the maturity of the health analytics capability to support the outputs from four Digital Enablement

workstreams:

i. Shared care records;

ii. Patient enablement;

iii. Advanced system-wide analytics; and

iv. Digital infrastructure.

C. Assess the overall level of analytical maturity.

D. Provide commentary on the STP’s readiness to demonstrate population health capabilities with respect to data

analytics.

E. Provide commentary on the enabling capability of current and planned technology infrastructure.

Methodology and approach

Aligned to the scope of activities:

A. Assess the maturity of the health analytics capability to support the outputs from four Digital Enablement

workstreams:

i. Understand and comment on the objectives, progress and plans of the workstreams to support health

analytics capability, through the following lenses and key lines of enquiry:

i. Operational: capability to operationalise place-based health analytics to embed data

analytics into day-to-day working, enable the delivery of new clinical workflows and support

patient self-help and direct engagement in their care;

ii. Clinical: capability to harness health analytics to enable governance and delivery of clinical

care and associated research requirements through technology-enabled place-based care

models;

iii. Financial: capability to use health analytics to understand and create mechanisms to

manage financial flows and payment mechanisms to support the achievement of place-based

care outcomes;

iv. Technical: capability of technology, analytics and associated governance frameworks to

deliver and scale to provide the technology infrastructure required to support place-based

care.

ii. Assess through desktop review of Digital Enablement documentation and interviews with ELHCP

senior leadership.

B. Assess the overall level of analytical maturity.

i. Plot the Digital Enablement current position against Deloitte’s Health IT & Data Analytics Capabilities

and Maturity Framework, in order to assess maturity.

ii. Consider the current position against key enabling capabilities (interoperability, data aggregation and

management, risk stratification, reporting, clinical workflow and patient engagement).

iii. Conduct workshops to understand the perspectives of the following groups on: clinicians, patients,

informatics teams, ELHCP leadership.


C. Provide commentary on the ELHCP’s readiness to demonstrate population health capabilities with respect to

data analytics.

i. Define and agree expectations of the population health analytics criteria for STPs based on wider

NHS and global experience of place-based health system approaches.

ii. Compare findings on health analytics capabilities to the above criteria, taking into account enabling

processes such as workforce capability, information governance, identification of relevant clinical use

cases and clinical engagement, as factors to enhance maturity and readiness.

D. Provide commentary on the enabling capability of current and planned technology infrastructure:

i. Understand and comment on the nature of technology platforms used to deliver health analytics and

supporting technology infrastructure, including provider patient record applications, as a basis to

support the required activities of population health data analytics.

ii. Understand and comment on technology infrastructure enhancement plans as set out in the Local

Digital Roadmap.

iii. Compare findings to our wider knowledge of other population health initiatives.


Appendix B – Population Health Analytics

Maturity Matrix

In order to support the assessment, health analytics function capabilities were considered against a maturity matrix, as outlined below.

We are able to draw upon Deloitte’s international experience in the assessment and implementation of Accountable Care Organisation systems in the

US to establish a population health analytics maturity matrix against which existing capabilities can be assessed in a consistent manner. The maturity

matrix is outlined below, summarising mature-state practices in each area.

Figure 14: Maturity matrix highlighting innovative practices


Appendix C – Interviewees

Stakeholders interviewed as part of the assessment are captured below:

Name Job Role Organisation

Vikrant Abbott Analytics Consultant Newham Clinical Commissioning Group (CCG_

Keith Apperly BI Manager North East London NHS Foundation Trust (NELFT)

Dr Ann Baldwin Clinical Director BHRS CCGs

Osman Bhatti GP IT Lead Tower Hamlets CCG

Henry Black Chief Finance Officer East London Health and Care Partnership (ELHCP)

Kambiz Boomla Clinical Lead Clinical Effectiveness Group

Les Borrett Director of Financial Strategy Waltham Forest CCG

Katie Brennan Deputy Director of Financial Strategy ELHCP

Niall Canavan Director of IT & Systems Homerton University Hospital

Peter Conoulty Associate Director of Client Services North East London Commissioning

Support Unit (NEL CSU)

Steven Course Chief Finance Officer East London NHS Foundation Trust (ELFT)

Jon Cox Consultant in Public Health Waltham Forest Council

Jeremy Cridland Associate Director Business Intelligence NEL CSU

David Culley Commissioning Project Lead Waltham Forest CCG

Charlie Davie Managing Director University College London Partners (UCLP)

Pam Dobson Clinical Programme Manager (Maternity) ELHCP

Lee Eborall POD lead NEL CSU

Dr Navina Evans Chief Executive ELFT

Sam Everington Chair Tower Hamlets CCG

Richard Fradgley Director of Integrated Care ELFT

Nikki Freeman Associate Director Business Intelligence NEL CSU

Umesh Gadhvi Director of Healthcare Informatics NELFT

Nichola Gardner NEL STP Programme Director ELHCP

Anna Garner Head of Performance and Alignment City & Hackney CCG

Dr Charles Gutteridge Chief Clinical Information Officer Barts Health NHS Trust

Simon Hall Acting Chief Officer Tower Hamlets CCG

Vincent Heneghan Assistant Finance Director Newham CCG

Matthew Henry BI Analyst Waltham Forest CCG

Terry Huff Chief Officer Waltham Forest CCG

Zaman Hussain Head of Information Services Barking, Havering and Redbridge University Hospitals NHS Trust

(BHRUT)

Barry Jenkins ED, Finance and Commercial Development NELFT

Bill Jenks WEL IT Programe Manager ELHCP


Phil Koczan GP IT Lead Waltham Forest CCG

Bhupinder Kohli GP IM&T Lead Newham CCG

Sunny Lachanah Project Manager BHR CCG

Rian Lamprecht Leadership Stakeholder ELFT

Ryan Makel Head of Information Homerton University Hospital

Rob Meaker Director of Innovation BHR CCG

Dr Anil Mehta Chair – NHS Redbridge CCG Redbridge CCG

Simon Miligan Director of Capital, Costing and Development Barts Health

Rob Miller Director of ICT Hackney Council

Jane Milligan Executive Lead ELHCP

Dean Mitchell Performance Manager NELFT

Emma Nichols Senior Programme Manager ELHCP

Rob Nimmo Head of IT Applications BHRUT

Fabian Odu BI Developer ELHCP

June Okochi Programme Manager (Maternity and Cancer) ELHCP

Efosa Omigie Head of Analytics ELHCP

Enrico Panzino Senior Commissioning Manager Waltham Forest CCG

Mike Part Chief Information & Technology Officer NHS England

Fiona Peskett Deputy Director of Strategy BHRUT

Julie Price Director of Performance and BI NELFT

Richard Quinton Finance Consultant Tower Hamlets CCG

Luke Readman Chief Information Officer ELHCP

John Robson Clinical Lead Clinical Effectiveness Group

Dilani Russell Deputy Chief Finance Officer City and Hackney CCG

Petra Scantlebury Assistant Director of Finance Strategy &

Planning

BHRUT

Gina Shakespeare Director of Delivery and Performance (Interim)

BHR CCGs

Jayne Taylor Assistant Director of Public Health City and Hackney CCG

Martin Wallis Digital Programme Manager ELHCP

Daniel Woodruffe Chief Information Officer ELFT


Appendix D – Interim Operating Models

To assist the Partnership in progressing to the future state, we have developed the proposed interim operating models (IOMS), and supporting

activities over time, as outlined below.


ELHCP have complementary capabilities that, when aligned to improve, can support the health outcomes of the local

population. The interim operating models proposed outline activities over time that could assist in realising this

potential. Here we summarise the approach aligning these capabilities for maximum benefit:

1. Engage clinical and operational leaders in the scope and content of the information within the Discovery

Platform;

2. Develop a supported method to assist programme leaders to define key questions they would like to ask of

this data source;

3. Align this supported method to known population health needs assessments and identified priority activities;

4. Formulate questions to establish care pattern assessment and build patient profiles aligned to risk stratified

patient cohorts;

5. Align financial flows and patient-level costing approaches to establish the current resource profile of these

patient cohorts;

6. Working with clinical leaders define a proposed clinical model based on efficacious interventions, consider

adopted digital capability to deliver this service model, as appropriate to the patient cohort;

7. Invest in, and pilot, an alternative service model, leveraging existing good practice, such as social prescribing,

and existing quality improvement methods within primary, secondary and community care;

8. Define a commissioning model that incentivises the proposed care model and promotes high quality data

collection through reward for data accuracy, completeness and coverage;

9. Establish an agreed data set and monitoring method with service providers and clinical teams, use the eLPR

and other clinical data sets as appropriate to measure the care pattern of the target population, including

developing a visualisation of care interventions on as near real-time a basis as possible; and

10. Engage clinical teams in monitoring the impact of their intervention through a defined and regular cadence

of information to reflect patient system usage.


Appendix E – Glossary of Terms

The following definitions apply throughout the report:

Term Definition

Accountable Care

Systems (ACSs)

Evolved from STPs, these build on previous efforts to integrate services in

the NHS. Eight ACSs will bring together local NHS organisations, often in partnership with social services and the voluntary sector.

Application Programme

Interface (APIs)

A tool used in apps and websites to enable different software components to

communicate.

Business Intelligence (BI)

A term that refers to a variety of tools used to analyse an organisation’s raw data. BI is made up of several related activities, such as data mining,

analytical processing, querying and reporting.

Chief Information Officer (CIO)

The most senior Executive with responsibility for information systems and technology within the organisation.

Clinical Commissioning Group (CCG)

A core part of the government's reforms to the health and social care system, replacing primary care trusts as the commissioners of most services funded by the NHS.

Commissioning Support Unit (CSU)

An organisation providing CCGs with specialist skills and knowledge to support in their role as commissioners.

Commissioning for Quality and Innovation (CQUINs)

An NHS payment framework encouraging healthcare organisations to improve care delivery, with a proportion of their income reliant on demonstrating the benefits.

East London Patient Record (eLPR)

A system to share read-only patient records across health and community organisations within five London boroughs - Waltham Forest, Tower Hamlets, Newham, Hackney and City of London.

Five Year Forward View (5YFV)

The NHS Five Year Forward View was published in October 2014 and set out the shared vision for the future of the NHS based around the new models of care.

Health Information Exchange (HIE)

The mobilisation of health care information electronically across organisations within a region, community, or care system. An effective HIE is a recognised key building block in sharing clinical information to enable

improved patient care.

Interoperability The ability to share data, guidelines, insights and analytics bi-directionally across the continuum of care.

Master Patient Index

(MPI)

An electronic medical database that holds information on every patient

registered at participating healthcare providers.

National Information Board (NIB)

The role of the National Information Board is to put data and technology safely to work for patients, service users, citizens and the professionals who serve them.

Patient-Level Information and Costing Systems (PLICS)

Costing methodology in the NHS based on actual interactions and events related to individual patients along with any associated costs

Population Health Population health aims to improve the health of a human population via health outcomes, patterns of health determinants, and policies and

interventions. One of the main aims is to reduce health inequalities among different population groups.

Population Health

Analytics Capabilities

The maturity of population health analytics capability should be considered against the six core capabilities, highlighted below.

Interoperability, Integration, HIE: Connects healthcare information

and data via Application Programming Interfaces (APIs), Health Information Exchange (HIE) or messaging protocols across the ACSs for clinicians and patients to access.


Data Aggregation and Management: Aggregates data from disparate sources to improve transparency

Analytics (including Risk Stratification): Enables Insight driven analysis that is both descriptive and prescriptive

Reporting: Delivers a self-serve solution for performance management

Clinical Workflow: Orchestrates the execution of activities from disparate systems constituting care continuum

Patient Activation: Enables the patient to manage their own care

needs and drives required clinical workflow.

We have used this taxonomy, developed through extensive use in US health systems, to inform our assessment of the existing portfolio of analytics

functions across ELHCP.

Risk Stratification A tool for identifying and predicting population groups which are high risk and prioritising the management of their care.

Secondary Uses Service (SUS)

A repository for healthcare data in England which enables a range of reporting and analyses to support the NHS in the delivery of healthcare services.

Service Line Reporting (SLR)

Provides data on financial performance, quality, staffing etc. based along service line management structures.

Sustainability and Transformation Plans (STPs)

NHS and local councils working in partnership in 44 areas across England to develop proposals for improvement to health and care across the country.

Vanguard Programme As part of the new care models programme, 50 organisations were selected as vanguards to take the first steps towards delivering the Five Year Forward View and supporting improvement and integration of services. There are five

vanguard types: integrated primary and acute care systems; multi-specialty community providers; enhanced health in care homes; urgent and emergency care; and acute care collaborations.


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February 2018

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Documents

East London Health and Care Partnerships Population Health ... Digital Roadmaps within each geography of the STP outline the digital ambitions across ELHCP. These individual health