East London Health and Care Partnerships Population Health
East London Health and Care Partnerships Population Health
Error! No text of specified style in document. | Error! Use the Home tab to apply Section title to the text that you want to appear here. [Type here] 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
3 Deloitte Confidential: Public Sector – For Approved External Use 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
4 Deloitte Confidential: Public Sector – For Approved External Use 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.
5 Deloitte Confidential: Public Sector – For Approved External Use 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 futurestate 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
6 Deloitte Confidential: Public Sector – For Approved External Use 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
7 Deloitte Confidential: Public Sector – For Approved External Use 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 systemwide 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
8 Deloitte Confidential: Public Sector – For Approved External Use 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-health care-system
9 Deloitte Confidential: Public Sector – For Approved External Use 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
10 Deloitte Confidential: Public Sector – For Approved External Use 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.
11 Deloitte Confidential: Public Sector – For Approved External Use 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.
12 Deloitte Confidential: Public Sector – For Approved External Use 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.
13 Deloitte Confidential: Public Sector – For Approved External Use 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)
14 Deloitte Confidential: Public Sector – For Approved External Use 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.
15 Deloitte Confidential: Public Sector – For Approved External Use 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.
16 Deloitte Confidential: Public Sector – For Approved External Use 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.
17 Deloitte Confidential: Public Sector – For Approved External Use 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
18 Deloitte Confidential: Public Sector – For Approved External Use 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:
19 Deloitte Confidential: Public Sector – For Approved External Use 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
20 Deloitte Confidential: Public Sector – For Approved External Use 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.
21 Deloitte Confidential: Public Sector – For Approved External Use 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.