Idea Description
Supplementary Information
Innovation 'Elevator Pitch':
UK developed patient focused Population Health solution pulls cross sector data (i.e. GP, Acute, Mental health, etc) and performs analytics and intelligent cohort finding that can identify patients with reported unexplained symptoms.
Overview of Innovation:
ArtemusICS supports a health system to analyse population data and identify specific gaps, health inequalities and unwarranted variations in its population’s health. ArtemusiCS secure web accessed hosted system provides a wide range of tools, dashboards, selection and filtration options to identify and create patient cohorts. ArtemusICS can then further identify costs, utilisation and gaps in care.

Collecting data from Acute, GP, Community, Mental, Social and Ambulance, ArtemusICS provides commissioners and providers with a tool to identify cohorts of patients, and understand the integrated care needs of a population right down to an individual. It contains analytical tools to allow for LTC risk, EOL risk, Frailty, Social isolation, etc to be collated and presented in one picture. It also allows for evaluation of interventions (i.e outcomes) and identification of RoI.

It has a unique attributes based architecture, where all data items are linked to one or more “attributes”, based on their coding (i.e. Snomed, Read, ICD10, etc). Inherently future proof and expandable, it allows us to utilise Machine Learning and AI techniques to drill through large data sets to find correlations and trends in specified data types, such as the symptoms presented in the MUS cohorts.

ArtemusICS operates on three distinct levels:
  • Overview Dashboards: provide whole-population analytics with facilities to filter down to key patient cohorts. A range of Dashboards provides commissioners and clinicians pertinent population count, event counts, service and usage counts and costs; with the facility to then drill through to cohort and patient specific views.
  • Cohort Manager enables the user to review sub-lists of patients, caseload management groups or individual patients against a range of detailed views including conditions, events, measures, timelines and management lists. Venn diagrams offering cohort comparisons to enable care planners and clinicians to pin-point a specific cohort with a specific set of attributes for intervention and care management.
  • Patient view provides a range of views specific to patient conditions, medications, diagnosis, events and measures, supporting the clinical user to improve diagnosis and plan care.
In addition, our remote monitoring technology can also collect surveys such as PHQ,GAD7, HADs etc
Stage of Development:
Market ready and adopted - Fully proven, commercially deployable, market ready and already adopted in some areas (in a different region or sector)
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Innovation 'Elevator Pitch':
PredictX Machine Learning system has been successfully deployed to solve a similarly difficult problem of identifiying School Ready Children. This model is ideal for solving the problem of early identification of MUS.
Overview of Innovation:
The Complex Symptoms Service (CSS) will focus on the problem of MUS following on from the success of the BMUSS Phase 1 pilot. A system that automatically segments and identifies patients with high probability of MUS at an early stage will significantly increase the savings realised of the CSS as well as improve the well-being of patients. The complexity of MUS makes it difficult to identify manually especially at early stage.

Machine Learning (ML) has advanced to a stage that the challenging task of identifying Medically Unexplained Symptoms (MUS) automatically is now possible.

Our ML model can infer & predict results even with large variety of causal factors and disparate source data and where a single data point in isolation is a poor indicator. In the case of some long term conditions such as diabetes, a single source such as blood sugar levels can be a confirmatory indicator. MUS suffers from the fact that one episode whether via GP assessment or hospital admission does not indicate MUS on its own. Instead, it takes multiple events and episodes examined together in a holistic model that indicates the probability of MUS.

If a system could identify MUS with minimal historical data, early intervention avoids much more serious and costly problems later on. ML models such as the one PredictX developed for Essex County Council (see attachments) is ideal for working on a large range of data sets and inferring relationships not obvious to human observers.

An accurate MUS identification system must be able to meet several requirements.
  1. Robustness of the model. The model must adapt to differences in local data availability and preserve the accuracy by using proxy data if core data is missing.
  2. Leverage a wide range of data sources including social care and health
  3. Capability to read, understand, and classify free text information in any format including PDF as the source data management is often a challenge.
The PredictX Risk model deals with all of the issues. ML models are utilised not just for the forecasting models but also to facilitate understanding of free text utilising advances in Natural Language Processing (NLP).

The model has further advantage of having real-world analogues & explanability.  There could be a direct relationship from the features to the phenotypes greatly aiding the health professionals responsible for the interventions.

The model has effectively been deployed in a similar context at Essex CC.
Stage of Development:
Market ready and adopted - Fully proven, commercially deployable, market ready and already adopted in some areas (in a different region or sector)
WMAHSN priorities and themes addressed: 
Mental Health: recovery, crisis and prevention / Wellness and prevention of illness / Wealth creation / Digital health / Innovation and adoption / Person centred care
Benefit to NHS:
The direction of Travel for the NHS is the attention to early identification and treatment of disease and chronic conditions. As MUS is so difficult to identify at an early stage, patients can ricochet around the system for years before the correct diagnosis is made. MUS is a pervasive condition which has significant implications for cost and resource consumption in the system. MUS accounts for 11 % of the NHS budget for adults and comprises up to 50% of new hospital outpatient visits.

This figure is 11% of total NHS spend. The related cost to the economy in terms of sickness absences and restrictions to quality of life for people with MUS accounts for over £14 billion per annum to the UK economy.

The Complex Symptoms Service is  estimated to save in excess of £750,000 per year, based on data from local pilot services and NHS research studies. This estimate is based on current patient identification methods which logically will identify only a subset of patients at an early stage. A system that automatically identifies MUS patients at an early stage will increase the pool of patients where the interventions have greater impactability.

The value to patients who can benefit from early intervention will be measured in better quality of life, less days in hospital and improved outcomes.
Initial Review Rating
4.80 (2 ratings)
Benefit to WM population:
The West Midlands population is uniquely positioned to benefit from the System. Instead of introducing a new system before the delivery mechanism has been tested and rolled-out. The West Midlands have already mobilised dedicated services for intervening in MUS cases via the  Birmingham Medically Unexplained Symptoms Service (BMUSS), phase 1 and 2.

The service successfully implemented evidence–based practice for MUS, targeting mild, moderate and severe MUS based on a stepped care model. As the infrastructure for impacting patients with MUS have been set-up, tested and proven to be effective, the stage is set for enlarging the funnel and treating more patients with MUS at an early stage.
The system will be effective at identifying patients with MUS at an early stage. Identification alone will be ineffective without the capacity for impactful intervention. The West Midlands deployment will combine both the capacity for early identification with the capacity for intervention.

The close and strategic working relationship established over many years between PredictX and the Midlands and Lancashire CSU will help to ensure that projects are executed per agreed scope and delivered on time and on budget.
Current and planned activity: 
PredictX works with the NHS at several levels currently. We work directly with Provider Trusts such as UCLH supplying them with advanced analytic solutions. We also work with CCGs and STPs focusing on advanced analytics that need to integrate social care and health information. We also have a strategic partnership with the West Midlands CSU working to serve their CCG members as well as investing in research and development efforts to apply Artificial Intelligence and Machine Learning techniques to address challenges the sector faces. This partnership has recently been codified with the launch of the Midlands and Lancashire Innovation Partnership - a partnership of the CSU, private sector and academia working together to deliver advanced, AI-enabled solutions.
What is the intellectual property status of your innovation?:
The IPR of our innovation is held 100% by PredictX. We are happy to co-invent new IPR and contribute to the sector.
Return on Investment (£ Value): 
Return on Investment (Timescale): 
6-12 mon
Ease of scalability: 
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Innovation 'Elevator Pitch':
Our expert people and advanced health analytics reveal insights from complex data that enable health & care professionals working across the West Midlands to make better decisions for the patients and populations they serve.
Overview of Innovation:
Sollis and the world-renowned Johns Hopkins University have collaborated to produce a population health analytics platform that helps health & care professionals transform services for patients and populations.
Sollis Clarity and The Johns Hopkins  ACG ®System is a person-focused case-mix system that captures the multi-dimensional nature of an individual’s health. It considers the total disease experience of each patient, including the implications of co-occurring disease, encouraging a holistic view of the patient rather than the management of specific diseases or episodes.
Sollis Clarity supports:
  • Case Finding - Patient level risk stratification
  • Resource Management - Case-mix risk adjustment and benchmarking
  • Population Health Needs Assessment - Population level risk stratification
  • Fair Shares Budgeting - Capitated budget setting
Sollis Clarity delivers robust business analytics and data management to identify and analyse populations across the continuum of care to help health & care providers and commissioners get a precise understanding of patterns of mult-morbidity across populations and its relationship to utilisation, costs and outcomes.
Sollis Clarity delivers insights into the morbidity patterns of different populations, supporting population health management, service transformation, integrated care and, ultimately, better outcomes for patients.
Sollis Clarity goes beyond patient level risk stratification. Risk stratification at a population level helps the health economy — providers and commissioners — analyse and minimise the progression of diseases and the exacerbation of co-morbidities. When combined with the ACG System it is a comprehensive family of measurement tools designed to help explain and predict how healthcare resources are delivered and consumed.
Sollis Clarity provides the evidence base to support:
  • Planning and service re-design
  • Clinical decision making
  • Outcomes-based commissioning
  • Risk stratification and predictive modelling
  • Population profiling / segmentation
  • Case-mix adjusted benchmarking
  • Integrated multi-disciplinary care
To view Sollis - Nigel's Story - click here.
To view UK Healthcare data analytics for NHS CCGs - click here.
To view The Proactive Care at Brighton & Hove - click here.
To view Population Profiling at NHS Slough CCG - click here.
To view Using Data to Gain Greater Insight - click here.
Stage of Development:
Market ready and adopted - Fully proven, commercially deployable, market ready and already adopted in some areas (in a different region or sector)
WMAHSN priorities and themes addressed: 
Long term conditions: a whole system, person-centred approach / Wealth creation / Digital health
Benefit to NHS:
Population health analytics delivers an evidence base (insights from data) that supports large scale health & care service transformation. It is an essential component of any Population Health Management strategy and as such underpins the journey to a fully fledged Accountable Care System (ACS).

The Sollis Clarity health analytics platform is a modular system with mix-and-match components to help you achieve your healthcare analysis and commissioning/budgeting requirements.
Population health management helps Health & Care stakeholders identify and quantify the drivers and outcomes for addressing the needs of local populations.
Led by directors with decades of NHS experience, Sollis analytics software and professional services have been used to analyse data on nearly half the population of England. We believe patient-centred care strategies based on the needs of local populations provide the key to better population health management.
Sollis Clarity is helping several CCGs in the UK to implement new reporting and service planning initiatives.  To view examples of ‘Service Transformation: Stories from the CCG Frontline’ summarising how CCGs are using the insights provided by Clarity Patients and ACG® System analysis for service planning and transformation – click here.
The Sollis Clarity health analytics platform has two distinct but complementary solutions — Clarity Patients and Clarity Finance — address population health management and financial analysis respectively.
Clarity Patients, combined with the Johns Hopkins ACG® System, provides a comprehensive family of measurement tools that helps explain how healthcare resources are delivered and consumed. It supports new commissioning models such as Commissioning for Value, Outcomes Based Commissioning and Year‑of‑Care Commissioning. It delivers analytics that provide insights to support health & care interventions and innovation and it will help you track the success — or otherwise — of those innovations over time.
Clarity Finance provides contract management and activity costing with multiple tariffs, giving you business critical evidence on which to base commissioning decisions. It enables you to store and compare different versions of cost and volume type tariffs as well as non-activity tariffs, such as year of care or outcome based currencies, to give you insights on the most effective commissioning decisions.
Initial Review Rating
5.00 (1 ratings)
Benefit to WM population:
Knowledge of the risk profile of the region’s population (based around a population segmentation approach) will help Health & Care stakeholders across the West Midlands commission and deliver appropriate preventative services that will drive positive health outcomes for the 'at need' populations of the West Midlands. 

Through the innovative use of information technology to identify patients most in need of an intervention (impactable patient cohorts) Sollis Clarity helps to identify patients who could most benefit from earlier, better informed health care interventions.
The Sollis population health analytics platform allows the patient population to be risk assessed to provide timely, evidential data to all members of a Multi-Disciplinary Team (MDT), to include clinicians but not limnited to them. This enables MDT members to provide focused levels of care to specific groups of patients, reducing the risk of a patient’s condition worsening due to it being identified early so assisting MDT members in identifying and improving the care of at-risk patients.
Much can be achieved through the acquisition of primary care, secondary care, community care, mental health, prescribing and social care data. It can provide a rich understanding of how healthcare resources are delivered and consumed and by whom. Such analysis can aid an understanding of whether scarce resources are being deployed to those population groups in greatest need.
It is important that any population health analysis should focus not on single disease conditions, but on the burden of multi-morbidity observable in a local population.  Population health programmes that have the best chance of success will be those that demonstrate an understanding of the importance of multi-morbidity and its impact on the local health and care economy.
Using data to identify early healthcare interventions can provide significant benefits to patients, particularly those with long-term conditions. Providing the functionality to make real time decisions based on clinical evidence will improve outcomes for patients.
Sollis exist to help our customers deliver better patient outcomes, better patient experiences at an affordable cost and are wholly focused on the delivery of insights that will help deliver a sustainable and transformed health and care system in the West Midlands.
Current and planned activity: 
We are currently providing analytics support to thirty (30) plus Clinical Commissioning Groups (CCGs) nationally as well as nine hundred (900) plus GP practices and a number of NHS Vanguards, principally Multispecialty Community Providers (MCPs).

We would like to engage with health and care professionals involved in the development and evaluation of New Care Models throughout the West Midlands who want to use evidence based data to understand patterns of multi-morbidity and its relationship to utilisation, costs and outcomes. We are particularly interested in engaging with Sustainability & Transformation Paernerships (STPs) and emerging Accountable Care Systems (ACS).
What is the intellectual property status of your innovation?:
Sollis owns all Intellectual Property (IP) for the following software applications:
  • Sollis Clarity (Population Analytics Platform)
  • Sollis Clarity Patients
  • Sollis Clarity Finance
 Johns Hopkins Health Care (JHHC) owns all Intellectual Property (IP) for the following software:
  • ACG® System
  • ISO9001
  • IG Toolkit Certified
Return on Investment (£ Value): 
Return on Investment (Timescale): 
3 years +
Ease of scalability: 
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