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Advancing the future of UK stroke care with deep AI

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Advancing the future of UK stroke care with deep AI

Posted | Updated by Insights team:

Publication | Update:

Mar 2024
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Digital Mind. Brain Artificial Intelligence Concept
image: @BlackJack3D | iStock

RapidAI provides AI-based solutions to optimize care delivery and support clinical decision-making. Here, they discuss the vital opportunities to advance stroke care by implementing AI technology

Cerebrovascular conditions are the fourth leading cause of death in the UK – 75% of which are strokes. It is also the single largest cause of complex disability among the 1.3 million
stroke survivors in the UK.

The number of strokes that occur each year in the UK is projected to increase by as much as 60% by 2035 due to changing demographics, an aging population, and an increased rate of chronic health conditions. Combined with the fact that the number of stroke survivors living with a disability is projected to increase by a third over the same period, regulators and clinical leaders have recognized that major proactive measures need to be taken to mitigate and curb these alarming trends.

NHS Long Term Plan

In 2019, while the UK was confronted with serious challenges spanning workforce shortages, funding pressures, and increased demand for services, the National Health Service (NHS) announced the launch of its Long Term Plan. Developed in conjunction with frontline health and care staff, patients, and other experts, the updated plan from the NHS detailed its goals for investing in the healthcare areas that matter most, from providing high-quality, lifesaving treatment and care for patients and their families to reducing pressure on the NHS staff and investing in exciting new technologies.

In the plan, which named stroke a national priority, regulators specifically cited better prevention, treatment, and care for stroke patients as critical areas to focus on. Additionally, it called for national support in scaling technology to ensure more patients have access to life-changing treatments, including the use of AI interpretation of CT and MRI scans to support clinical decisions for thrombolysis and thrombectomy. Driven by a concerted effort to advance stroke care to align with the broader vision outlined in the Long Term Plan, by June 2023, 86% of integrated stroke delivery networks had implemented AI stroke-diagnosis technology, with a goal of eventually rolling out the technology to 100% of stroke networks.

Identifying ways to enhance the quality of stroke care technology remains a significant priority in improving stroke patient outcomes throughout the UK. Recently, leaders from across the world – including clinicians, regulators, and other critical stakeholders – met for the Advancing Stroke Treatment with Resources, Integration, and Delivery (ASTRID) meeting to share their experiences and discuss best practices, aiming to advance stroke care in the UK further.

Among the topics discussed at ASTRID, one emerged as the frontrunner: the transformative impact of AI in advancing stroke care. Innovative deep AI solutions—that reshape clinical workflows improve communication among care teams, and ensure compliance with industry-specific regulations—emphasize advanced technology’s critical role in shaping stroke care’s future.

The future of deep clinical AI

Intuitive clinical workflow technology facilitates informed decision-making, such as pinpointing locations of anomalies and supplying specific quantitative data to physicians, enabling them to determine the optimal course of action for each patient. These solutions also help physicians manage a higher volume of cases, streamlining and standardizing processes and abiding by regulatory compliance – ultimately resulting in more consistent, quality care.

When it comes to stroke, time is of the essence. Deep clinical AI medical imaging analysis technology can help improve accuracy in diagnosis and support physicians by serving as a second set of eyes – relieving them of some of the enormous pressure they face when having to prioritize their efforts to make quick treatment decisions. For cases when there is suspicion of stroke signals, such as left ventricular occlusions (LVOs), AI solutions can automatically identify regions of the brain and deliver real-time notifications to physicians, providing precise insights into medical images, including specific quantitative data to determine the best course of action for each patient and reduce overall time to treatment.

The adaptive nature of deep clinical AI solutions allows hospitals to tailor their IT environments to suit specific needs, accommodating fluctuations in demand and varying clinician workflows. AI-powered systems facilitate better communication among care teams by organizing and centralizing patient information. Advanced technology can be leveraged to further streamline and operationalize multi-step processes, connecting physicians through an easy-to-use platform for clinical data updates, image previewing, and communication.

These solutions comply with industry- specific regulations, such as the Data Protection Act in the UK, helping healthcare organizations meet their regulatory obligations without bearing the full burden of compliance themselves. This ensures that patient data remains secure and compliant with relevant privacy laws. Deep clinical AI solutions can also contribute to quality control measures by standardizing and improving the accuracy of diagnoses and treatment plans, adhering to high-quality standards aligned with regulatory expectations.

Across the UK, physicians are leveraging deep clinical AI solutions to advance stroke care and improve patient outcomes. A great example of this is RapidAI’s stroke platform, which automates image processing and analysis and provides easy-to-read, near-real-time views of the brain. It also gives clinicians standardized results for assessing whether a patient is eligible for endovascular treatment, minimizing the variability associated with interpretation by individual clinicians. Deep clinical AI solutions deliver the clinical, operational, and financial value that hospitals seek – from reducing door-in-door-out times to helping physicians make more informed treatment decisions faster; these tools increase efficiencies throughout the patient journey and offer more informed decision-making that can change patient outcomes.

Future-proofed medical care

Technology developments continue to move at light speed. For health systems to keep up, cutting-edge technology, such as deep clinical AI solutions, should be at the forefront of conversations to ensure medical care is prepared for future advancements. The solutions that deliver the most value pair clinical validation, operational efficiencies, and financial return while being adaptable for future advancements in new disease states. Stroke care enhancements are already making traction across the UK, but the possibilities to impact other disease states are limitless.

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This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

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We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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