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Leveraging digital innovations in healthcare for organisational success

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Leveraging digital innovations in healthcare for organisational success

Posted | Updated by Insights team:

Publication | Update:

Mar 2024
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Suzanne Wait, Managing Director at The Health Policy Partnership, explains why organisational digital innovations may be key to streamlining and sustaining our health systems

In the fast-paced world of digital health, there is much excitement around Artificial Intelligence, telemedicine and mobile apps and their transformative potential to improve people’s health. One area of innovation that receives less attention is organisational digital innovations: incremental changes to administrative processes and operational aspects of healthcare that improve the efficiency of care. Why are these important? It has been estimated that up to 20% of healthcare is inefficient and does not deliver any tangible benefits to patients. But digital solutions are working to improve this.

In the UK, initiatives such as Choosing Wisely and Getting It Right the First Time aim to direct physicians towards interventions that achieve the best patient outcomes with the resources available. However, greater organisational efficiency is about more than just choosing the right intervention.

It’s about ensuring people are offered coordinated and continuous care, being able to share their information securely and effectively between the different health professionals caring for them, and relieving health professionals from time-consuming administrative or bureaucratic processes so that they can focus their time on caring for their patients.

With health systems worldwide facing workforce shortages coupled with increasing demand for care, addressing these needs is desirable and essential to protecting the sustainability of our health systems. This same message was conveyed when leading innovators in digital health convened at ‘Les Grandes Tendances de la e-Santé’, a conference in Paris looking at the digital innovations shaping the future of healthcare – and a significant proportion of them fell under the ‘organisational’ umbrella.

Enhancing the capacity of health professionals to deliver effective care to more people

One of the scarcest resources for health professionals is time, so anything that can speed up time-consuming processes is of tremendous value. In the administration of radiotherapy, careful planning is needed to ensure treatment is tailored to each patient, both in terms of the dosing and the location of irradiation. The UK
currently lacks a third of the radiologists needed, while up to a quarter of people in Europe who need radiotherapy do not have access. But new AI-based techniques are increasingly being used to guide individualised planning, cutting the time required for planning from a whole day to less than 30 minutes, with the same – or even better – levels of precision. These innovative techniques could significantly expand cancer centres’ capacity to deliver radiotherapy to more cancer patients sooner.

Improving coordination through centralised and shared data

Hospitals and health systems’ complex and siloed nature creates significant inefficiencies for patients, who often have their information lost or tests duplicated as they navigate health providers. This structural inability for ‘data to follow the patient’ also deeply affects health professionals. Suppose they are not confident they have all the data necessary to provide the best care for their patients. In that case, health professionals may often find themselves chasing data between different departments or health settings before making clinical decisions.

Cloud-based systems allow information to be shared with the whole team involved in a person’s care. This has been transformative, offering a secure, centralised and virtual repository of all relevant data surrounding a person via electronic medical records. Data analytics also allow this data to be compiled into a clinical dashboard in minutes, providing clinicians with a real-time overview of their patients’ medical history, test results, treatments, and diagnoses. The impact on patients is also tangible in terms of faster diagnoses and, access to care, and improved communication with their care teams.

Enhanced learning and minimising the risk of error

Medical skills are learnt through observation and practice, and repetition of tasks is key to developing expertise in given interventions. The need for repetition is possibly most recognised in the field of surgery, where the risks involved in this training are popularly depicted across film and television.

The advent of digital twins, however, now enables doctors to practice interventions on digital replicas of human patients, providing a safe space to acquire expertise and perfect their skills. For example, digital twins of human hearts are being used at Boston Kids Hospital in the US to allow surgeons to practise-test interventions in different conditions to find the optimal conditions for each person. Researchers are also using digital twins to test cancer treatments on digitally derived cellular models, which can reproduce patient and tumour characteristics with high levels of precision. This technology helps with negating the need for animal testing and trial-and-error scenarios with people.

Where innovation meets system readiness

Organisational digital innovations will only achieve their full potential if they can be fully integrated into the evolving architecture of health systems. Flexibility in financial planning and procurement practices is also needed to integrate digital approaches and supporting technology into organisational budgets. In France, the social security system has created a special reimbursement tariff for organisational digital innovations, creating opportunities for healthcare settings to include them in their existing ways of working.

Health professionals are a precious resource, and we need to think about how technology can best serve their needs to make their jobs easier and enable them to care for their patients with more information, precision and confidence. Digital innovations that optimise organisational efficiency have a vital and growing role to play in doing just that.

Disclaimer
This article follows the publication of Our Health in the Cloud, a report published in 2023 by The Health Policy Partnership with support and funding from Amazon Web Services.

 

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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.

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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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