The term “20/20 vision” informally denotes perfect eyesight. So, moving into the year 2020 it is an apt metaphor for our goal, which is to help improve global population health through better decision-making.

Our forecasting methodology – based on a microsimulation model – integrates health, economic and population data from multiple sources as a baseline to predict the long-term disease outcomes and costs of different interventions, be they policies, treatments or other strategies. In particular, our focus is non-communicable diseases (NCDs) – which include for example heart disease, stroke, some cancers and chronic kidney disease – reflecting our origins and 12-year track record within the UK Health Forum.

In the months since the launch of HealthLumen in May 2019, we have surveyed the broad population health and healthcare landscape to determine key trends and where best to apply our capabilities to improve decision-making.

An ageing population and a changing healthcare landscape

From a global perspective, the population health and healthcare sectors are changing dramatically. Costs continue to rise, and new technologies are emerging rapidly, complicating the landscape for both providers and users.

In particular, this changing landscape is because we are living longer but not always healthier. Many nations are facing challenges today due to this demographic shift in age among their populations. The United Nations recently reported that the world’s population of 60 years old will double and those who are 80 years old will triple during the next 30 years. At the same time, younger age groups will decrease in size. Therefore, quantifying the short- and long-term impact of these demographic shifts on health systems and wider society via modelling is important if services are to be appropriately resourced.

NCDs on the rise

NCDs are the biggest challenge to the world’s health and social care systems, as they are by far the largest causes of avoidable mortality, morbidity, disability, and both a significant driver of, and outcome of, inequalities.

NCDs are now collectively responsible for almost 70% of all deaths worldwide. Almost three quarters of all NCD deaths, and 82% of the 16 million people who died prematurely, or before reaching 70 years of age, occur in low- and middle-income countries.

NCDs are linked by their often modifiable risks factors of:

  • Physical inactivity
  • Poor diets which often include too much salt, sugar and fat
  • Tobacco use
  • Excess alcohol consumption
  • And environmental factors resulting from high emissions

Assessing the impact of interventions on these risk factors is core to our modelling capability.

Multiple diseases

The rise in NCDs includes the increasing prevalence of multi- or co-morbidities. For example, the US Centers for Disease Control and Prevention estimated that about 50% of all adults in the US have one or two chronic diseases and that 25% have two or more. In Europe, about 50 million adults have multiple chronic diseases. This amounts to a tremendous challenge for health and social care systems since people with multiple conditions are high utilisers of healthcare resources and are some of the most difficult-to-treat.

Consequently, making the right decisions becomes ever more pressing and challenging. Our focus is on:

  • Understanding this burden
  • Assessing the most cost effective suite of interventions to suit individuals with multiple conditions
  • Modelling the impact of reduction of risk factors via different types of interventions to reduce NCDs

Better data and AI

Given this population health landscape, the collection and analysis of high quality data are critical to improvements in planning the effectiveness and efficiency of healthcare delivery and interventions. Whether those interventions are policy-based, new drugs or digital therapeutics.

For example, a huge amount of the waste in healthcare expenditure results from not knowing what works for particular patients in particular clinical contexts. AI is making great strides in these areas, broadly falling into two categories:

  • Machine Learning (ML) techniques that analyse structured data such as imaging, genetic and electronic patient data. In medical applications, ML can cluster patients’ traits, or infer the probability of the disease outcomes.
  • Natural Language Processing (NLP) methods that extract information from unstructured data such as clinical notes or medical journals to supplement and enrich structured medical data. The NLP procedures turn texts to machine-readable structured data, which can then be analysed by ML techniques.

HealthLumen can validate these new methods with aggregate data.

Real world evidence

The proliferation of data and demands for transparency we see today will accelerate as we head through 2020 and into the next decade. The number and types of users of medical data and information will continue to expand rapidly.

The US Food and Drug Administration define real-world evidence as: “Healthcare information derived from multiple sources outside of typical clinical research settings, including electronic medical records (EMRs), claims and billing data, product and disease registries, and data gathered by personal devices and health applications.”

These data sets can be used within a predictive model like ours to effectively complement the knowledge gained from “traditional” clinical trials, whose limitations make it difficult to generalise findings to larger, more inclusive populations of patients, providers, and healthcare delivery systems or settings reflective of actual use in practice.

Digital health

Digital health comprises of technologies and services that enable healthcare outside of traditional clinical settings. It is part of a trend to decentralise healthcare and alleviate overburdened hospitals and clinics. Coupled with escalating healthcare costs and ageing populations suffering with co-morbidities, digital health offers a potential solution for relieve the impact of these problems for all players, including patients, providers and payers.

In fact, it is worth noting that the opportunity is so significant that companies not previously in the healthcare space are entering the field. Most of the leading tech companies such as Amazon, Apple and Alphabet (the owner of Google) have made significant investments into various aspects of digital health.

The idea of digital health replacing some drugs presents an interesting challenge for pharmaceutical companies in particular. However, prevention and treatment must work in tandem and more than ever governments and insurers have to consider prevention as part of healthcare systems. We will be aiming to quantify which new digital technologies are most cost-effective and how they can best sit within health systems, which include prevention measures and pharmaceutical therapies.

Navigating the possibilities

So, in 2020 we will be supporting our client’s decision-making in the area of NCDs by providing evidence for the values of different interventions.

Within that framework we will:

  • Assess new sources of data
  • Implement our AI/ML development plans
  • Develop our proposition for digital therapeutics

Watch this space as the vision unfolds…

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