Data Science

What’s Driving Data-Based Decision-Making?

What's Driving Data-Based Decision-Making

We are in a new world awash with data, which is starting to change the way we think and act. By looking back and detecting patterns one can theoretically predict the future is driving a whole series of initiatives within health and care. This is being played out in a series of dimensions:

  • The changing nature of healthcare providers
  • The changes to the metrics that drive health and care systems
  • The nature and structure of screening processes
  • The whole process of disease diagnosis, as well as the potential prediction of adverse events
  • The disruption and transformation of healthcare delivery
  • A growing appreciation of the importance of real-world data and its implications on the existing methodologies we adopt to determine evidence-based care

Followed by increasing changes in the provision of healthcare, the more established healthcare providers are being challenged by the new data integrators. The analogy of the decline of the shopping mall or the virtual transformation of the banking system is often quoted and there is some truth in this. How else could companies that specialise in personalised delivery of products to people suddenly be serious players in the health and care space?

Their strength is twofold, as well as their undoubted financial muscle and spread:

  • They are close enough to individual people to have enough data points to be able to predict what will prove to be enticing and attractive.
  • They have a strong understanding of the digital space to enable planning for a world where digital interventions are often powered by data rather than individuals.

This will prove to be attractive enough and effective enough to provide better and more predictable care.

Confronting Ageing Populations and Rising Costs with Personalised Medicine

We are in a health and care system where costs and activity are rising faster than our ability to pay for them, not helped by our ageing populations. Data-driven systems have the capacity to manage the prevention of disease as well as treat it. We are already seeing examples of programmes that are delivering wholescale-personalised initiatives, like diabetes prevention.

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Screening will also need to change. What was ideated as a system where the parameters for inclusion were as simple and unidimensional as age (for example, every adult over a certain age will be encouraged to be screened for colorectal cancer) will change to a far more segmented approach where people will be selected as high risk associated with their genomic characteristics. Additionally, we are already being exposed to systems which are digital diagnosticians. Some of these are also starting to be voice-activated, obviating the need for data entry. In addition to this, there are more structured chatbots utilising algorithms to assist in diagnosis.

Healthcare delivery pathways will inevitably change as a result. Our ways of managing non-communicable disease are also changing with the simpler modifications to treatment being managed digitally and the more complex needing clinical attention. The added advantage to digital approaches is their predictability and their adherence to protocol, which is often difficult to reproduce in a clinical system where there are a multitude of players.

The Result of Evidence-Based Care Adoption

Modern medicine has evolved and become more effective as a result of adopting evidence-based care. The move to a digital world could be viewed as turbocharging this process. This however also has its challenges. For instance, the arrival of multiple data sources and the adoption of machine learning—the very structured and well-developed epidemiological systems that underpinned the very basis of medicine—are under threat by the arrival of real-world data. We all know how difficult it is to utilise these methods of research when trying to identify precursors of disease or disease modifying agents for conditions which are multifactorial and multidimensional—especially if they have prolonged pre-condition phases like dementia.

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In addition, the numbers that need to be engaged in trials make this whole exercise prohibitive in both cost and complexity. Digital real-world data could change this, but we need to be aware that association is not necessarily the same as causality. Epidemiological thinking needs to adapt and develop as a result.

Data-driven decision-making will become the norm quicker than we predict. We are now used to being confronted with different premiums when we try to insure a car and the fact that our age, where we live, our educational attainment and our style of driving (or lifestyle in a health world) can have a significant impact upon our chances of making a claim. The same is true in healthcare and we take this for granted in our health premiums. The challenge we have is to harness these systems and to give them a human face and touch, and to make people want to use them. The other challenge we face is to become even more engaged in this endeavour. The key of course, is finding that balance.

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