Contemporary medicine is experiencing a tremendous paradigm shift to a new framework for healthcare which is predictive, preventative, personalised and participatory, also known as the 4 Ps of medicine. Technological and scientific advancements, increasing information and knowledge, and unmet expectations in the quality of care, are all contributing to this shift.   

A decade from now, if not earlier, the way we understand, teach and deliver medicine will change for the better. Health information technology has a fundamental role to play in this paradigm shift.[1]

This change is not happening all of a sudden, it is the combination of many disciplines reaching maturity at the same time. Medical research, statistical analysis, data modelling, computational power, machine learning and more, are now at a point of maturity that when combined become very powerful in solving challenges in healthcare. For example, managing chronic conditions, ageing population, financial pressure on health systems, an incumbent shortage of doctors are only a few of the issues organisations are facing today. These developments in technology will enable healthcare organisations to provide tailored care to an individual, but also manage health at a population level.

Technological and scientific advancements

Systems biology, upheld by the success of the molecular and panomics (genomic, proteomic, metabolomic, etc.) is progressively transforming the way we look at diseases. Thanks to molecular biology, computational analysis and mathematical modelling, we have identified what properties, at cellular, tissue or organism levels are responsible for certain health conditions. Even though this is still an expensive work in progress, there is huge potential for this approach to transform the way we deliver healthcare. As an example, specific tests will identify a disease before the symptoms have shown, allowing care teams to intervene early and manage it before it’s too late.

Genetic characteristics, as well as social features and lifestyle choices have been found to be key factors in the development of many diseases. That is why data collected from research centres, community care hubs and smart devices can make a significant contribution to the modern patient profile, in addition to the more traditional health data from hospitals, laboratory systems and pharmacies.

Making the influx of information meaningful

The application of business intelligence and reporting to the modern dataset that makes up a patient, is very powerful. Identifying patterns and revealing insights based on the data, can help clinicians make more informed decisions about a patient’s care, but also empowers the patient to be engaged with their health. BI and analytics tools can suggest changes a patient can make on their own to improve their health. Modifying behavioural habits can reduce clinical problems and improve health, as in the case of smoking, unhealthy eating[2], and alcohol consumption[3].

In a time where individuals have access to a vast amount of medical knowledge online, they feel empowered to take better control over their healthcare. The patient is not passively receiving care anymore, they are an active participant.

Overall, governments, organisations and institutions are making use of analytics to understand how to improve the complex healthcare system. The value-based care model, in particular, takes advantage of analytics insights to re-allocate resources where they can be most effective. That results in funding successful programs, modifying behaviour and activities to improve patients’ health, and thinking of alternative ways to treat individuals.

How can a health technology solution be successful?

From including non-traditional data types, to enabling population stratification based on risk factors, a technology solution has to embrace the flexibility to cater for an ever-growing longitudinal patient record. As the medical history definition expands to include genetic tests and social determinants of health, clinical decision support systems need to be put in place to help the clinician in navigating the amount of information available and making the best decision possible. Finally, the solution should carefully consider patient engagement. The data needs to be curated and presented in the most appropriate form, so that the individual can easily understand the information and can act upon it.

This is simply an introduction to the model behind the 4 Ps of medicine. The next blog in this series will focus on the predictive side of precision medicine.