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May 5

Personalising population health – PharmaTimes

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In addition, by using anonymised patient-level and real-world data to identify specific cohorts of patients best suited to a particular medicine or drug, pharma companies can advance the use of personalised medicine.

By using real-world data and predictive modelling to explore outcomes, pharma can work with healthcare services to get closer to the right medicine, to the right patient, first time. By tailoring and marketing specific medicines to cohorts of patients, the potentially arduous trial and error process to find the appropriate treatment can be reduced. For example, looking at different cohorts of asthmatic individuals, and merging both predictive modelling and personalised medicine to find out what treatment is likely to work best for specific symptoms, can help inform the prescription of one inhaler over another. This knowledge allows clinicians to enhance diagnosis, prognosis and tailor their medical treatments. This enables patients to receive the best medical support which could lead to significant improvements to their life during and post treatment.

Closing the loopPersonalised medicine can be taken one step further. The knowledge gained from real-world, longitudinal data in a personalised medicines approach can be overlaid back onto population health data to conduct hypothesis testing with a view to risk prevention and disease management.

Research has found that using a Predict, Preventive and Personalised Medicine (PPPM) approach will become a focal point of efforts in healthcare aimed at curbing the prevalence of both communicable and non-communicable diseases.

Predictive analytics can also be used to empower patients about their health conditions and health risks. We can begin to do more than just question whether there is a statistical significance between heart disease and diabetes; rather we can look at the clinical condition of patients with long-term conditions, such as diabetes, to determine why a clinician may have opted to move a patient on to another class of drug. This may be, for example, BMI, co-morbidities, blood pressure, and so on. We can then look at the outcomes for those patients in terms of heart disease, significant clinical events or control of their diabetes and weight loss for example. This awareness allows patients to make changes to their lifestyle or behaviours to avoid future health deteriorations, or detect them before they arise based on the information available around the individual and disease history.

Having an understanding of what will impact an individuals overall health and well-being is being called upon in many sections of public health, especially as many diseases and conditions could be prevented or detected earlier. By merging both population healthcare and personalised medicines data, and combining this approach with tools such as predictive modelling, risk prevention and hypothesis testing, we have the capability and opportunity to transform the way we deliver healthcare for the better

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Personalising population health - PharmaTimes

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