Personalising population health

The King’s Fund defines population health as: ‘An approach aimed at improving the health of an entire population.
It is about improving the physical and mental health outcomes and wellbeing of people within and across a defined local, regional or national population, while reducing health inequalities. It includes action to reduce the occurrence of ill health, action to deliver appropriate health and care services and action on the wider determinants of health. It requires working with communities and partner agencies.’
At its broadest, population health aims to understand and inform health outcomes of wider groups of people using organisational data, rather than individual data, which is collected, analysed, interpreted and distributed to inform and develop public health interventions. In the UK, this internationally collected data helps inform the National Institute of Health and Care Excellence (NICE) clinical guidance based on a picture of what the average patient looks like, which can then be applied to the general population.
As part of the NHS’ Long Term Plan, the overall goal is to improve population health by deploying solutions to support Independent Clinical Services (ICS) to understand the areas of greatest health needs and offer appropriate services to meet these. By utilising public resources, we can better enable people to live healthier lives by identifying groups of people who are at risk of adverse health outcomes.

There’s no population health without personalised medicine
Whilst based on general, statistically significant data, NICE guidance is a template that doesn’t take into consideration either a patient’s specific clinical parameters or personal circumstances to adapt treatments or medication to suit not only the clinical endpoints but also individual needs. Clinicians will more than often deviate from these guidelines because they have a closer relationship to the patient, and can therefore make a grounded decision – firstly whether to tailor the selection of treatment, and secondly, how the treatment applies to the patient. This is the essence of personalised medicine.
Population health doesn’t tell you whether a single mum diagnosed with breast cancer may opt for a mastectomy over radio or chemotherapy due to concerns over her life expectancy, or whether an elderly man with prostate cancer wants to choose a treatment which doesn’t impair his enjoyment of life. Or indeed, whether their lifestyle and life choices might adversely affect their adherence with a treatment pathway that they haven’t been fully consulted on. Using the power of anonymised patient-level data, the one-model-fits-all population health guidance can be customised to the individual.
The ability to overlay anonymised patient-level data onto NICE guidance, as well as local and national patient pathways, allow us to understand what treatment may work better for cohorts of patients based on subsections of similarities, such as existing conditions and co-morbidities. This data can then be delved into deeper by examining what may impact individuals within those specific subsections, such as age, sex, weight, faith, ethnicity and lifestyle choice, to get closer to the best treatment first time.
Essentially, personalised medicine is a fundamental component of improving population health; you can’t have one without the other. The two can be used in unison where population health acts as the foundation by holding this overarching amount of data about the population, which can then be tailored using personalised medicine to suit the patient’s needs. It is this interconnection which makes it possible to move to an era of truly personalised and better care.

The role of pharma
Pharmaceutical companies can play a vital role in delivering personalised medicine through Patient Support Programmes (PSPs.) Through funding and support, patients are provided with a level of care and communication from nurses, which is used as a mechanism to improve access to, usage of and adherence to medication.
For example, community visits from nurses to patients, such as those who are elderly with arthritis, can provide help with administering their injectables. Having access to a funded personalised guidance and support treatment regime will have a positive impact on a patient’s journey, which is part of the NHS’ Long Term Plan to offer more person-centred care. PSPs have the ability to deliver advanced patient outcomes which will make a fundamental difference to their quality of life, strengthen patient and healthcare professional relationships, and prevent unnecessary (re-)admissions, saving money in the long term.
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 loop
Personalised 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 individual’s 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.

‘Personalised medicine is a fundamental component of improving population health; you can’t have one without the other’