Over the last few years, there has been a number of exciting discoveries in the world of Physics, as predictions made many years earlier have been confirmed. Famously, of course, researchers operating the Large Hadron Collider confirmed the existence of the Higgs Boson in 2012, predicted in 1964 and which remained to be confirmed by experiment. As a result, Peter Higgs and François Englert were awarded the 2013 Nobel Prize in Physics for their work and predictions.
For me, however, more exciting was the detection of gravitational waves in February 2016. Predicted in 1916 by Albert Einstein on the basis of his theory of General Relativity, the two LIGO interferometers in the US identified the gravity waves generated by a pair of merging black holes. Since that initial finding, several other events have been detected, by both LIGO and the European Virgo detector. As a result of this work, Rainer Weiss, Kip Thorne and Barry Barish were awarded the 2017 Physics Nobel.
In August 2017, the interferometers detected gravity waves from two merging neutron stars, and were able to locate the event in space sufficiently well that it could also be seen by telescopes working in the electromagnetic spectrum. A gamma-ray burst associated with the neutron star merger and occurring 1.7 seconds after the gravitational wave was detected by the Fermi Space Telescope. This was corroborated by the electromagnetic follow-up of the event involving 70 observatories, yielding observations over a large region of the electromagnetic spectrum which further confirmed the neutron star nature of the merged objects and the associated kilonova.
This is what really enthused me; the idea that by combining data from the new gravitational wave observatories with results from more traditional electromagnetic detectors, new things could be discovered about the universe.
This multiplicity of observing methods leads to many new insights, and there is a lot of excitement in the field about how the use of multiple different observational methods may lead to new and exciting results.
As above, so below, and in many diverse fields. In our own work at Sollis on population health, we have for many years understood the very real benefits of using multiple perspectives to understand the health and care needs of a geography, whether an STP, a CCG or a smaller locality, such as a Primary Care Home (PCH) footprint. We recognise that it usually isn’t sufficient to simply target patients based on their age, or even their morbidity burden. Equally, the use of calculated measures, such as risk scores or the Electronic Frailty Index (EFI), needs care. The data must be examined to understand the overlap and differences between these multiple different views of a population, and the nature of the inherent local variation between patients.
At the same time, to successfully define an intervention to support a specific group, however defined, it is important to consider questions of Impactibility. This may simply consider the clinical characteristics of specific diseases and how susceptible patients may be to improvement in their care. A well-known example is to focus on Ambulatory Care Sensitive conditions (ACSCs), which have been identified as conditions that are amenable to improved care in the community to reduce acute hospital pressures. However, for a more complete picture supporting a far wider range of patients it is usually necessary to also explore local social and environmental factors. A useful starting place in this area may be the use of the various indices of deprivation, but there are many other similar tools available.
This leads naturally to considerations of Patient Activation, particularly where an intervention requires specific action by the patient to be effective. To reach certain groups it may well require specific, targeted communication and encouragement, for example through voluntary groups or the use of key messages during consultations. Using appropriate lenses it is possible to identify more efficiently those patients who would benefit from such additional engagement.
From such analyses, making appropriate use of demographic, diagnostic, calculated, social, environmental and other perspectives, it becomes possible to meaningfully segment a local population and define appropriate interventions. Unlike the detection of gravity waves, this isn’t bleeding-edge science, but it does require deep understanding, detailed analysis and robust testing of results. There won’t be a Nobel Prize in the offing for this work, but — done well — we do believe there are other rewards, including many significant improvements to the provision of health and care in a given area.