The last year has seen incredible transformation in the healthcare industry, with technology playing a central role.
For example, we have seen amazing research progress at phenomenal speed in efforts to tackle the novel Coronavirus SARS-CoV-2; with rapid development of new treatments, rigorously tested in time frames only dreamed of before.
Technology has also been rapidly adopted to help maintain services while minimising the risk of infection.
And, as societies reopen, getting hospitals, clinics and research centres back to pre-COVID focuses is the next priority.
With so many new technologies and processes embedded in such a short space of time, there is also a new opportunity for the healthcare sector to fully embrace innovation with the more-widespread adoption of machine learning (ML).
ML techniques have the ability to influence clinical care by utilising the vast qualities of data generated by technology adoption in healthcare, presenting insights from ML algorithms to the clinician informing clinical decision-making.
With so many new technologies and processes embedded in such a short space of time, there is also a new opportunity for the healthcare sector to fully embrace innovation with the more-widespread adoption of machine learning
The growing confidence in the power and breadth of these technologies resulted in the UK Government pledging an additional £250m to boost the role of Artificial Intelligence (AI) within the health service post-COVID-19.
With technology as an enabler, medical professionals have the potential to work with greater efficiency, allowing greater time with their patients and more-personalised care.
One such example of where technology is already having a positive impact is in the development of sophisticated algorithms that have helped improve patient care during the pandemic.
Embracing predictive models
ML analysis of large sets of real-world data, such as electronic patient records, can create predictive models and algorithms.
One benefit of this is being able to more accurately predict certain health conditions developing in particular patient groups, potentially enabling earlier intervention, or identifying treatments that are likely to be most effective for a particular patient.
By comparing an individual’s healthcare records against a database of millions of other anonymised patient records, clinicians have more information to help inform their, and their patients’, decision making and personalise treatment plans.
For several reasons, not least that human interaction, not technology, is at the core of the clinician-patient relationship; the sector previously has sometimes been slow to embrace new technologies.
However, the use of technology has become widely accepted by clinicians as capabilities develop and evidence of their clinical impact becomes apparent.
A good example of how this works in practice is SYNE-OPS-1, an operational AI algorithm, launched earlier this year, which provides real-time operational decision-making support to NHS managers who are coping with the pandemic.
By comparing an individual’s healthcare records against a database of millions of other anonymised patient records, clinicians have more information to help inform their, and their patients’, decision making and personalise treatment plans
The algorithms offer an hourly risk forecast for the number of patients expected to be transferred to ICU or be put on mechanical ventilators, allowing hospital managers to direct their resources more effectively as patient numbers fluctuate.
The pandemic has highlighted to clinicians that using modern technology can augment the decision-making process and relive pressure on healthcare services – something that is going to be critical if we are to recover from the 4.7 million appointments missed as a direct result of the pandemic.
Applications for maternal health
Maternal health is a prime example of where technology has been embraced by clinicians and patients, and is already being used as a force for good in ensuring the safety of expectant mothers and their babies.
Regular check-ups and monitoring are a key part of the care of pregnant women, and with Government advice to limit face-to-face appointments in this vulnerable group during the COVID-19 pandemic, new processes had to be rapidly put in place.
This ensured women were remaining healthy during pregnancy and surveillance for, for example, for hypertensive disorders that affect one in 10 pregnancies worldwide, was able to occur safely.
As with much of our lives now, video calls have become the norm, and recent UK research has shown the healthcare industry is no exception.
Over half (57%) of those surveyed believe the ability to see a healthcare professional remotely during the pandemic has been important and helpful.
And, for maternal health, these continued virtual connections have been critical in the ongoing support for women during their pregnancy.
Predictive modelling and algorithms, coupled with remote patient monitoring, have made it easier and safer for clinicians to identify when specific treatments are needed.
The pandemic has highlighted to clinicians that using modern technology can augment the decision-making process and relive pressure on healthcare services
They can better predict which women are likely to need medication to control diabetes during their pregnancy, or which women should adopt certain lifestyle measures and take earlier intervention to help them.
Remote patient monitoring increases efficiency in healthcare, by allowing clinicians to filter patients and change the operational mindset from ‘who are we seeing today’ to ‘who needs attention today’ and prioritising those who are most unwell.
The future of predictive healthcare
We are also likely to see the predictive healthcare model applied to other areas of the healthcare sector.
Chronic diseases like diabetes, chronic obstructive pulmonary disease and heart failure will, in the very near future, routinely benefit from predictive modelling and remote patient monitoring, not only making it quicker to identify illnesses, but also improving patient care.
While long-term management by remote monitoring is more challenging, specific short-term intensive support can be well addressed for patients suffering with chronic diseases, particularly when new treatments need starting.
For example, for patients with type 2 diabetes who are transitioning to using insulin therapy, or heart failure patients post discharge from hospital, short-term monitoring can provide intensive support and care.
Chronic diseases like diabetes, chronic obstructive pulmonary disease and heart failure will, in the very near future, routinely benefit from predictive modelling and remote patient monitoring, not only making it quicker to identify illnesses, but also improving patient care
The last 12 months have seen technology become an invaluable asset for the healthcare sector, more than it has ever been before.
And. as the level of understanding, acceptance, and capability of technology increase, the more opportunities there are to make a difference.
Although previously the healthcare industry has been slower to embrace new technology; AI and ML are now helping to drive a digital healthcare revolution.