Leveraging the value of innovation and disruptive technology in healthcare
The term ‘disruptive’ often has negative connotations, but when it comes to innovative technology in healthcare, it is overwhelmingly positive. Initiatives such as Scan4Safety (using barcode technology to identify patients and products) are helping improve patient safety and Near Patient Testing (or Point of Care Testing) technologies are helping deliver care quicker and closer to patient homes.
While there is little doubt the British health and social care system will see considerable operational and clinical gains from the implementation of much needed emerging innovation, it still has a long way to go to be considered a leading user of technology and to reap the benefits it can provide.
In this article, we look at some of the ways in which health and social care organisations can make incremental leaps in efficiency with the latest technological innovations, some of which are available today, and some of which are close on the horizon.
The role of technology in integrating health and social care
UK hospitals are now seeing unprecedented demand for beds. At the start of the 2017/18 winter, one in five hospitals ran out of bed space, with some trusts officially declaring they had reached 100% occupancy 99 times in just two weeks. And the average daily bed occupancy of general and acute beds has increased from 89.8% to 91.4% over the past five years.
The bed capacity problem is well known and multi-faceted, two of the key driving factors are:
- The UK population is increasing and getting older. By 2046, it is estimated that 24.7% of the UK population will be over 65, compared to 18% in 2016.
- More patients with long-term conditions are increasing the complexity of patient care requirements, e.g. the number of patients with diabetes has increased from 6% to 6.7% over the past five years alone.
Another well-established cause of reaching maximum bed capacity is not being able to discharge patients who are medically fit but require extra social support and not being able to discharge patients who require some ongoing monitoring. Technology can assist this process in several ways:
- Remote monitoring & medicine – by using telemonitoring technology, patients who are generally well but require ongoing monitoring can return to their home in the knowledge that should their health decline, or should a problem emerge, their clinical team will be automatically alerted. This would enable earlier discharge, freeing up capacity, but allowing a quick response if the patient’s health deteriorates. Telemonitoring and telemedicine are now advancing rapidly. One such solution is Kardia ECG, which is able to record a high standard cardiac rhythm using a small and relatively inexpensive pad which links to a standard smartphone. With the application of special algorithms, amongst other benefits, the software can assess for arrhythmia, and keep a close watch on patients with atrial fibrillation – even detecting this early.
- Integrating data – there are many programmes at governmental level looking at how technology can help make health and social care work together more efficiently. At present, there is a persistent lack of data integration between the two sides, but rather than waiting for a national strategy, it is possible to achieve better integration with existing technology. By using existing data integration engine software, data analytics tools, and bed capacity management solutions (which are used by some NHS trusts to gain a real-time view of hospital bed capacity) in health and social care settings, it could be possible to have an overview of capacity across the regional health and social care system. Using such an approach would also offer social care providers a picture of the discharge needs of hospitals, allowing them to plan accordingly.
Doing more with fewer clinical resources
One of the consistent themes of the latest disruptive technologies in healthcare is undoubtedly maximising the use of available data. In the past, data has resided in silos, unable to be harnessed in ways now possible as data becomes increasingly integrated. By correctly analysing large datasets, patterns emerge which can point to large cost savings and efficiencies, perhaps with minor or inexpensive changes; otherwise known as ‘low hanging fruit’. It may even be possible to use predictive analysis to proactively alert operational managers to problems that are yet to occur. The challenge is seeing through the noise of the data to find the nuggets of gold.
Machine learning is also extremely powerful and has the potential to transform data analytics and clinical care. One way in which machine learning can be used is to augment the ability of clinicians to find disease in patients. Using algorithms, new software solutions can now rapidly analyse patient diagnostic images to find clinical signs of illness.
Large potential improvement in operational efficiency may be realised by using in-home devices to capture data on patient progress following surgery, thereby ensuring close monitoring, and crucially negating the need for face follow up appointments. Having such larger volumes of patient data (as opposed to data collected purely in clinic) will provide a much more accurate view of how the patient has healed, and at a much lower cost.
We are entering a world of personalised medicine, in which therapies, medication and interventions are bespoke to our unique genetic makeup, medical history, environment, and personal characteristics. Large-scale initiatives such as the 100,000 Genomes Project, in which 100,000 complete human genomes will be sequenced by the end of 2018, will enable the recommendation of treatments that are optimised for an individuals’ genetic profile. This will lead to many improvements and efficiencies including earlier detection of disease, improved health outcomes, less need to repeat or try new treatments due to ineffective drugs, less time in hospital, fewer drugs needed, and less time required by clinicians.
In the future, with the aid of handheld DNA scanners, a patient’s entire DNA profile will be uploaded, and illnesses or markers of future disease picked up before the patient is even aware. Such technology certainly raises ethical and moral questions, but the potential for re-engineering our model of care from one that is reactive to proactive is considerable.
Personalised medicine will also go a long way to assisting health and care commissioners, and managers understand the ever-growing clinical complexity of patients’ clinical and medication needs and how to most effectively allocate scarce resources.
How can we help?
Akeso & Company offer unrivalled experience and expertise in the areas of Healthcare and Life Sciences operations. We were appointed as the Category Tower operator for Diagnostic technology and associated Consumables in the NHS Supply Chain Future Operating Model (CT8) in January 2018.
Through continuous market horizon scanning, Akeso has a broad and comprehensive understanding of the latest innovations and disruptive new technologies which have the potential to deliver transformative value to the UK health and care system.
We work with healthcare comissioners, healthcare providers and technology suppliers, and take an end-to-end view of the clinical pathway to fully understand the potential of an innovation or technology to improve clinical outcomes, reduce risk and bring about operational / cost efficiency.
For more information, please call on 020 3011 1381,or email firstname.lastname@example.org
Overnight Bed Availability and Occupancy Data. NHS England
UK Population Data. Office of National Statistics
Disease and Risk Factor Prevalence Data. Public Health England