With $2.5billion raised by healthcare AI start-ups in the first quarter of the year, there can be little doubt that the sector at large is seen as ripe for opportunity. This opportunity will only have been accelerated by the pandemic, given the need for efficiency to clear backlogs in the system, as well as the funding pressures it has placed on public healthcare systems.
However, while no-one disputes the potential of AI technologies, the claims made for how they might transform healthcare businesses – and create huge value in the process – look premature. One recent study from the Stanford Institute for Human-Centred Artificial Intelligence warned that we probably won’t see the big breakthroughs that will fundamentally change how doctors diagnose and treat patients until well into the 2030s.
So, with caution needed around some of the AI hype, where will it really move the needle for healthcare companies, and in doing so increase their value?
1. AI to improve quality of clinical output
Improving the quality of care that is provided to patients is fundamentally important to not only the success of healthcare companies, but also – crucially – it saves lives.
For example, ECI portfolio company, 4ways, is a leader in applying AI within the teleradiology space. It has deployed AI that works in the background of the radiology workflow, automatically detecting and alerting radiologists to potential significant clinical findings, allowing those individuals to ensure they are providing the highest quality of care.
From an investor’s perspective further improving the accuracy of diagnoses and supporting clinicians with a technical safety net, provides a clear differentiator over competitors, while importantly improving outcomes for patients. However, being able to leverage such AI is not easy, and it is important to be able to understand the reality of use cases, versus the theoretical, when assessing a potential investment. AI can be difficult to deploy within the healthcare sector as it requires significant pools of data to train, that data is often unstructured, and health data is particularly personal and sensitive, so has tough regulatory standards quite rightly governing access.
This is shown by the journey 4ways has already been on prior to being able to deploy such technology, ensuring data is usable, adhering to strict governance standards and working on cutting edge platforms such as IBM Watson Health’s Merge PACS™ 8.0 platform, which they have recently upgraded to.
Healthcare businesses that understand these deployment challenges and have gone through the process of scoping and cleaning their data, before trying to leverage it for AI, will be in a much better place to deliver on its potential and consolidate multiple algorithms across a breadth of areas.
2. AI to drive efficiency and efficacy
Efficiency is a much-lauded word in healthcare, a panacea to the problems facing a sector that is often understaffed and overburdened. Certainly, if one looks at the NHS in the UK, the funding pressures combined with a backlog of activity from the pandemic, means that without leveraging technology to drive efficiency, the health service and its infrastructure are likely to face significant difficulties. For instance, in the case of radiology, there is an estimated prevalence of 40% burnout within radiologists. There is clearly an imperative to utilise the manpower resource more effectively without increasing the intensity or burden of work.
There are a number of reasons for inefficiencies, including simply the volume of the people and patients within it, vast quantities of data to analyse (of varying types and quality), often fragmented and antiquated technology systems, and a procurement system that can slow down implementation at times.
What that means is that to be successful, healthcare companies will need to make it clear how they are delivering value to their clients. One of the main ways that will happen is through triage and prioritisation, supporting early detection of diseases, more efficient analysis of scans, reduction of repetitive manual tasks, or being able to notice patterns of behaviour earlier on. That efficiency will also need to be scalable, with only 54% of AI developers in the UK surveyed by the NHS’s AI lab recently claiming that their product will be ready for development at scale in one year, most others being much longer.
If healthcare businesses are considering deploying AI, it is key for them to understand what ROI it can genuinely deliver and how that benefits clients and ultimately patients and how successfully it has been proven to date.
3. AI to improve people’s jobs
The future of healthcare will undoubtedly be underpinned by AI, but it’s unlikely that the sci-fi images of robot doctors will be seen and certainly not any time soon. Research from PwC suggests that only 39% of Britons would be willing to engage directly with AI for their healthcare needs, with many people feeling uncomfortable about the idea of depending solely on technology for their care.
Where AI is delivering real value is through working in conjunction with and supporting qualified individuals to ensure accuracy of diagnoses and making their jobs as efficient as possible through removing manual repetitive tasks. Allowing healthcare professionals to focus more of their time on high-value tasks means they are able to provide better care to patients, but also it will create more job satisfaction, drive retention and allow them to focus on the more interesting parts of their role. This is especially important given an ongoing skills shortage within the healthcare sector, with Nursing Notes highlighting that the 93,806 full-time vacancies across NHS England is up by 13% compared to last year.
What is clear is that AI will play a greater role in improving the quality of care that healthcare companies can provide, the efficiency with which they are able to do so and the overall experience for the patient. Those that can demonstrate they can enhance their business with AI, will be able to command a higher valuation premium in M&A.
For investors, understanding the realities of those use cases, and ensuring that they are operating with robust governance, will be a key part of due diligence in M&A transactions of the future.
