The World Healthcare Organization (WHO) estimates that by 2030, the global healthcare workforce shortfall will be equal to 11 million health workers, predominantly in low- and lower-middle-income countries. The shortage spans almost all roles in the workforce, including nursing professionals, general practitioners, healthcare assistants, home-based care workers and more. So, could AI provide the answers?
GlobalData points out that the workforce shortfall is not only hampering the workforce’s ability to deliver healthcare to the public but also exacerbating existing retention challenges caused by burnout and stress. According to Tyler Fletcher, executive vice president of healthcare data at GlobalData, there is huge potential for AI to support in redesigning the next generation of work in the healthcare sector, but organisations should be wary of scepticism which could limit their success.
“The prospect of joining the healthcare workforce has rapidly become synonymous with high workloads, long hours and a quick route to burnout,” Fletcher explains. “The future, however, is looking brighter, and there is promise that new advancements in AI are turning the tide. With time, they will go some way to addressing some of the issues facing healthcare workers.
“Crucially, these advancements face the usual challenges of fitting snugly into already tight budgets while low levels of trust from employees will no doubt persist. Through careful implementation, AI’s ability to deliver more accurate predictive models, sophisticated data analysis tools and advanced systems for automating clinical and administrative tasks will provide crucial respite for workers.”
One of the most prominent use cases of AI in medicine is its ability to analyse medical imagery. AI-assisted computer tomography (CT) imaging has the potential to enhance the utility of traditional CT imaging by automatically diagnosing changes in organ features as an indication of disease. This greatly helps radiologists and other clinicians accelerate the time taken to interpret images, which traditionally takes 30-40 minutes per scan. Faster diagnoses will ultimately lead to improved patient outcomes.
Elsewhere, AI-powered ambient scribe tools are being deployed to record entire patient visits, create transcripts and draft the necessary clinical notes. A test run of Microsoft and Nuance’s tool DAX found that doctors reported it reduced their workload, with 57% reporting that they spend less time on clinical documentation.
“Combining the masses of available medical data with AI opens up a world of new potential, and it is promising that it is already being used to detect changes before they manifest more seriously. This could trigger a shift in many practitioners focusing their time and work on preventative care,” Fletcher continued.
“There is also the more business-focused argument for AI to understand profit margins. Within all healthcare organisations, budgets are decreasing and need to be managed more effectively than ever. AI tools can efficiently analyse and support business functions, identify avenues for cost reductions, and assist management teams with their efforts.
“Access to data-led insights is also at its best. In the past, the avenues for addressing challenges like burnout have been predominantly supported by in-person surveys or anecdotal evidence. Now, management teams have access to tools that can highlight causes for concern in staffing and notify managers of potential for burnout before they happen.
“Though innovative, these applications do also carry significant risks, including potential job displacement, increased complexity of medical information and cases, and the practicalities of the necessary skills shift coming hand in hand with the danger of diminishing clinical skills.”
