In reality, technologists are still trying to develop the methods to benchmark the performance of these technologies, organisations such as Microsoft and Google are working to assess LLMs for health and care applications. An additional problem unique to LLMs is that they can – and do – make up information that is not factually correct or evidence-based. This ‘creativity’ risks introducing non-evidence based or clinically validated information in response to medical questions. Many questions must be answered before LLMs are ready for clinical use, for example: how much of this creativity is acceptably low risk? What are the best-use cases for LLMs and other similar AI in health and care? Can the combination of people and processes mitigate the fallings?
It's logical to assume the most impactful way to improve health care services for patient benefit is by applying these AI tools to patient-facing services. However, we shouldn’t overlook the significant potential for this technology to be applied to operational activity, eg, for reporting and creating funding applications, creating commissioning documentation and procurement information, or by creating job descriptions. LLMs have the potential to reduce the time to complete tasks and in doing so enable the limited time and attention of staff to be used differently. This is particularly important when there’s a shortage of non-clinical staff, for example, health services and public health managers and directors are on the shortage occupation list open to visas for overseas workers to boost staff numbers. As well as managers there are real challenges when it comes to specialist roles in digital, data and technology.
The Hewitt review identified the need to recruit into digital, data and technology roles within health and care to continue the evolution of the health care system towards better use of data. The review flagged the existing shortages in this specialist technical workforce and the challenge of recruiting to vacant posts when needing to provide salaries that compare with those on offer in other sectors including technology companies. LLMs have the potential to help alleviate some of these pressures if the technologies are developed for non-clinical use in health and care settings.
The non-clinical uses for AI potentially have lower regulatory requirements and shorter timeframes for demonstrating value. AI has the potential to improve system and service efficiency, productivity and accountability while relieving workload pressures and overcoming skills shortages. To realise these benefits there needs to be greater focus and drive behind the development, evaluation and deployment of AI for non-clinical use, as well as skills and education development for non-clinical staff to use these technologies to the fullest extent.