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Using AI tools to streamline administrative effort
One of the areas in which AI tools are already delivering value is in streamlining administrative effort. The use of robotic process automation (RPA) to streamline routine tasks helps hospitals and other healthcare providers to operate more efficiently. This means that a larger proportion of funds can be directed into delivering frontline services.
Microsoft Copilot for Microsoft 365 is an exciting new development which has a lot of potential to streamline back-office and administrative services, especially in automating patient communications and searching for information from multiple documents and sources.
2
Improving facilities management with AI
There is huge potential for AI-powered solutions working in conjunction with Internet of Things (IoT) devices, such as meters, sensors and monitoring solutions, to help minimise the overheads of running a hospital campus and all the associated HVAC services. As well as helping to reduce energy costs, it can help to optimise space utilisation and help healthcare providers achieve their net zero goals.
Additionally, predictive analytics can be applied to monitor services use, facilities use, patient footfall traffic, staff workflows, rotas and shifts, and other metrics. This way, healthcare providers can predict and optimise all aspects of facilities management.
3
Summarising patient records using AI
Many healthcare providers have invested in electronic patient records (EPR) to help integrate patient data. However, it can still be a challenge for physicians to navigate through the information held in these systems. AI offers the possibility to search multiple systems and information sources quickly, to present pertinent patient information quickly and in summarised fashion, thereby aiding physicians and enabling more appropriate responses.
Of course, there are some data privacy and governance concerns around this. Healthcare providers must be confident that any localised and personal patient information used by the AI solution is not used to train the underlying AI models so that data security is maintained.
4
Aiding diagnoses with AI
AI is already being used and tested in a variety of diagnosis scenarios. Models are being developed that are more effective in interpreting scan data than their human counterparts. For example, in the area of cancer diagnoses from recorded images. It is hoped that these breakthroughs will help to speed up diagnosis, make diagnosis more accurate and even help to identify disease earlier.
