Circular Wave are pleased to announce the release of Workforce AI which consolidate our pioneering workforce management analytics, insight and prediction modules.
In partnership with our NHS and private sector healthcare clients, we have developed cutting-edge predictive and simulation functionality to support complex healthcare workforce processes.
To do this, we have applied the latest in machine learning to new and ground-breaking applications which improve the productivity and efficiency of health and social care providers.
Cutting-edge solutions to workforce problems
We’re developing AI/ML technology around several areas to support our clients and optimise their processes.
Likelihood to fill a shift
As soon as a gap in a clinical roster emerges, workforce managers go to work with a range of options to fill the vacant shift and ensure their ward has a safe level of staffing to operate safely.
Across health and social care, NHS and private sectors managers have options depending on their workforce processes as they search for the contingent workforce. This can include an internal staff bank, collaborative or digital staff suppliers (NHSP, Locum’s Nest, Patchwork, Lantum) and traditional local agencies.
Effective Agency Management System can handle this at a single click of a button radically reducing the time to publish a vacant shift to multiple suppliers and most sites now have digital systems to automate and streamline this process. This process is increasingly replacing the traditionally manual process of phoning or emailing multiple local agencies.
Fortunate managers will have a range of options but what they don’t get is the likelihood of the shift to be filled by any such supplier. This is crucial information to a manager as it gives them confidence and enables them to act proportionately.
It’s obvious that to think that a thriving internal staff bank is more likely to fill a Monday day shift with a week’s notice than it is to fill a Sunday night shift that’s been released that day - we’re able to quantify this and include far more complex variables.
We get this likelihood by training an AI algorithm to run on the providers own historic data, over a period of time and in conjunction with extensive predictive features which capture deep behavioural trends that contribute to process.
Once a manager knows there’s only a 5% chance of filling the shift with the regional collaborative staff bank, they know to spend more time negotiating with their preferred agency - you can see the value.
This is just one example of Workforce AI in production. Beyond this, we’re developing AI/ML technology around:
Forecasting retention and engagement
Chatbots / FAQ
With many more to come! Like all our application we work with our healthcare clients to configure and customise our solutions for their specific requirements and toughest challenges.