Outcomes and lessons learned

The resulting code, released as open source on our GitHub (available to anyone to re-use), enables users to:

• load information about wards, placement, and students into the tool
• run the genetic algorithm to produce up to 10 schedules
• dee score comparisons across produced schedules, allowing quantitative comparison
• automate mandatory reporting
• where relevant technical expertise is available, the scoring metrics could be extended and updated to reflect exactly what the user would be looking for

The tool is estimated to save hundreds of hours constructing and analysing schedules for nursing students. This time can be spent much more effectively elsewhere, thereby freeing up placement coordinators to utilise their expertise across the Trust. Additionally, the tool produces improved schedules by consistently taking into account the wards and specialities that a student has already undertaken a placement within.

It should be noted this tool aims to support placement coordinators, as the process of producing placement schedules isn’t just fitting together all the pieces. Bespoke requests will come in from students, requiring tweaking of placement schedules to accommodate a wide range of circumstances. This tool aims to provide a high-quality baseline from which placement coordinators can construct schedules which meet every requirement, from all over the trust.

Finally, a successful element of this project has been the knowledge sharing and mutual upskilling between the two parties. AI Lab Skunkworks helped Imperial College Healthcare trust identify and implement a novel AI-led approach which provided a solution they may otherwise not have been able to explore. Meanwhile, Imperial College Healthcare trust shared their expertise in how the placement allocation process works, particularly what does and does not work well. This was useful underlying information the AI Lab Skunkworks team hopes to share more broadly across the range of projects we work on.