Predicting the predictable
If you’re still reading I’m presuming at this point you are completely convinced that AI is the solution to some of healthcare’s biggest problems.
I want to finish by demonstrating the breadth of application these novel techniques can achieve. Surgery is often a hospitals most expensive and resource intensive activity, costing up to 42% of their budget. In all hospitals I have worked in surgical cases almost invariably overrun and cancellations occur. This is costly for the hospital as well as the patient. At times this cannot be prevented because of emergency cases that arrive unexpectedly. Elective (planned) procedures should for the most part run on time. The following paper, released in 2020 by the Artificial Intelligence Innovation Center in China, outlines a novel way of tackling this historic problem.
The team have trained an ensembled decision tree (see below), appropriately named “XGBoost”, to predict the surgical case duration of elective cases in order to maximise the efficiency of their surgical planning. They have trained the model on over 170,000 training cases and validated on a separate 8,500 test cases. Slightly more cases than your average theatre co-ordinator is likely to have seen… They estimated the current performance of surgical planning based on classic booking methods to be between 19–37% accurate with a 15 minute margin on either side of the slot. Machine learning in this instance, achieved a 51% accuracy by the same measure.
What excites me most about the surgical planning example is its scalability. There’s no reason a similar process couldn’t be used to predict all sorts of poorly optimised healthcare resource allocation procedures. In the height of covid, when resources are scarce, this is of crucial importance in my opinion.