Hi everyone,
I would like to develop and test a surgical scheduling decision support system, to predict rates of surgical delays and cancellations. I plan to collect retrospective data on patient demographics, health history, surgical times, etc to train the model. The prototype will then be tested against prospective data for sensitivity & specificity.
How can i go about this?
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First you find the data (or a suitable dataset), then you clean the data.
Perhaps at this point you want to see which parts of data is most useful to you and use that only to predict your output! That means that you have to perform feature analysis, check Kaggle courses…
Next you build an ML model (may be able to use a lot of frameworks depending also on type of ML model), perhaps for tabular data the tree and forest models seem to perform well.
Then you train this model with the data you gathered, and fix parameters as they come along according to the evaluation of the training phase.
And hopefully once you get good performance on the previous stage then you have a good model to be used to predict.
Thats a rough guideline!
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To the previous sensible reply I suggest also
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survey what is already being discussed in this speciality, such as Fujitsu's quantum-inspired computing helps uncover additional OR capacity | Healthcare IT News
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think broadly about what optimization means here; it’s potentially a very complex topology.
Operating Room Performance Optimization Metrics: a Systematic Review - PMC
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To support what you suggest about ‘optimization’ in this case there are also economic and social ‘externalities’ to consider.
I have a good older friend who he and his wife are reasonably well known regional artists-- Though their son became a big-wig ME atIntuitive / Da Vinci Surgical (i.e. one of the robots).
I should remember to ask him next time we speak where they are at with all this.
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Indeed. In fact, each set of stakeholders - surgeons, nursing staff, hospital CFO, patients, etc - may have different and competing priorities and success metrics.
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Unfortunately, not only the people building such models, but more importantly those willing to pay for them (unless this Bhutan, and I’m not sure that ‘heard factoid’ is still true) are optimizing for ‘overall happiness’ 
(and then 
).
Thank you! I plan on taking into account nursing resource allocation (their years of experience, skillset, etc) into my model.
My takeaway from @Nevermnd ’s reply is that some important components of ‘optimization’ of a clinical service may not be limited to dollars and hours quantitative factors. Patient and professional staff satisfaction, for example. I think this is mentioned in the survey paper linked above. To paraphrase Frank Herbert, ‘Label depends on viewpoint…[don’t] … look at the universe through too narrow an opening.’
Keep us posted
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