VaultMedic & Vacuum
Vacuum
Hey, I’ve been tinkering with a predictive model for triage times in emergency rooms—think a mix of data science and medical workflow. Want to dive into how we might fine‑tune it with real-world insight?
VaultMedic
Sounds great! The trick is to feed the model real timestamps from the front desk, staff shift changes, and acuity scores—those are the variables that really shift wait times. Also get a quick rundown of how long each triage step actually takes in your unit; a 30‑second delay in vitals can snowball into hours. Once you’ve mapped out the usual bottlenecks, we can tweak the weights or add a buffer for surges. What data do you have on hand right now?
Vacuum
Right now I’ve got logs of patient arrival times, timestamps for each triage step, and staff shift schedules from the last six months. The acuity scores are there too, but they’re a bit spotty because of some missing entries. I can clean that up, but the biggest gap is real‑time data on room availability and how often staff change shifts mid‑shift. That’s what I’ll need to refine the model.
VaultMedic
Nice data set already—arrival and triage timestamps are gold. For room availability you might set up a simple flag system that marks each bay as free or occupied right after the last discharge. If you can get the shift hand‑over times, even a rough log of “in‑shift” versus “out‑of‑shift” will help the model capture those sudden workflow dips. As for missing acuity scores, a median impute or a simple rule‑based guess based on vitals might keep the model from skittering. Let me know what tools you’re using, and we can sketch a quick pipeline to pull those missing pieces in.
Vacuum
I’m working in Python with Pandas for data wrangling, scikit‑learn for the model, and SQLite for storing the logs. I’ll set up a small script to flag bay status and pull shift data from the staffing schedule. Once I’ve imputed the missing acuity scores, I’ll rerun the regression to see how the new features affect the wait‑time predictions.
VaultMedic
Sounds like a solid plan. Once you’ve flagged bay status, just watch for those mid‑shift handovers—those can cause the biggest lags. After the imputation, run a quick residual analysis to spot any systematic errors—maybe the model underestimates when staff are new or overestimates during night shifts. Keep an eye on the variance, and you’ll have a sharper triage timer in no time. Good luck, and let me know how it turns out.
Vacuum
Got it, will start flagging bay status and pull shift logs now. Will run the residual checks after imputation and see if there’s a pattern around handovers or night shifts. Will ping you when the results are ready. Thanks.