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Study objective To create a preoperative predictive model for prolonged post-anaesthesia care unit (PACU) stay for outpatient surgery and compare with an existing (University of California-San Diego, UCSD) model. Design Retrospective observational study. Setting Post-anaesthesia care unit. Patients: Outpatient surgical patients discharged on the same day in a large academic institution. Preoperative data were collected. The study period was three months in 2016. Measurements: Prolonged PACU stay defined as a length of stay longer than the third quartile. We utilized multivariate regression analyses and bootstrapping statistical techniques to create a predictive model for prolonged PACU stay. Main results: Four strong predictors for prolonged PACU stay: general anaesthesia, obstructive sleep apnoea, surgical specialty and scheduled case duration. Our model had an excellent discrimination performance and a good calibration. Conclusion We developed a predictive model for prolonged PACU stay in our institution. This model is different from the UCSD model probably secondary to local and regional differences in outpatient surgery practice. Therefore, individual practice study outcomes may not apply to other practices without careful consideration of these differences.

Even the most innovative healthcare technologies provide patient benefits only when adopted by clinicians and/or patients in actual practice. Yet realizing optimal positive impact from a new technology for the widest range of individuals who would benefit remains elusive. In software and new product development, iterative rapid-cycle “agile” methods more rapidly provide value, mitigate failure risks, and adapt to customer feedback. Co-development between builders and customers is a key agile principle. But how does one accomplish co-development with busy clinicians? In this paper, we discuss four practical agile co-development practices found helpful clinically: (1) User stories for lightweight requirements; (2) Time-boxed development for collaborative design and prompt course correction; (3) Automated acceptance test driven development, with clinician-vetted specifications; and (4) Monitoring of clinician interactions after release, for rapid-cycle product adaptation and evolution. In the coming wave of innovation in healthcare apps ushered in by open APIs to EHRs, learning rapidly what new product features work well for clinicians and patients will become even more crucial.

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