Same-day discharge following minimally invasive hysterectomy has been shown to be safe and feasible in select populations, but many nonclinical factors influencing same-day discharge remain unexplored.

To develop prediction models for same-day discharge following minimally invasive hysterectomy using both clinical and nonclinical attributes and to compare model concordance of individual attribute groups.

We performed a retrospective study of patients who underwent elective minimally invasive hysterectomy for benign gynecologic indications at 69 hospitals in a statewide quality improvement collaborative between 2012 and 2019. Potential predictors of same-day discharge were determined a priori and placed into 1 of 7 attribute groupings: intraoperative, surgeon, hospital, surgical timing, patient clinical, patient socioeconomic, and patient geographic attributes. To account for clustering of same-day discharge practices among surgeons and within hospitals, hierarchical multivariable logistic regression models were fitted using predictors from each attribute group individually and all predictors in a composite model. Receiver operator characteristic curves were generated for each model. The Hanley–McNeil test was used for comparisons, 95% confidence intervals for the areas under the receiver operator characteristic curve were calculated, and a P value of <.05 was considered significant.

Of the 23,513 patients in our study, 5062 (21.5%) had same-day discharge. The composite model had an area under the receiver operator characteristic curve of 0.770 (95% confidence interval, 0.763–0.777). Among models using factors from individual attribute groups, the model using intraoperative attributes had the highest concordance for same-day discharge (area under the receiver operator characteristic curve, 0.720; 95% confidence interval, 0.712–0.727). The models using surgeon and hospital attributes were the second and third most concordant, respectively (area under the receiver operator characteristic curve, 0.678; 95% confidence interval, 0.670–0.685; area under the receiver operator characteristic curve, 0.655; 95% confidence interval, 0.656–0.664). Models using surgical timing and patient clinical, socioeconomic, and geographic attributes had poor predictive ability (all areas under the receiver operator characteristic curve <0.6).

Clinical and nonclinical attributes contributed to a composite prediction model with good discrimination in predicting same-day discharge following minimally invasive hysterectomy. Factors related to intraoperative, hospital, and surgeon attributes produced models with the strongest predictive ability. Focusing on these attributes may aid efforts to improve utilization of same-day discharge following minimally invasive hysterectomy.