A novel artificial intelligence framework to quantify the impact of clinical compared with nonclinical influences on...

The relative proportion of clinical compared with nonclinical influences on length of stay after colectomy has never been measured. We developed a novel machine-learning framework that quantifies the proportion of length of stay after colectomy attributable to clinical factors and infers the overall impact of nonclinical influences.

Patients who underwent partial colectomy, total colectomy, or low anterior resection included in American College of Surgeons National Surgical Quality Improvement were analyzed. Multivariable linear regression, random forest, and neural network models were developed to assess the impact of 56 clinical variables on length of stay. The random forest and neural network models were fine-tuned to maximize the explanatory power of clinical variables on length of stay. R² measured the proportion of length of stay explained by clinical factors. The contribution of nonclinical factors was inferred from residual analysis. Mean absolute error was used to measure the discrepancy between actual and model-predicted length of stay.

Of 96,081 patients, 71% underwent partial colectomy (mean length of stay, 6.8 days; standard deviation, 5.6), 27% low anterior resection (5.4; 4.4), and 2% total colectomy (11.8; 7.1). Clinical factors in multivariable linear regression models accounted for only 29-54% of length of stay variability. The random forest and neural network models demonstrated persistent unexplained length of stay variability even when considering nonlinear interactions (R²: random forest [range, 0.46-0.55]; neural network [range, 0.44-0.57]), consistent with multivariable linear regression models. Mean absolute error showed clinical factors could not account for 2-2.5 days of length of stay after low anterior resection and partial colectomy, and 4 days after total colectomy.

This is the first study to quantify the overall influence of clinical factors on post-colectomy length of stay, revealing they explain less than 55% of variability. By maximizing clinical factors' explanatory impact using machine learning, the remaining variability is inferred to be nonclinical. Our findings provide hospitals with a novel paradigm to indirectly measure the influence of previously elusive nonclinical factors.