Real-time prediction of inpatient length of stay for discharge prioritization

Abstract

Objective: Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information.

Materials and methods: The authors use supervised machine learning methods to predict patients' likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden's Index (i.e., sensitivity + specificity - 1), and aggregate accuracy measures.

Results: The model compared to clinician predictions demonstrated significantly higher sensitivity (P < .01), lower specificity (P < .01), and a comparable Youden Index (P > .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge.

Conclusions: There is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.

Keywords: length of stay; machine learning; operational forecasting; patient flow.

Figure 1:

Classification tree for end-of-day discharge predictions.

Figure 2:

Variable importance summary from training regression random forest models for the 2 p.m. and end-of-day discharge predictions.

Figure 3:

Comparison of actual, model prediction, and clinician prediction distributions of the average number of patients discharged from the unit each day.

Figure 4:

Histogram summary of differences between model, clinician, and actual daily discharges.

Figure 5:

Histogram summary of daily Spearman’s rank correlation between remaining LOS and model prediction scores for 2 p.m. and the end of the day.