Comparing artificial intelligence and clinical team discharge predictions to actual patient discharges in two neonatal intensive care units

Background: When patients are admitted to the hospital, clinical care teams must enter an expected discharge date (CT-EDD) into the electronic health record (EHR). This is often challenging for neonates admitted to the neonatal intensive care unit (NICU) due to extended lengths of stay. Pieces, a generative artificial intelligence (AI) program, uses EHR data and machine learning (ML) algorithms to predicted discharge dates (AI-PDD) for patients anticipated to discharge home in the next 5 days. Objective: To compare CT-EDD and AI-PDD for patients at a level 3 (L3) delivery NICU and level 4 (L4) referral NICU with actual discharge dates. Methods: Retrospective chart review from Sept 1, 2024 to December 31, 2024 including all NICU patients discharged home. Patients who were transferred or who died before discharge were excluded. The CT-EDD and AI-PDD were recorded starting with the date of discharge and up to six days prior, for up to 7 total days. Days with missing CT-EDD or AI-PDD were excluded. Differences between CT-EDD/AI-PDD and actual discharge date were evaluated. Results: At the L3 delivery NICU, the CT-EDD demonstrated better accuracy compared to the AI-PDD for each of the 6 days preceding discharge and the day of discharge (p<0.001). The CT-EDD performed better than the AI-PDD for 61.1% of total patient days and 69.4% of patients. Conversely, at the L4 referral NICU, the AI-PDD demonstrated better accuracy compared to the CT-EDD for each of the 6 days preceding discharge (p<0.001). The AI-PDD performed better than the CT-EDD for 61.8% of total patient days and 62.6% of patients. Conclusion: AI-PDD was more accurate than CT-EDD in a L4 referral NICU setting but underperformed in a L3 delivery NICU setting. Additional studies are warranted to evaluate factors that may contribute to these differences.