Inpatient length of stay (LoS) is an important managerial metric which if known in advance can be used to efficiently plan admissions, allocate resources and improve patient care. Using historical patient data and machine learning techniques, LoS prediction models can be developed. Ethically, these models can not be used for patient discharge in lieu of unit heads but are of utmost necessity for hospital management systems for effective hospital planning. Thus, the design of the prediction system should be adapted to a true hospital setting.In this study, we predict early hospital LoS at the granular level of admission units by applying transfer learning to leverage information learned from a potential source domain. Time-varying data from 110,079 and 60,492 patient admissions to 8 and 9 ICU units were respectively extracted from eICU and MIMIC-IV databases. These were fed into a Long-Short Term Memory and a Fully connected network to train a source domain model, which weights were transferred either partially or fully to initiate training in the target domains. Shapley Additive exPlanations (SHAP) algorithms were used to study the effect of weight transfer on model explanability. Compared to the two benchmark models, the proposed weight transfer model showed statistically significant gains in prediction accuracy (between 1% and 5%) as well as computation time (up to 2hrs) for some of the target domains.The proposed method thus provides an optimal clinical decision support system for hospital management that can ease processes of data access via ethical committee, computation infrastructures and time.