A comparative evaluation of time-series models for forecasting inpatient deaths and discharges against medical advice.

Forecasting inpatient mortality (IM) and discharges against medical advice (DAMA) provides essential insights for healthcare quality monitoring and hospital management. This study compared six time-series forecasting methods-ARIMA, Grey Model, NNETAR, LSTM, Prophet, and Chronos, a pretrained probabilistic model-to predict monthly IM and DAMA in two tertiary hospitals in China from January 2018 to December 2024. Model performance was evaluated using RMSE, MAE, MAPE. Chronos demonstrated the best predictive accuracy for IM across both hospitals, achieving the lowest MAPE values (26.96-33.37%) and outperforming traditional and deep learning approaches (Diebold-Mariano test, p < 0.05). For DAMA forecasting, Chronos performed optimally (MAPE = 5.52%) in the hospital with higher and more stable DAMA volumes, whereas NNETAR yielded relatively superior results (MAPE = 11.29%) in the hospital with smaller and more irregular time series. LSTM consistently showed limited generalizability, likely due to small sample sizes and model complexity. These findings indicate that pretrained models such as Chronos can deliver robust and scalable forecasting performance even with limited data, while simpler neural networks like NNETAR may better handle low-volume, noisy data. Implementing these models in hospital management systems could enhance the timeliness and precision of quality monitoring, enabling proactive responses to adverse clinical and operational trends.