Process Improvement Director, Quality Management Rolando A. Berrios
Rolando A. Berrios is an industrial and systems engineer with more than 20 years of experience in systems optimization and operational excellence applying methods from management science including applied statistics, econometrics, and operation research. The major focus of his work has been on achieving long-term impacts and results while building organization effectiveness and capability intended for business retention and expansion. These efforts involve the development of operation management systems and data analytics in a wide variety of industries, including healthcare, government, distribution, manufacturing, and telecommunications. He has also provided short-term technical assistance for Chemonics projects in Nigeria, Mozambique, Zimbabwe, and Cameroon. Rolando holds a Ph.D. in systems engineering from George Washington University, an M.S. in economics from University of Puerto Rico, and a B.S. in industrial and systems engineering from The Ohio State University.
by Rolando A. Berrios
Choice of a Short-Term Prediction Model for Patient Discharge Before Noon: A Walkthrough of Arima Model
This article, appearing in journal The Health Care Manager, presents a detailed process of a building model for forecasting patient discharge before noon applying the Box-Jenkins methodology using weekly historic data. . Accurately forecasting is of crucial importance to plan early discharge activities, influenced by the fluctuations in daily discharges process. The objective is to find an appropriate autoregressive integrated moving average (ARIMA) model for forecasting the rate of patients out by noon based on the lowest error in a statistical forecast by applying the mean absolute percentage error.
This article, appearing in Quality Management in Health Care Journal, explores how the uncertainty and ambiguity of not knowing how many patients will be discharged can affect patient throughput in hospitals, causing concerns for responding to demand for admissions. It argues that Box-Jenkins forecasting performance is superior in predicting DBN with the least forecast error. Predicted values are significant to decision-making interventions aimed at taking new patients, improving quality patient care, and meeting patient throughput performance goals.