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Machine Learning Based Forecast for the Prediction of Inpatient Bed Demand by Manuel Tello; Eric S. Reich; Jason Puckey; Rebecca Maff; Andres Garcia-Arce; Biplab Sudhin Bhattacharya; Felipe Feijoo is a Health Professions article available to read on EtoBox.

What is Machine Learning Based Forecast for the Prediction of Inpatient Bed Demand about?

## Abstract ## Background Overcrowding is a serious problem that impacts the ability to provide optimal level of care in a timely manner. High patient volume is known to increase the boarding time at the emergency department (ED), as well as at post-anesthesia care unit (PACU). Furthermore, the same high volume increases inpatient bed transfer times, which causes delays in elective surgeries, increases the probability of near misses, patient safety incidents, and adverse events. ## Objective The purpose of this study is to develop a Machine Learning (ML) based strategy to predict weekly forecasts of the inpatient bed demand in order to assist the resource planning for the ED and PACU, resulting in a more efficient utilization. ## Methods The data utilized included all adult inpatient encounters at Geisinger Medical Center (GMC) for the last 5 years. The variables considered were class of inpatient encounter, observation, or surgical overnight recovery (SORU) at the time of their discharge. The ML based strategy is built using the K-means clustering method and the Support Vector Machine Regression technique (K-SVR). ## Results The performance obtained by the K-SVR strategy in the re

Who reads Machine Learning Based Forecast for the Prediction of Inpatient Bed Demand?

Author

Manuel Tello; Eric S. Reich; Jason Puckey; Rebecca Maff; Andres Garcia-Arce; Biplab Sudhin Bhattacharya; Felipe Feijoo

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Health Professions (Health Sciences)