Machine learning based forecast for the prediction of inpatient bed demand

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

Research output: Contribution to journalArticlepeer-review


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 retrospective cohort amounts to a mean absolute percentage error (MAPE) that ranges between 0.49 and 4.10% based on the test period. Additionally, results present a reduced variability, which translates into more stable forecasting results. CONCLUSIONS: The results from this study demonstrate the capacity of ML techniques to forecast inpatient bed demand, particularly using K-SVR. It is expected that the implementation of this model in the workflow of bed capacity management will create efficiencies, which will translate in a more reliable, inexpensive and timely care for patients.

Original languageEnglish
Pages (from-to)55
Number of pages1
JournalBMC medical informatics and decision making
Issue number1
StatePublished - 2 Mar 2022
Externally publishedYes


  • Census
  • K-SVR
  • Overcrowding
  • Support vector machine


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