Forecasting hospital daily occupancy using patient journey data: a heuristic approach

Shaowen Qin, Dale Ward

Research output: Contribution to conferencePaper

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Abstract

Hospitals are dynamic environments that involve many stochastic processes. Each day, some patients are discharged from hospital, emergency patients arrive and require admission, and a variable number of elective admissions are planned for the day. The ability to forecast hospital occupancy will assist hospital managers to balance the supply and demand on inpatient beds on a daily basis, which in turn will reduce the risk of hospital congestion. This study employed a heuristic approach to derive a forecasting model based on hospital patient journey data. Instead of using estimated overall length of stay (LOS) for each patient, the forecasting model relies on daily evaluation of the probabilities of staying or being discharged based on a patient’s current LOS. Patients’ characteristics are introduced as additional model parameters in an incremental manner to balance model complexity and prediction accuracy. It was found that a model without enough details can provide indications of overall trends in terms of the mean occupancy. However, more parameters, such as day of the week, must be considered in order to capture the extremes present in the data. Of course, as more parameters are introduced, less data become available for meaningful analysis. This proof-of-concept study provides a demonstration of a heuristic approach to determine how complex a model needs to be and what factors are important when forecasting hospital occupancy.
Original languageEnglish
Pages150-159
Number of pages10
DOIs
Publication statusPublished - 16 Nov 2018
EventThe 14th International Conference on Advanced Data Mining and Applications - Nanjing, China, Nanjing, China
Duration: 16 Nov 201818 Nov 2018

Conference

ConferenceThe 14th International Conference on Advanced Data Mining and Applications
Abbreviated titleADMA2018
CountryChina
CityNanjing
Period16/11/1818/11/18

Bibliographical note

© Springer Nature Switzerland AG 2018

Keywords

  • forecast model
  • hospital occupancy
  • probability of discharge
  • patient journey
  • Patient journey
  • Hospital occupancy
  • Forecast model
  • Probability of discharge

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    Qin, S., & Ward, D. (2018). Forecasting hospital daily occupancy using patient journey data: a heuristic approach. 150-159. Paper presented at The 14th International Conference on Advanced Data Mining and Applications, Nanjing, China. https://doi.org/10.1007/978-3-030-05090-0_13