 A systematic review of the prediction of hospital length of stay: Towards a unified frameworkKieran Stone, Reyer Zwiggelaar, Phil Jones and Neil Mac Parthaláin PLOS Digital Health, 2022, vol. 1, issue 4, 1-38 Abstract: Hospital length of stay of patients is a crucial factor for the effective planning and management of hospital resources. There is considerable interest in predicting the LoS of patients in order to improve patient care, control hospital costs and increase service efficiency. This paper presents an extensive review of the literature, examining the approaches employed for the prediction of LoS in terms of their merits and shortcomings. In order to address some of these problems, a unified framework is proposed to better generalise the approaches that are being used to predict length of stay. This includes the investigation of the types of routinely collected data used in the problem as well as recommendations to ensure robust and meaningful knowledge modelling. This unified common framework enables the direct comparison of results between length of stay prediction approaches and will ensure that such approaches can be used across several hospital environments. A literature search was conducted in PubMed, Google Scholar and Web of Science from 1970 until 2019 to identify LoS surveys which review the literature. 32 Surveys were identified, from these 32 surveys, 220 papers were manually identified to be relevant to LoS prediction. After removing duplicates, and exploring the reference list of studies included for review, 93 studies remained. Despite the continuing efforts to predict and reduce the LoS of patients, current research in this domain remains ad-hoc; as such, the model tuning and data preprocessing steps are too specific and result in a large proportion of the current prediction mechanisms being restricted to the hospital that they were employed in. Adopting a unified framework for the prediction of LoS could yield a more reliable estimate of the LoS as a unified framework enables the direct comparison of length of stay methods. Additional research is also required to explore novel methods such as fuzzy systems which could build upon the success of current models as well as further exploration of black-box approaches and model interpretability.Author summary: Hospital length of stay (LoS) is the number of days that an in-patient will remain in hospital. LoS has long been used as a measure for hospitals so that they can better improve patient care, reduce overall costs, and appropriately allocate resources according to staff and patient needs. It can also give an indication of hospital care unit efficiency and patient flow. There is of course much considerable variability There is considerable variability amongst patient LoS for different patient diagnoses. The LoS for the same diagnosis may vary from 2 to 50+ days between patients. This variation can be due to several factors such as a patient’s characteristics, social circumstances, or treatment complexity. This paper explores the makeshift nature of the current LoS prediction approaches and highlights the need for a unified framework to be adopted which could yield a more reliable estimate of LoS. This framework would enable the performance of several LoS prediction approaches to be directly compared and could be used across several hospital environments. Expanding the influence of these models that are generated as part of a unified framework would ensure that the prediction approaches in place are suitably robust. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000017 DOI: 10.1371/journal.pdig.0000017 Access Statistics for this article More articles in PLOS Digital Health from Public Library of Science |
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