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Stone K, Zwiggelaar R, Jones P, Mac Parthaláin N. A systematic review of the prediction of hospital length of stay: Towards a unified framework. PLOS DIGITAL HEALTH 2022; 1:e0000017. [PMID: 36812502 PMCID: PMC9931263 DOI: 10.1371/journal.pdig.0000017] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 01/06/2022] [Indexed: 05/09/2023]
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.
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Affiliation(s)
- Kieran Stone
- Department of Computer Science, Aberystwyth University, Ceredigion, SY23 3DB, Wales, United Kingdom
| | - Reyer Zwiggelaar
- Department of Computer Science, Aberystwyth University, Ceredigion, SY23 3DB, Wales, United Kingdom
| | - Phil Jones
- Bronglais District General Hospital, Aberystwyth, Ceredigion, SY23 1ER, Wales, United Kingdom
| | - Neil Mac Parthaláin
- Department of Computer Science, Aberystwyth University, Ceredigion, SY23 3DB, Wales, United Kingdom
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Lequertier V, Wang T, Fondrevelle J, Augusto V, Duclos A. Hospital Length of Stay Prediction Methods: A Systematic Review. Med Care 2021; 59:929-938. [PMID: 34310455 DOI: 10.1097/mlr.0000000000001596] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE This systematic review sought to establish a picture of length of stay (LOS) prediction methods based on available hospital data and study protocols designed to measure their performance. MATERIALS AND METHODS An English literature search was done relative to hospital LOS prediction from 1972 to September 2019 according to the PRISMA guidelines. Articles were retrieved from PubMed, ScienceDirect, and arXiv databases. Information were extracted from the included papers according to a standardized assessment of population setting and study sample, data sources and input variables, LOS prediction methods, validation study design, and performance evaluation metrics. RESULTS Among 74 selected articles, 98.6% (73/74) used patients' data to predict LOS; 27.0% (20/74) used temporal data; and 21.6% (16/74) used the data about hospitals. Overall, regressions were the most popular prediction methods (64.9%, 48/74), followed by machine learning (20.3%, 15/74) and deep learning (17.6%, 13/74). Regarding validation design, 35.1% (26/74) did not use a test set, whereas 47.3% (35/74) used a separate test set, and 17.6% (13/74) used cross-validation. The most used performance metrics were R2 (47.3%, 35/74), mean squared (or absolute) error (24.4%, 18/74), and the accuracy (14.9%, 11/74). Over the last decade, machine learning and deep learning methods became more popular (P=0.016), and test sets and cross-validation got more and more used (P=0.014). CONCLUSIONS Methods to predict LOS are more and more elaborate and the assessment of their validity is increasingly rigorous. Reducing heterogeneity in how these methods are used and reported is key to transparency on their performance.
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Affiliation(s)
- Vincent Lequertier
- Research on Healthcare Performance (RESHAPE), Université Claude Bernard Lyon 1, INSERM U1290
- Health Data Department, Lyon University Hospital, Lyon
- Univ Lyon, INSA Lyon, Université Claude Bernard Lyon 1, Univ Lumière Lyon 2, DISP, EA4570, 69621 Villeurbanne, France
| | - Tao Wang
- University of Lyon, INSA Lyon, Université Claude Bernard Lyon 1, Univ Lumière Lyon 2, UJM-Saint-Etienne, Decision and Information Systems for Production systems (DISP), Villeurbanne Cedex
| | - Julien Fondrevelle
- Univ Lyon, INSA Lyon, Université Claude Bernard Lyon 1, Univ Lumière Lyon 2, DISP, EA4570, 69621 Villeurbanne, France
| | - Vincent Augusto
- Mines Saint-Etienne, University of Clermont Auvergne, CNRS, UMR 6158 LIMOS, Centre CIS, Saint-Etienne, France
| | - Antoine Duclos
- Research on Healthcare Performance (RESHAPE), Université Claude Bernard Lyon 1, INSERM U1290
- Health Data Department, Lyon University Hospital, Lyon
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Awad A, Bader–El–Den M, McNicholas J. Patient length of stay and mortality prediction: A survey. Health Serv Manage Res 2017; 30:105-120. [DOI: 10.1177/0951484817696212] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses and healthcare resource utilisation. This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.
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Affiliation(s)
- Aya Awad
- School of Computing, University of Portsmouth, UK
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Turgeman L, May J, Ketterer A, Sciulli R, Vargas D. Identification of readmission risk factors by analyzing the hospital-related state transitions of congestive heart failure (CHF) patients. ACTA ACUST UNITED AC 2015. [DOI: 10.1080/19488300.2015.1095823] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Belciug S, Gorunescu F. Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation. J Biomed Inform 2014; 53:261-9. [PMID: 25433363 DOI: 10.1016/j.jbi.2014.11.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 10/21/2014] [Accepted: 11/18/2014] [Indexed: 10/24/2022]
Abstract
Scarce healthcare resources require carefully made policies ensuring optimal bed allocation, quality healthcare service, and adequate financial support. This paper proposes a complex analysis of the resource allocation in a hospital department by integrating in the same framework a queuing system, a compartmental model, and an evolutionary-based optimization. The queuing system shapes the flow of patients through the hospital, the compartmental model offers a feasible structure of the hospital department in accordance to the queuing characteristics, and the evolutionary paradigm provides the means to optimize the bed-occupancy management and the resource utilization using a genetic algorithm approach. The paper also focuses on a "What-if analysis" providing a flexible tool to explore the effects on the outcomes of the queuing system and resource utilization through systematic changes in the input parameters. The methodology was illustrated using a simulation based on real data collected from a geriatric department of a hospital from London, UK. In addition, the paper explores the possibility of adapting the methodology to different medical departments (surgery, stroke, and mental illness). Moreover, the paper also focuses on the practical use of the model from the healthcare point of view, by presenting a simulated application.
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A psychological approach to learning causal networks. Health Care Manag Sci 2013; 17:194-201. [PMID: 24048957 DOI: 10.1007/s10729-013-9250-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2013] [Accepted: 08/05/2013] [Indexed: 10/26/2022]
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Fenton N, Neil M. Comparing risks of alternative medical diagnosis using Bayesian arguments. J Biomed Inform 2010; 43:485-95. [PMID: 20152931 DOI: 10.1016/j.jbi.2010.02.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2008] [Revised: 01/02/2010] [Accepted: 02/07/2010] [Indexed: 10/19/2022]
Abstract
This paper explains the role of Bayes Theorem and Bayesian networks arising in a medical negligence case brought by a patient who suffered a stroke as a result of an invasive diagnostic test. The claim of negligence was based on the premise that an alternative (non-invasive) test should have been used because it carried a lower risk. The case raises a number of general and widely applicable concerns about the decision-making process within the medical profession, including the ethics of informed consent, patient care liabilities when errors are made, and the research problem of focusing on 'true positives' while ignoring 'false positives'. An immediate concern is how best to present Bayesian arguments in such a way that they can be understood by people who would normally balk at mathematical equations. We feel it is possible to present purely visual representations of a non-trivial Bayesian argument in such a way that no mathematical knowledge or understanding is needed. The approach supports a wide range of alternative scenarios, makes all assumptions easily understandable and offers significant potential benefits to many areas of medical decision-making.
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Affiliation(s)
- Norman Fenton
- Queen Mary University of London, RADAR (Risk Assessment and Decision Analysis Research), School of Electronic Engineering and Computer Science, London E1 4NS, UK.
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Analysing the length of care episode after hip fracture: a nonparametric and a parametric Bayesian approach. Health Care Manag Sci 2009; 13:170-81. [DOI: 10.1007/s10729-009-9121-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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9
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Goodson J, Jang W, Rantz M. Nursing home care quality: insights from a Bayesian network approach. THE GERONTOLOGIST 2008; 48:338-48. [PMID: 18591359 DOI: 10.1093/geront/48.3.338] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
PURPOSE The purpose of this research is twofold. The first purpose is to utilize a new methodology (Bayesian networks) for aggregating various quality indicators to measure the overall quality of care in nursing homes. The second is to provide new insight into the relationships that exist among various measures of quality and how such measures affect the overall quality of nursing home care as measured by the Observable Indicators of Nursing Home Care Quality Instrument. In contrast to many methods used for the same purpose, our method yields both qualitative and quantitative insight into nursing home care quality. DESIGN AND METHODS We construct several Bayesian networks to study the influences among factors associated with the quality of nursing home care; we compare and measure their accuracy against other predictive models. RESULTS We find the best Bayesian network to perform better than other commonly used methods. We also identify key factors, including number of certified nurse assistant hours, prevalence of bedfast residents, and prevalence of daily physical restraints, that significantly affect the quality of nursing home care. Furthermore, the results of our analysis identify their probabilistic relationships. IMPLICATIONS The findings of this research indicate that nursing home care quality is most accurately represented through a mix of structural, process, and outcome measures of quality. We also observe that the factors affecting the quality of nursing home care collectively determine the overall quality. Hence, focusing on only key factors without addressing other related factors may not substantially improve the quality of nursing home care.
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Affiliation(s)
- Justin Goodson
- Department Managment Sciences, University of Iowa, Iowa City, Iowa, USA
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Liu P, El‐Darzi E, Lei L, Vasilakis C, Chountas P, Huang W. Applying data mining algorithms to inpatient dataset with missing values. JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT 2007. [DOI: 10.1108/17410390810842273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Abstract
Inpatient census, or occupancy, is a primary driver of resource use in hospitals. Fluctuations in occupancy complicate decisions related to staffing, bed management, ambulance diversions, and may ultimately impact both quality of patient care and nursing job satisfaction. We describe our approach in building a computerized model to provide short-term occupancy predictions for an entire hospital by nursing unit and shift. Our model is a comprehensive system built using real hospital data and utilizes statistical predictions at the individual patient level. We discuss the results of piloting an early version of the model at a mid-size community hospital. The primary focus of the paper is on the development and methodology of a second generation of the predictive occupancy model. The results and accuracy of this new model is compared to a variety of other predictive methods based on tests using 2 years of actual hospital data.
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Marshall A, Vasilakis C, El-Darzi E. Length of stay-based patient flow models: recent developments and future directions. Health Care Manag Sci 2005; 8:213-20. [PMID: 16134434 DOI: 10.1007/s10729-005-2012-z] [Citation(s) in RCA: 68] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Modelling patient flow in health care systems is vital in understanding the system activity and may therefore prove to be useful in improving their functionality. An extensively used measure is the average length of stay which, although easy to calculate and quantify, is not considered appropriate when the distribution is very long-tailed. In fact, simple deterministic models are generally considered inadequate because of the necessity for models to reflect the complex, variable, dynamic and multidimensional nature of the systems. This paper focuses on modelling length of stay and flow of patients. An overview of such modelling techniques is provided, with particular attention to their impact and suitability in managing a hospital service.
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Affiliation(s)
- Adele Marshall
- Department of Applied Mathematics and Theoretical Physics, David Bates Building, Queen 's University of Belfast, Belfast Northern Ireland, UK.
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An Analysis of Missing Data Treatment Methods and Their Application to Health Care Dataset. ADVANCED DATA MINING AND APPLICATIONS 2005. [DOI: 10.1007/11527503_69] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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Acid S, de Campos LM, Fernández-Luna JM, Rodríguez S, María Rodríguez J, Luis Salcedo J. A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service. Artif Intell Med 2004; 30:215-32. [PMID: 15081073 DOI: 10.1016/j.artmed.2003.11.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2002] [Revised: 11/10/2002] [Accepted: 06/23/2003] [Indexed: 10/26/2022]
Abstract
Due to the uncertainty of many of the factors that influence the performance of an emergency medical service, we propose using Bayesian networks to model this kind of system. We use different algorithms for learning Bayesian networks in order to build several models, from the hospital manager's point of view, and apply them to the specific case of the emergency service of a Spanish hospital. This first study of a real problem includes preliminary data processing, the experiments carried out, the comparison of the algorithms from different perspectives, and some potential uses of Bayesian networks for management problems in the health service.
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Affiliation(s)
- Silvia Acid
- Departamento de Ciencias de la Computación e I.A., Universidad de Granada, Escuela Técnica Superior de Ingeniería Informática, Avda. de Andalucía 38, Granada E-18071, Spain
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15
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Affiliation(s)
- Gary Harrison
- Department of Mathematics College of Charleston Charleston, South Carolina USA
| | - Simone Ivatts
- Geriatrics St. George's Hospital Medical School Visiting Professor, Health and Social Care Modelling Group University of Westminster
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Marshall AH, McClean SI, Shapcott CM, Millard PH. Modelling patient duration of stay to facilitate resource management of geriatric hospitals. Health Care Manag Sci 2002; 5:313-9. [PMID: 12437281 DOI: 10.1023/a:1020394525938] [Citation(s) in RCA: 36] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
A fundamental aspect of health care management is the effective allocation of resources. This is of particular importance in geriatric hospitals where elderly patients tend to have more complex needs. Hospital managers would benefit immensely if they had advance knowledge of patient duration of stay in hospital. Managers could assess the costs of patient care and make allowances for these in their budget. In this paper. we tackle this important problem via a model which predicts the duration of stay distribution of patients in hospital. The approach uses phase-type distributions conditioned on a Bayesian belief network.
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Affiliation(s)
- A H Marshall
- Department of Applied Mathematics and Theoretical Physics, Queen's University of Belfast, Northern Ireland.
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Affiliation(s)
| | - Peter Millard
- St. George's Hospital Medical School and Visiting Professor, Health and Social Care Modelling Group University of Westminster
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