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Vasilakis, Christos; El‐Darzi, Elia

doi:10.1023/a:1009649615548

Cited by 50 publications

(6 citation statements)

References 6 publications

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“…This understanding is further expanded with the addition of external compartments such as independent home and support home to the basic model configuration [ 45 ]. The basic model was also configured to cater for a potential winter bed crisis scenario to attempt to determine the cause in English hospitals [ 46 ]. However, the application of these models in the real-world tends to be very operationally focused, resulting in a need for large volumes of data to be captured.…”

Section: Approaches To Modelling Losmentioning

confidence: 99%

A systematic review of the prediction of hospital length of stay: Towards a unified framework

et al. 2022

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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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Section: Approaches To Modelling Losmentioning

confidence: 99%

A systematic review of the prediction of hospital length of stay: Towards a unified framework

et al. 2022

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“…patients) 'compete' for limited resources such as hospital beds and operating room time [17]. Many simulation studies that have tackled questions around demand and capacity in healthcare, both under typical health system conditions (for example [18,19]) and in periods of increased pressure such as mass casualty events [20] and winter bed crises [21,22]. Specifically in the context of intensive care, simulation studies have addressed bed requirements by using the system dynamics simulation approach to evaluate different management policies [23], and applying analytical queuing models and simulations to the management of patient flow [24,25].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

Wood

McWilliams

Thomas

et al. 2020

Health Care Manag Sci

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Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these 'capacity-dependent' deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional 'capacity-independent' deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the

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“…Replacing traditional pager-oriented methods for digital alternatives based on web and mobile interfaces provides means to collect information on workload that has the power of being specific to each ward, speciality, working group or task-type. Also, quantifying demand variability over local facilities may allow for the study of 460 suspected relations with cyclic patterns of staff sickness, norovirus effects on wards, lack of social beds and traditional winter pressures ( [53,54]). …”

Section: Discussionmentioning

confidence: 99%

Out of hours workload management: Bayesian inference for decision support in secondary care

Perez

Brown

Pinchin

et al. 2016

Artificial Intelligence in Medicine

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Objective: In this paper, we aim to evaluate the use of electronic technologies in Out of Hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures.Methods and Material : We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data.Results: Main results support claims relating to (i) the importance of datadriven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation.Conclusions: The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Response to reviewersGeneral Comments: We are grateful to all reviewers for taking the time to review our paper and make a number of helpful comments that we believe have significantly improved the quality of the manuscript. All comments have been, according to their nature, merged, described and addressed here: 5Comment 1: The major concern shared by reviewers relates to a lack practical relevance of the results presented; for instance, managerial conclusions are not well exploited, the paper lacks a managerial perspective of the output from professionals involved in the study, or results do not extrapolate to different settings neither is it clear why they support the decision making. 10Response to Comment 1: The authors agree that the previous version of the manuscript offered a mostly descriptive study of the data and lacked relevant interpretations and conclusions drawn from results. In addition, we believe we were not successful in clearly conveying the aims and scope of our work; yet, we have made the efforts to undertake the alterations necessary in order to correct 15 these problems.Nationwide hospital settings are indeed subject to disparate workload pressures; these may drastically vary within distant geographical regions and in relation to specialities covered by each facility. The focus of our research team and this work relates to support systems for effective management that can 20 target local facilities and working groups. This is indeed a great problem, in that policy alterations and intelligent design of support systems (for instan...

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Untitled

Cited by 50 publications

References 6 publications

A systematic review of the prediction of hospital length of stay: Towards a unified framework

A systematic review of the prediction of hospital length of stay: Towards a unified framework

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

Out of hours workload management: Bayesian inference for decision support in secondary care

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