Does length of hospital stay reflect power-law behavior? A q-Weibull density approach

Reyes-Santías, Francisco; Reboredo, Juan C.; Assis, Edilson Machado de; Castro, Miguel Angel Rivera

doi:10.1016/j.physa.2020.125618

Cited by 2 publications

(4 citation statements)

References 37 publications

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“…Improved forecasting of ED arrivals is a first step to implementing useful real-time management algorithms that offer solutions to complex ED management, in terms of both resource use and health implications for patients. Furthermore, better forecasting of ED arrivals is useful to predict hospital admissions and the impact of ED arrivals on bed utilization and length of stay [ 27 ]. However, a task we leave for future research is how ED arrivals and their forecast through INGARCH models could ultimately shape hospital admissions and bed utilization.…”

Section: Discussionmentioning

confidence: 99%

Forecasting emergency department arrivals using INGARCH models

Reboredo,

Barba-Queiruga,

Ojea-Ferreiro

et al. 2023

Health Econ Rev

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Background Forecasting patient arrivals to hospital emergency departments is critical to dealing with surges and to efficient planning, management and functioning of hospital emerency departments. Objective We explore whether past mean values and past observations are useful to forecast daily patient arrivals in an Emergency Department. Material and methods We examine whether an integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model can yield a better conditional distribution fit and forecast of patient arrivals by using past arrival information and taking into account the dynamics of the volatility of arrivals. Results We document that INGARCH models improve both in-sample and out-of-sample forecasts, particularly in the lower and upper quantiles of the distribution of arrivals. Conclusion Our results suggest that INGARCH modelling is a useful model for short-term and tactical emergency department planning, e.g., to assign rotas or locate staff for unexpected surges in patient arrivals.

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Section: Discussionmentioning

confidence: 99%

Forecasting emergency department arrivals using INGARCH models

Reboredo,

Barba-Queiruga,

Ojea-Ferreiro

et al. 2023

Health Econ Rev

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“…The Lotka's law, Pareto's principle and Price's law have been shown to approximate a wide variety of social phenomena both within (eg, length of hospital stay) and outwith healthcare (eg, population distributions), [1][2][3][4][5][6][7][8] but it is not a universal phenomenon. 5 6 This paper appears to demonstrate that monthly distribution of case volumes among spinal surgeons is another exception to these rules, and that the distribution among spinal units may be better approximated by alternatives (exponential decay in this case).…”

Section: Findings In Contextmentioning

confidence: 99%

“…It is also not clear whether these hold true within surgery or healthcare. For example, length of hospital stay and operating room turn-over time can be well modeled by a power law distribution 7 8. There have been some attempts to describe distribution of operations within surgery, particularly in the context of global health 9.…”

Section: Introductionmentioning

confidence: 99%

“…For example, length of hospital stay and operating room turn-over time can be well modeled by a power law distribution. 7 8 There have been some attempts to describe distribution of operations within surgery, particularly in the context of global health. 9 However, there are no mathematical equivalents such as Price’s law within the field surgery and case-volume distributions.…”

Section: Introductionmentioning

confidence: 99%

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Distribution of case volumes in surgery: an analysis of the British Spine Registry

Koh,

Muirhead,

Khan

et al. 2024

BMJ Surg Interv Health Technologies

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ObjectivesTo characterize the distribution of case volumes within a surgical field.DesignAn analysis of British Spine Registry.Setting295 centers in England that conducted at least one spinal operation either within the NHS or private settings between 1 May 2016 and 27 February 2021.Participants644 surgeons.Main outcome measuresMathematical descriptions of distributions of cases among surgeons and the extent of workforce-level case-volume concentration as a surrogate marker.ResultsThere were wide variations in monthly caseloads between surgeons, ranging from 0 to average monthly high of 81.8 cases. The curves showed that 37.7% of surgeons were required to perform 80% of all spinal operations, which is substantially less than in fields outside of healthcare.With the COVID-19 pandemic, the case volumes of surgeons with the highest volumes dropped dramatically, whereas those with the lowest case numbers remained nearly unchanged. This, along with the relatively low level of case-volume concentration within spinal surgery, may indicate an inevitability of at least some level of surgical care being provided by the relatively lower volume surgeons.ConclusionsWhile there is a reasonable degree of workforce-level case volume concentration within spinal surgery, with high volume spinal surgeons providing a large proportion of care, it is not clear whether a further concentration of case volumes into those few hands is possible or desirable.

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Does length of hospital stay reflect power-law behavior? A q-Weibull density approach

Cited by 2 publications

References 37 publications

Forecasting emergency department arrivals using INGARCH models

Forecasting emergency department arrivals using INGARCH models

Distribution of case volumes in surgery: an analysis of the British Spine Registry

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