An observational, retrospective study of the length of stay, and its influencing factors, among elderly patients at the Emergency Department

Golüke, Nienke M.S.; Huibers, Corlina Johanna Alida; Stalpers, S.C.; Taekema, Diana G.; Vermeer, Sarah E.; Jansen, P.A.F.

doi:10.1016/j.eurger.2015.04.005

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…Marshall et al uses conditional phase-type distribution to model the LOS of elderly patients in hospital. 7,[78][79][80] The approach illustrates data on hospital processes for a number of geriatric patients along with personal details, admissions reasons, dependency levels and destination (the causal network). The final model represents patient LOS in terms of five of the most significant patient variables in the dataset, namely patient age, gender, admission method into hospital, Barthel grade (dependency score) and destination on departure from hospital.…”

Section: Markov Model and Phase-type Distributionsmentioning

confidence: 99%

Patient length of stay and mortality prediction: A survey

Awad

Bader-El-Den

McNicholas

2017

Health Serv Manage Res

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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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Section: Markov Model and Phase-type Distributionsmentioning

confidence: 99%

Patient length of stay and mortality prediction: A survey

Awad

Bader-El-Den

McNicholas

2017

Health Serv Manage Res

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“…[32] Our study found there was an overall decrease in ED LOS for stroke presentations over the five year period to a median of 215 minutes for haemorrhagic stroke and 238 minutes for ischaemic stroke. Comparable to this, Goluke et al [33] reported a median ED LOS of 243 minutes for elderly patients but significantly shorter LOS with neurology admissions. Over the five years in our study, compliance with the proportion of patients discharged from the ED within four hours increased with around 55% of patients having a LOS in ED less than four hours in 2014-15.…”

Section: Discussionmentioning

confidence: 87%

Evaluation of adult stroke presentations at an Emergency Department in Queensland Australia

Bernaitis

Anoopkumar‐Dukie

Bills

et al. 2019

International Emergency Nursing

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Background:Stroke is a leading cause of mortality and morbidity which places high demands on emergency departments(EDs). Currently there is limited data on stroke presentations to Australian EDs and the time performance management of these presentations. Therefore, the aim of this study was to evaluate stroke presentations at an ED in Queensland, Australia in terms of demographics and time performance measures over a five year period.Methods:Retrospective analysis of ED presentations by patients≥18 years with a final diagnosis of stroke between 1 July 2010 and 30 June 2015.Results:Over the five years there was a 51.4% increase in presentations diagnosed with stroke. The majority of these patients arrived by ambulance(71.0%) and were admitted(94.9%) with death in ED for 1.4% of presentations. From 2010 to 2015 for both haemorrhagic and ischaemic stroke there was a significant decrease in median LOS in ED(435 to 215 mins,p<0.05 and 451 to 238 mins,p<0.001 respectively) and in the proportion of patients in ED greater than four hours(82.4% to 44%,p<0.05 and 92.4% to 45.8%,p<0.0001 respectively). Conclusion:Despite increased presentations of stroke, the ED improved in multiple time performance measures. Improving time-based targets in ED is particularly important for stroke presentations given the time critical nature of stroke management. An observational study was conducted at a public teaching hospital in South-East Queensland. The ED recorded 91902 total ED presentations in 2014-5[11]. Human Research Ethics Committee approval was obtained from the local health service and Griffith University (HREC/14/QPAH/445 and GU 2015/863) Data source Data were extracted by a member of the hospital Health Informatics Division. This pertained to routinely collected data from the hospital information systems including Emergency Department Information System (EDIS), Hospital Based Corporate Information System (HBCIS) and the PPM/TII database. Data extracted included demographics, i.e. date of birth and gender, and clinical information, i.e. arrival date and time, mode of arrival, triage priority, date and time seen by treating doctor, departure date and time, departure status, and ED length of stay (LOS) with times represented in minutes.

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“…ED is served as the gateway to admission and alternative choice for outpatient care. Acknowledge these facts, as well as the reality that organizational factors, especially access block, rather than ED treatment delay, are more vital in reducing EDLOS [4,16,20,21]. Possible solutions include set-up a well-established primary care system [22], a highly integrated health system can effectively organize primary and high-level care units, ensure the continuity of care and redirect non-urgent selfreferrals for ED Secondly, a multidisciplinary task group for speci c complaints suchlike stroke, myocardial infarction, and trauma has also proven effective in reducing EDLOS and improving clinical outcomes [23][24][25][26].…”

Section: Discussionmentioning

confidence: 99%

“…Although no direct data has been released, experience from the US showed that a demographic transition like this could trigger a sharp increase in demand for medical services, especially for acute care [2,3]. The aging population has a higher prevalence rate of chronic disease, long-term medication, atypical symptoms, and multimorbidity, making them more likely to suffer from prolonged Emergency Department Length Of Stay (EDLOS) and higher admission rates [4,5].…”

Section: Introductionmentioning

confidence: 99%

Elderly emergency department boarders’length of stay and risk factors

Wang

Liu

et al. 2023

Preprint

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Study objective: The distribution of Emergency Department Length Of Stay (EDLOS) for young and old emergency department (ED) boarders and its risk factors remain ill-defined, and we attempt to sort it out using organizational data linked at the individual level from a tertiary public teaching hospital at Lanzhou, China. Methods: A retrospective cohort study of ED admitted patients between 1 January 2016 to 31 December 2018 was conducted. The primary outcome was the distribution of EDLOS between the elder (≥65 years) and the younger (17-64 years) and the rate for prolonged EDLOS, defined as≥75th percentile of EDLOS. Secondary outcomes were the possible risk factors. Using Oaxaca-Blinder decomposition to decompose the prolonged EDLOS rate gap between two age groups. Results: A total of 28562 ED presentations were included in the study, and the mean age was 50.92 years (S.D.±17.52). The mean EDLOS for the younger group was 11.5h(S.D.±18) and 13.18h(S.D.±20.82）for the elderly patients. The two age groups differed significantly in prolonged EDLOS rate (P<0.001). 29.13% of elderly patients and 11.5% of younger patients suffered from prolonged EDLOS. The result of multivariable regression suggests excessive medication, MRI inspection, and consultation by specialists from internal medicine or emergency from the inpatient department contribute the most. Results of Blinder-Oaxaca decomposition suggested that characteristic differences can explain nearly two-thirds of the prolonged EDLOS gap between the two age groups, in which organizational differences were the major contributor. Conclusion: Elderly patients had significantly longer EDLOS than their younger counterparts, and organizational factors played a dominant role, which may be overcome through organizational reform.

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An observational, retrospective study of the length of stay, and its influencing factors, among elderly patients at the Emergency Department

Cited by 4 publications

References 13 publications

Patient length of stay and mortality prediction: A survey

Patient length of stay and mortality prediction: A survey

Evaluation of adult stroke presentations at an Emergency Department in Queensland Australia

Elderly emergency department boarders’length of stay and risk factors

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