Predicting elderly patient length of stay in hospital and community care using a series of conditional Coxian phase-type distributions, further conditioned on a survival tree

Gordon, Andrew S.; Marshall, A.H.; Zenga, M

doi:10.1007/s10729-017-9411-9

Cited by 13 publications

(11 citation statements)

References 17 publications

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“…These papers were particularly interesting as they focused on the cross over between community care and either single or multiple hospitals. Papers [18,42,43,51,56,62,63,75,86] focused on the intersection between community care and multiple hospitals whilst papers [23,27,28,31,32,37,69] focused on single hospitals and community care.…”

Section: • Multiple Hospitalsmentioning

confidence: 99%

“…• Forecasting -Predicting future scenarios with the current care plan in place, e.g., forecasting length of stay in hospital and community care [32],…”

Section: Research Aimsmentioning

confidence: 99%

“…Out of the 28 papers, 16 focused on predicting future demands and how the corresponding departments would be required to adapt to this change [4,9,12,15,18,20,33,34,37,43,45,49,57,62,83,84]. A further nine papers aimed to predict the length of stay of patients in hospitals or within community care [1,32,40,53,54,55,66,69,81]. Surprisingly, of these nine, one author was co-author on five of these papers, suggesting that the research within this field is limited to a few research teams [32,53,54,55,66].…”

Section: Research Aimsmentioning

confidence: 99%

“…A further nine papers aimed to predict the length of stay of patients in hospitals or within community care [1,32,40,53,54,55,66,69,81]. Surprisingly, of these nine, one author was co-author on five of these papers, suggesting that the research within this field is limited to a few research teams [32,53,54,55,66].…”

Section: Research Aimsmentioning

confidence: 99%

See 3 more Smart Citations

A survey of OR/MS models on care planning for frail and elderly patients

Williams

Gärtner

Harper

2021

Operations Research for Health Care

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Section: • Multiple Hospitalsmentioning

confidence: 99%

“…• Forecasting -Predicting future scenarios with the current care plan in place, e.g., forecasting length of stay in hospital and community care [32],…”

Section: Research Aimsmentioning

confidence: 99%

Section: Research Aimsmentioning

confidence: 99%

Section: Research Aimsmentioning

confidence: 99%

See 2 more Smart Citations

A survey of OR/MS models on care planning for frail and elderly patients

Williams

Gärtner

Harper

2021

Operations Research for Health Care

View full text Add to dashboard Cite

“…According to their results, there is evidence that the proposed system dynamic methodologies can support healthcare management in analyzing both different care pathways and delayed discharges of patients, optimizing their LOS. Finally, concentrating on elderly patients treated by the Regional Healthcare System of Italy’s Abruzzo Region, Gordon and colleagues introduce a new methodology, based on a series of conditional Coxian phase-type distributions, that clusters patients according to their covariates (i.e., age, gender, and admission method) and LOS in hospital and then models patient pathways, making it possible to predict their LOS in hospital and community care [ 15 ]. This approach can shed new light on the rates at which patients move between healthcare suppliers (i.e., hospital and community care), supporting managers in reducing the negative effects of bed occupancy and of the premature discharge of patients without a suitable period of convalescence.…”

Section: Introductionmentioning

confidence: 99%

Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds

Ippoliti

Falavigna

Zanelli

et al. 2021

Cost Eff Resour Alloc

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Background The problem of correct inpatient scheduling is extremely significant for healthcare management. Extended length of stay can have negative effects on the supply of healthcare treatments, reducing patient accessibility and creating missed opportunities to increase hospital revenues by means of other treatments and additional hospitalizations. Methods Adopting available national reference values and focusing on a Department of Internal and Emergency Medicine located in the North-West of Italy, this work assesses prediction models of hospitalizations with length of stay longer than the selected benchmarks and thresholds. The prediction models investigated in this case study are based on Artificial Neural Networks and examine risk factors for prolonged hospitalizations in 2018. With respect current alternative approaches (e.g., logistic models), Artificial Neural Networks give the opportunity to identify whether the model will maximize specificity or sensitivity. Results Our sample includes administrative data extracted from the hospital database, collecting information on more than 16,000 hospitalizations between January 2018 and December 2019. Considering the overall department in 2018, 40% of the hospitalizations lasted more than the national average, and almost 3.74% were outliers (i.e., they lasted more than the threshold). According to our results, the adoption of the prediction models in 2019 could reduce the average length of stay by up to 2 days, guaranteeing more than 2000 additional hospitalizations in a year. Conclusions The proposed models might represent an effective tool for administrators and medical professionals to predict the outcome of hospital admission and design interventions to improve hospital efficiency and effectiveness.

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Identification and prioritization of indicators of hospital bed allocation in Iran

Najibi

Lotfi

Kharazmi

et al. 2022

World Med & Health Policy

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The distribution of hospital beds can be used as an indicator to assess the distribution of health services. This study aimed to determine and prioritize indicators of hospital bed allocation in Iran. In the first phase of the study, we used a scoping review to identify criteria affecting hospital bed allocation. Then, we applied an expert panel and the Delphi method to localize the criteria. Finally, we carried out Analytic Network Process method based on Decision Making Trial and Evaluation Laboratory technique (DANP), to determine the weight and importance of indicators and to evaluate the causal relationship between them. Based on the scoping review, we identified 75 criteria that influence bed allocation. In 2 Delphi cycles, 28 criteria were selected and included in the DANP phase. The population, the presence of a reference hospital in the region, the type of hospital, the common diseases in the region, and the presence of home care services were the variables that had the most interaction with other variables. The variable of home care services was at the highest level of the causal chain. Bed occupancy rate, average length of stay, waiting list, as well as type and level of the hospital had the highest priority in the allocation of hospital beds. Furthermore, given the importance of home care services, policies should be adopted to expand these services nationwide. The indicators identified in this study will be useful for the development of policies aimed at an equitable allocation of hospital beds.

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Predicting elderly patient length of stay in hospital and community care using a series of conditional Coxian phase-type distributions, further conditioned on a survival tree

Cited by 13 publications

References 17 publications

A survey of OR/MS models on care planning for frail and elderly patients

A survey of OR/MS models on care planning for frail and elderly patients

Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds

Identification and prioritization of indicators of hospital bed allocation in Iran

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