scholarly journals Using probabilistic patient flow modelling helps generate individualised intensive care unit operational predictions and improved understanding of current organisational behaviours

2019 ◽  
Vol 21 (3) ◽  
pp. 221-229
Author(s):  
George Hadjipavlou ◽  
Jill Titchell ◽  
Christina Heath ◽  
Richard Siviter ◽  
Hilary Madder
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Patient Flow ◽  
Optimal Strategies ◽  
Bed Occupancy ◽  
Optimal Resource ◽  
Unit Function ◽  
One Step ◽  
Organisational Behaviour ◽  
Patient Flow Modelling

Purpose We sought a bespoke, stochastic model for our specific, and complex ICU to understand its organisational behaviour and how best to focus our resources in order to optimise our intensive care unit’s function. Methods Using 12 months of ICU data from 2017, we simulated different referral rates to find the threshold between occupancy and failed admissions and unsafe days. We also modelled the outcomes of four change options. Results Ninety-two percent bed occupancy is our threshold between practical unit function and optimal resource use. All change options reduced occupancy, and less predictably unsafe days and failed admissions. They were ranked by magnitude and direction of change. Conclusions This approach goes one step further from past models by examining efficiency limits first, and then allowing change options to be quantitatively compared. The model can be adapted by any intensive care unit in order to predict optimal strategies for improving ICU efficiency.

Parameters of bed occupancy of intensive care units in Hungary

2011 ◽  
Vol 152 (24) ◽  
pp. 946-950 ◽  
Author(s):  
Miklós Gresz
Keyword(s):  
Health Care ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Health Insurance ◽  
Intensive Care Units ◽  
Emergency Care ◽  
Present Author ◽  
Bed Occupancy ◽  
National Network ◽  
Bed Capacity

According to the Semmelweis Plan for Saving Health Care, ”the capacity of the national network of intensive care units in Hungary is one but not the only bottleneck of emergency care at present”. Author shows on the basis of data reported to the health insurance that not on a single calendar day more than 75% of beds in intensive care units were occupied. There were about 15 to 20 thousand sick days which could be considered unnecessary because patients occupying these beds were discharged to their homes directly from the intensive care unit. The data indicate that on the whole bed capacity is not low, only in some institutions insufficient. Thus, in order to improve emergency care in Hungary, the rearrangement of existing beds, rather than an increase of bed capacity is needed. Orv. Hetil., 2011, 152, 946–950.

The impact of bed occupancy on patient flow in intensive care

2014 ◽  
Vol 27 (1) ◽  
pp. 60
Author(s):  
T. Williams ◽  
T. Swiney ◽  
M. Phillips ◽  
G. Leslie ◽  
S. Rankin ◽  
...  
Keyword(s):  
Intensive Care ◽  
Patient Flow ◽  
Bed Occupancy ◽  
The Impact

scholarly journals Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Joeri Ruyssinck ◽  
Joachim van der Herten ◽  
Rein Houthooft ◽  
Femke Ongenae ◽  
Ivo Couckuyt ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
University Hospital ◽  
Bed Occupancy ◽  
Time Period ◽  
Daunting Task ◽  
Failure Assessment ◽  
Random Survival Forests ◽  
Occupancy Prediction ◽  
Survival Forests

Predicting the bed occupancy of an intensive care unit (ICU) is a daunting task. The uncertainty associated with the prognosis of critically ill patients and the random arrival of new patients can lead to capacity problems and the need for reactive measures. In this paper, we work towards a predictive model based on Random Survival Forests which can assist physicians in estimating the bed occupancy. As input data, we make use of the Sequential Organ Failure Assessment (SOFA) score collected and calculated from 4098 patients at two ICU units of Ghent University Hospital over a time period of four years. We compare the performance of our system with a baseline performance and a standard Random Forest regression approach. Our results indicate that Random Survival Forests can effectively be used to assist in the occupancy prediction problem. Furthermore, we show that a group based approach, such as Random Survival Forests, performs better compared to a setting in which the length of stay of a patient is individually assessed.

A mathematical model for simulating daily bed occupancy in an intensive care unit*

2012 ◽  
Vol 40 (4) ◽  
pp. 1098-1104 ◽  
Author(s):  
Julio Barado ◽  
Juan María Guergué ◽  
Laida Esparza ◽  
Crisitina Azcárate ◽  
Fermín Mallor ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Bed Occupancy

Variability in Surgical Caseload and Access to Intensive Care Services

2003 ◽  
Vol 98 (6) ◽  
pp. 1491-1496 ◽  
Author(s):  
Michael L. McManus ◽  
Michael C. Long ◽  
Abbot Cooper ◽  
James Mandell ◽  
Donald M. Berwick ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Delivery System ◽  
Care Delivery ◽  
Patient Flow ◽  
Healthcare Delivery ◽  
Hospital Data ◽  
Healthcare Delivery System ◽  
Care Services

Background Variability in the demand for any service is a significant barrier to efficient distribution of limited resources. In health care, demand is often highly variable and access may be limited when peaks cannot be accommodated in a downsized care delivery system. Intensive care units may frequently present bottlenecks to patient flow, and saturation of these services limits a hospital's responsiveness to new emergencies. Methods Over a 1-yr period, information was collected prospectively on all requests for admission to the intensive care unit of a large, urban children's hospital. Data included the nature of each request, as well as each patient's final disposition. The daily variability of requests was then analyzed and related to the unit's ability to accommodate new admissions. Results Day-to-day demand for intensive care services was extremely variable. This variability was particularly high among patients undergoing scheduled surgical procedures, with variability of scheduled admissions exceeding that of emergencies. Peaks of demand were associated with diversion of patients both within the hospital (to off-service care sites) and to other institutions (ambulance diversions). Although emergency requests for admission outnumbered scheduled requests, diversion from the intensive care unit was better correlated with scheduled caseload (r = 0.542, P < 0.001) than with unscheduled volume (r = 0.255, P < 0.001). During the busiest periods, nearly 70% of all diversions were associated with variability in the scheduled caseload. Conclusions Variability in scheduled surgical caseload represents a potentially reducible source of stress on intensive care units in hospitals and throughout the healthcare delivery system generally. When uncontrolled, variability limits access to care and impairs overall responsiveness to emergencies.

scholarly journals Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units

2021 ◽  
Author(s):  
Stef Baas ◽  
Sander Dijkstra ◽  
Aleida Braaksma ◽  
Plom van Rooij ◽  
Fieke J. Snijders ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Length Of Stay ◽  
The Netherlands ◽  
Real Time ◽  
Data Warehouse ◽  
Intensive Care Units ◽  
Bed Occupancy ◽  
Control Centre

AbstractThis paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital’s data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital’s control centre and is used in several Dutch hospitals during the second COVID-19 peak.

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