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

ABSTRACT: 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.

SUBMITTER: Hadjipavlou G

PROVIDER: S-EPMC7401432 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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

Hadjipavlou George G Titchell Jill J Heath Christina C Siviter Richard R Madder Hilary H

Journal of the Intensive Care Society 20190905 3

<h4>Purpose</h4>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.<h4>Methods</h4>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.<h4>Results</h4>Ninety-two percent bed occupancy is ...[more]

PMID: 32782461

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Project description:A crucial ability of the human brain is to learn and exploit probabilistic associations between stimuli to facilitate perception and behavior by predicting future events. Although studies have shown how perceptual relationships are used to predict sensory inputs, relational knowledge is often between concepts rather than percepts (e.g., we learned to associate cats with dogs, rather than specific images of cats and dogs). Here, we asked if and how sensory responses to visual input may be modulated by predictions derived from conceptual associations. To this end we exposed participants of both sexes to arbitrary word-word pairs (e.g., car-dog) repeatedly, creating an expectation of the second word, conditional on the occurrence of the first. In a subsequent session, we exposed participants to novel word-picture pairs, while measuring fMRI BOLD responses. All word-picture pairs were equally likely, but half of the pairs conformed to the previously formed conceptual (word-word) associations, whereas the other half violated this association. Results showed suppressed sensory responses throughout the ventral visual stream, including early visual cortex, to pictures that corresponded to the previously expected words compared with unexpected words. This suggests that the learned conceptual associations were used to generate sensory predictions that modulated processing of the picture stimuli. Moreover, these modulations were tuning specific, selectively suppressing neural populations tuned toward the expected input. Combined, our results suggest that recently acquired conceptual priors are generalized across domains and used by the sensory brain to generate category-specific predictions, facilitating processing of expected visual input.SIGNIFICANCE STATEMENT Perceptual predictions play a crucial role in facilitating perception and the integration of sensory information. However, little is known about whether and how the brain uses more abstract, conceptual priors to form sensory predictions. In our preregistered study, we show that priors derived from recently acquired arbitrary conceptual associations result in category-specific predictions that modulate perceptual processing throughout the ventral visual hierarchy, including early visual cortex. These results suggest that the predictive brain uses prior knowledge across various domains to modulate perception, thereby extending our understanding of the extensive role predictions play in perception.

Project description:INTRODUCTION:Patients admitted to a critical care medicine (CCM) environment, including an intensive care unit (ICU), are susceptible to harm and significant resource utilisation. Therefore, a strategy to optimise provider performance is required. Performance scorecards are used by institutions for the purposes of driving quality improvement. There is no widely accepted or standardised scorecard that has been used for overall CCM performance. We aim to improve quality of care, patient safety and patient/family experience in CCM practice through the utilisation of a standardised, repeatable and multidimensional performance scorecard, designed to provide a continuous review of ICU physician and nurse practice, as well as departmental metrics. METHODS AND ANALYSIS:This will be a mixed-methods, controlled before and after study to assess the impact of a CCM-specific quality scorecard. Scorecard metrics were developed through expert consensus and existing literature. The study will include 19 attending CCM physicians and approximately 300 CCM nurses. Patient data for scorecard compilation are collected daily from bedside flow sheets. Preintervention baseline data will be collected for 6 months for each participant. After this, each participant will receive their scorecard measures. Following a 3-month washout period, postintervention data will be collected for 6 months. The primary outcome will be change in performance metrics following the provision of scorecard feedback to subjects. A cost analysis will also be performed, with the purpose of comparing total ICU costs prior to implementation of the scorecard with total ICU costs following implementation of the scorecard. The qualitative portion will include interviews with participants following the intervention phase. Interviews will be analysed in order to identify recurrent themes and subthemes, for the purposes of driving scorecard improvement. ETHICS AND DISSEMINATION:This protocol has been approved by the local research ethics board. Publication of results is anticipated in 2019. If this intervention is found to improve patient- and unit-directed outcomes, with evidence of cost-effectiveness, it would support the utilisation of such a scorecard as a quality standard in CCM.

Dataset Information

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

Publications

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

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