Reducing ICU blood draws with artificial intelligence

Cismondi, FC; Fialho, AS; Sm, Vieira; Celi, LA; Reti, SR; Sousa, J. R. C. De; Finkelstein, SN

doi:10.1186/cc11043

Cited by 4 publications

(4 citation statements)

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“… 16-bed; n = 229 PD N/S Education (prices information via emails); A&F (number of tests ordered via emails) Pre-I: 10 mo Per-I: 3 mo Post-I: 9 days 39% reduction in inappropriate tests for critical patients; no statistical significance for semi-critical patients N/S N/S Cahill et al [ 41 ] 2018 Retrospective BAA (C.A.) N/S N/S Education (iatrogenic anemia focus culture); Guidance (locally established) Pre-I: 11 mo Post-I: 11 mo 23% reduction in laboratory orders; 21% reduction in blood specimens; 23% reduction in POCT specimens N/S No increase in LOS nor transfusion need Castellanos et al [ 84 ] 2018 Prospective ITS 25-bed PCT CPOE (clinical decision support system implementation) Pre-I: 4 mo Per-I: 4 consecutives periods of 28 days (ON1-OFF1-ON2-OFF2) Post-I: 28 days 0.807 TPD on Pre-I (= baseline), 0.662 (−18%) on ON1, 0.733 (−10%/baseline) on OFF1, 0.803 (−0.4%/baseline) on ON2, 0.792 (−2%/baseline) on OFF2, 0.807 (+ 0%/baseline) in Post-I EUR 15000 (/y) if persistence of scenario “ON1” N/S Cismondi et al [ 98 ] 2012 Database MIMIC-II database version 2.6; n = 40,426 patients HCT, HB, PLT, CA, LACT, aPTT, INR/PT, FIB AI (TS fuzzy modeling; inputs: heart rate, respiratory rate, oxygen saturation, temperature, arterial blood pressure, urine output, intravenous infusions volumes and packed red blood cells, fresh frozen plasma, and platelets transfusions) N/A Reduction in 50% of total amount of tests; 11.5% false negatives (= tests that would not be done following algorithm but were in fact appropriate) N/S N/A ...…”

Section: Interventions To Improve Laboratory Testing Appropriateness ...mentioning

confidence: 99%

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Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review

Devis,

Catry,

Honore

et al. 2024

Ann. Intensive Care

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Healthcare expenses are increasing, as is the utilization of laboratory resources. Despite this, between 20% and 40% of requested tests are deemed inappropriate. Improper use of laboratory resources leads to unwanted consequences such as hospital-acquired anemia, infections, increased costs, staff workload and patient stress and discomfort. The most unfavorable consequences result from unnecessary follow-up tests and treatments (overuse) and missed or delayed diagnoses (underuse). In this context, several interventions have been carried out to improve the appropriateness of laboratory testing. To date, there have been few published assessments of interventions specific to the intensive care unit. We reviewed the literature for interventions implemented in the ICU to improve the appropriateness of laboratory testing. We searched literature from 2008 to 2023 in PubMed, Embase, Scopus, and Google Scholar databases between April and June 2023. Five intervention categories were identified: education and guidance (E&G), audit and feedback, gatekeeping, computerized physician order entry (including reshaping of ordering panels), and multifaceted interventions (MFI). We included a sixth category exploring the potential role of artificial intelligence and machine learning (AI/ML)-based assisting tools in such interventions. E&G-based interventions and MFI are the most frequently used approaches. MFI is the most effective type of intervention, and shows the strongest persistence of effect over time. AI/ML-based tools may offer valuable assistance to the improvement of appropriate laboratory testing in the near future. Patient safety outcomes are not impaired by interventions to reduce inappropriate testing. The literature focuses mainly on reducing overuse of laboratory tests, with only one intervention mentioning underuse. We highlight an overall poor quality of methodological design and reporting and argue for standardization of intervention methods. Collaboration between clinicians and laboratory staff is key to improve appropriate laboratory utilization. This article offers practical guidance for optimizing the effectiveness of an intervention protocol designed to limit inappropriate use of laboratory resources.

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Section: Interventions To Improve Laboratory Testing Appropriateness ...mentioning

confidence: 99%

“…Cismondi et al . [ 97 , 98 ] applied fuzzy systems algorithms on patients hospitalized in the ICU for gastrointestinal (GI) bleeding with an input of 11 physiological variables (such as heart rate, temperature, oxygen saturation, urine output, etc . ).…”

Section: Interventions To Improve Laboratory Testing Appropriateness ...mentioning

confidence: 99%

Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review

Devis,

Catry,

Honore

et al. 2024

Ann. Intensive Care

View full text Add to dashboard Cite

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“…Previous work on multivariate time series classification functions under the assumption that the signals are aligned and collected at the same time steps, with missing values being imputed through simple methods such as mean-fitting and interpolation [Kreindler and Lumsden, 2016] or more complex procedures such as Expectation Maximization (EM) [García-Laencina et al, 2010], multiple imputation [Galimard et al, 2016], resampling [Cismondi et al, 2013] and kernel methods [Rehfeld et al, 2011]. These methods are usually easy to implement and can yield good results when datasets have few missing values, but perform poorly when the dataset is too sparse [Che et al, 2018].…”

Section: Related Workmentioning

confidence: 99%

Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)

Singh¹,

Deznabi²,

Narasimhan³

et al. 2019

Preprint

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Missing values, irregularly collected samples, and multi-resolution signals commonly occur in multivariate time series data, making predictive tasks difficult. These challenges are especially prevalent in the healthcare domain, where patients vital signs and electronic records are collected at different frequencies and have occasionally missing information due to the imperfections in equipment or patient circumstances. Researchers have handled each of these issues differently, often handling missing data through mean value imputation and then using sequence models over the multivariate signals while ignoring the different resolution of signals. We propose a unified model named Multi-resolution Flexible Irregular Time series Network (Multi-FIT). The building block for Multi-FIT is the FIT network. The FIT network creates an informative dense representation at each time step using signal information such as last observed value, time difference since the last observed time stamp and overall mean for the signal. Vertical FIT (FIT-V) is a variant of FIT which also models the relationship between different temporal signals while creating the informative dense representations for the signal. The multi-FIT model uses multiple FIT networks for sets of signals with different resolutions, further facilitating the construction of flexible representations. Our model has three main contributions: a.) it does not impute values but rather creates informative representations to provide flexibility to the model for creating taskspecific representations b.) it models the relationship between different signals in the form of support signals c.) it models different resolutions in parallel before merging them for the final prediction task. The FIT, FIT-V and Multi-FIT networks improve upon the state-of-the-art models for three predictive tasks, including the forecasting of patient survival. * Equal contribution, randomly ordered.

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“…Ongoing examples of our current investigations include the following: prediction of which patients with hypotension will respond to fluid resuscitation and which ones will proceed to develop multiorgan failure; Markov modeling to determine the proper duration for a trial of aggressive ICU care among high-risk patients (21); and fuzzy modeling to predict whether gastrointestinal bleeding will stop with conservative treatment alone or requires an endoscopic or surgical intervention. Artificial intelligence methods have also been used with the MIMIC database to predict whether a laboratory test is significantly changed from the last determination by modeling the treatments and the physiologic response during the interim period among patients who presented with gastrointestinal bleeding (22). The goal is to reduce unnecessary testing, which contributes to patient discomfort, use of staff time, iatrogenic anemia, increased laboratory costs, and medical errors that result from false-positive results.…”

Section: Creating Data-driven Tools Predictive Modeling Prognosticatmentioning

confidence: 99%

“Big Data” in the Intensive Care Unit. Closing the Data Loop

Celi

Mark

Stone

et al. 2013

Am J Respir Crit Care Med

160

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The data generated in the process of medical care has historically not just been underused, it has been wasted. This was due in part to the difficulty of accessing, organizing, and using data entered on paper charts, but notable variability in clinical documentation methods and quality made the problem even more challenging. In the absence of a practical way to systematically capture, analyze, and integrate the information contained in the massive amount of data generated during patient care, medicine has remained a highly empirical process in which the disconnected application of individual experiences and subjective preferences continues to thwart continuous improvement and consistent delivery of best practices to all patients.

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Reducing ICU blood draws with artificial intelligence

Cited by 4 publications

References 0 publications

Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review

Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review

Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)

“Big Data” in the Intensive Care Unit. Closing the Data Loop

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