scholarly journals Robust parameter extraction for decision support using multimodal intensive care data

2008 ◽  
Vol 367 (1887) ◽  
pp. 411-429 ◽  
Author(s):  
G.D Clifford ◽  
W.J Long ◽  
G.B Moody ◽  
P Szolovits
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
Information Flow ◽  
Clinical Decision ◽  
Parameter Extraction ◽  
High Dimensional ◽  
Digital Information ◽  
Prior Probabilities ◽  
Recent Developments ◽  
Robust Parameter

Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in the integration and archiving of these data have resulted in new opportunities for data analysis and clinical feedback. New problems associated with ICU databases have also arisen. ICU data are high-dimensional, often sparse, asynchronous and irregularly sampled, as well as being non-stationary, noisy and subject to frequent exogenous perturbations by clinical staff. Relationships between different physiological parameters are usually nonlinear (except within restricted ranges), and the equipment used to measure the observables is often inherently error-prone and biased. The prior probabilities associated with an individual's genetics, pre-existing conditions, lifestyle and ongoing medical treatment all affect prediction and classification accuracy. In this paper, we describe some of the key problems and associated methods that hold promise for robust parameter extraction and data fusion for use in clinical decision support in the ICU.

scholarly journals Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

2021 ◽  
Vol 11 (6) ◽  
pp. 2880
Author(s):  
Miguel Pereira ◽  
Patricia Concheiro-Moscoso ◽  
Alexo López-Álvarez ◽  
Gerardo Baños ◽  
Alejandro Pazos ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Intensive Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Web Of Science ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Real Time Information ◽  
Time Information ◽  
Improve Patient

The advances achieved in recent decades regarding cardiac surgery have led to a new risk that goes beyond surgeons' dexterity; postoperative hours are crucial for cardiac surgery patients and are usually spent in intensive care units (ICUs), where the patients need to be continuously monitored to adjust their treatment. Clinical decision support systems (CDSSs) have been developed to take this real-time information and provide clinical suggestions to physicians in order to reduce medical errors and to improve patient recovery. In this review, an initial total of 499 papers were considered after identification using PubMed, Web of Science, and CINAHL. Twenty-two studies were included after filtering, which included the deletion of duplications and the exclusion of titles or abstracts that were not of real interest. A review of these papers concluded the applicability and advances that CDSSs offer for both doctors and patients. Better prognosis and recovery rates are achieved by using this technology, which has also received high acceptance among most physicians. However, despite the evidence that well-designed CDSSs are effective, they still need to be refined to offer the best assistance possible, which may still take time, despite the promising models that have already been applied in real ICUs.

Physicians’ responses to clinical decision support on an intensive care unit—Comparison of four different alerting methods

2013 ◽  
Vol 59 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Anne-Marie J. Scheepers-Hoeks ◽  
Rene J. Grouls ◽  
Cees Neef ◽  
Eric W. Ackerman ◽  
Erik H. Korsten
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision

scholarly journals Prospective evaluation of medication-related clinical decision support over-rides in the intensive care unit

2018 ◽  
Vol 27 (9) ◽  
pp. 718-724 ◽  
Author(s):  
Adrian Wong ◽  
Mary G Amato ◽  
Diane L Seger ◽  
Christine Rehr ◽  
Adam Wright ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Adverse Drug Events ◽  
Clinical Decision ◽  
Limited Data ◽  
Exact Test ◽  
Increased Risk ◽  
Adjusted Logistic Regression

BackgroundClinical decision support (CDS) displayed in electronic health records has been found to reduce the incidence of medication errors and adverse drug events (ADE). Recent data suggested that medication-related CDS alerts were frequently over-ridden, often inappropriately. Patients in the intensive care unit (ICU) are at an increased risk of ADEs; however, limited data exist on the benefits of CDS in the ICU. This study aims to evaluate potential harm associated with medication-related CDS over-rides in the ICU.MethodsThis was a prospective observational study of adults admitted to any of six ICUs between July 2016 and April 2017 at our institution. Patients with provider-overridden CDS for dose (orders for scheduled frequency and not pro re nata), drug allergy, drug–drug interaction, geriatric and renal alerts (contraindicated medications for renal function or renal dosing) were included. The primary outcome was the appropriateness of over-rides, which were evaluated by two independent reviewers. Secondary outcomes included incidence of ADEs following alert over-ride and risk of ADEs based on over-ride appropriateness.ResultsA total of 2448 over-ridden alerts from 712 unique patient encounters met inclusion criteria. The overall appropriateness rate for over-rides was 81.6% and varied by alert type. More ADEs (potential and definite) were identified following inappropriate over-rides compared with appropriate over-rides (16.5 vs 2.74 per 100 over-ridden alerts, Fisher’s exact test P<0.001). An adjusted logistic regression model showed that inappropriate over-rides were associated with an increased risk of ADEs (OR 6.14, 95% CI 4.63 to 7.71, P<0.001).ConclusionsApproximately four of five identified CDS over-rides were appropriately over-ridden, with the rate varying by alert type. However, inappropriate over-rides were six times as likely to be associated with potential and definite ADEs, compared with appropriate over-rides. Further efforts should be targeted at improving the positive predictive value of CDS such as by suppressing alerts that are appropriately over-ridden.

Sign in / Sign up

Export Citation Format

Share Document