Summary international symposium on computer assisted decision support and database management in anesthesia, intensive care, and cardio-pulmonary medicine

1989 ◽  
Vol 6 (1) ◽  
pp. 1-6
Author(s):  
D. F. Sittig ◽  
R. M. Gardner
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
International Symposium ◽  
Database Management ◽  
Computer Assisted ◽  
Pulmonary Medicine

Abstracts from the 1st International Symposium on Decision Support in Anaesthesia and Intensive Care

1997 ◽  
Vol 14 (1) ◽  
pp. 49-68
Author(s):  
D. Streifert ◽  
N. Lutter ◽  
E. Van der Vorst ◽  
J. Mulier ◽  
B. Schwilk ◽  
...  
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
International Symposium

Computer-assisted decision support for the diagnosis and treatment of infectious diseases in intensive care units

2005 ◽  
Vol 5 (5) ◽  
pp. 305-312 ◽  
Author(s):  
CAM Schurink ◽  
PJF Lucas ◽  
IM Hoepelman ◽  
MJM Bonten
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
Infectious Diseases ◽  
Intensive Care Units ◽  
Diagnosis And Treatment ◽  
Computer Assisted

An Intelligent Computer-Assisted Instruction System Designed for Rural Health Workers in Developing Countries

1992 ◽  
Vol 31 (03) ◽  
pp. 193-203 ◽  
Author(s):  
B. Auvert ◽  
V. Gilbos ◽  
F. Andrianiriana ◽  
W. E. Bertrand ◽  
X. Emmanuelli ◽  
...  
Keyword(s):  
Developing Countries ◽  
Decision Support ◽  
Rural Health ◽  
Health Workers ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Instruction System ◽  
Knowledge Based ◽  
Assisted Instruction ◽  
Intelligent Computer

Abstract:This paper describes an intelligent computer-assisted instruction system that was designed for rural health workers in developing countries. This system, called Consult-EAO, includes an expert module and a coaching module. The expert module, which is derived from the knowledge-based decision support system Tropicaid, covers most of medical practice in developing countries. It allows for the creation of outpatient simulations without the help of a teacher. The student may practice his knowledge by solving problems with these simulations. The system gives some initial facts and controls the simulation during the session by guiding the student toward the most efficient decisions. All student answers are analyzed and, if necessary, criticized. The messages are adapted to the situation due to the pedagogical rules of the coaching module. This system runs on PC-compatible computer.

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.

Creation of an Open Framework for Point-of-Care Computer-Assisted Reporting and Decision Support Tools for Radiologists

2017 ◽  
Vol 14 (9) ◽  
pp. 1184-1189 ◽  
Author(s):  
Tarik K. Alkasab ◽  
Bernardo C. Bizzo ◽  
Lincoln L. Berland ◽  
Sujith Nair ◽  
Pari V. Pandharipande ◽  
...  
Keyword(s):  
Decision Support ◽  
Point Of Care ◽  
Computer Assisted ◽  
Decision Support Tools ◽  
Open Framework ◽  
Support Tools

Abstract P263: Computer-Assisted Decision Support in the Treatment of Acute Coronary Syndromes and Impact on Cardiovascular Outcomes

2011 ◽  
Vol 4 (suppl_1) ◽  
Author(s):  
Richard V Milani ◽  
Carl J Lavie ◽  
Daniel P Morin ◽  
Andres Rubiano
Keyword(s):  
Quality Of Care ◽  
Decision Support ◽  
Baseline Risk ◽  
Risk Scores ◽  
Computer Assisted ◽  
Improve Quality ◽  
Consensus Guidelines ◽  
Mortality And Morbidity ◽  
Coronary Syndrome

Background: Evidence from clinical trials and consensus guidelines suggest that in-hospital initiation of key therapeutics can reduce mortality and morbidity in patients admitted with acute coronary syndrome (ACS). As a result, the AHA and ACC have co-developed guideline-based “performance measures” for ACS patients, such that when every measure has been performed, the patient is considered to have achieved optimal or “perfect” care (PC). Computer-assisted decision support (CADS) is a tool that can improve quality of care and is well suited for complex algorithms governing treatment decisions. We sought to determine if CADS tailored to ACS would enhance the likelihood of achieving PC, and whether achievement of PC would translate into reduced mortality. Methods: 452 consecutive patients (mean age 68±13 years) admitted with ACS in 2009 were evaluated (unstable angina 29%, NSTEMI 61%, STEMI 10%). Physicians had the option of using either pre-printed ACS orders (standard orders) versus CADS generated orders. The CADS system utilized patient clinical data including risk scoring, to suggest specific therapeutics and drug dosing based on consensus guidelines. Endpoints were attainment of PC and 30-day mortality. Results: The 77 patients admitted using CADS generated orders were statistically similar (age, gender, ACS diagnosis, TIMI risk) to the 375 patients admitted with the standard order set. Attainment of PC was almost twice as likely when using CADS versus standard orders (84% vs. 44%, p<0.05). PC patients trended towards higher TIMI risk scores (3.2 ±1.7 vs 2.9 ±1.6, p = 0.09) but had half the 30-day mortality (2% vs 4%, p=0.05) compared to patients not achieving PC. Conclusions: Use of CADS in the setting of ACS is feasible and doubles the likelihood of attaining PC. Although patients achieving PC had higher baseline risk, their mortality was reduced by 50% compared to those not achieving PC. These data support the use of CADS in the setting of ACS to improve quality of care and subsequent outcomes.

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