Mestrado Integrado em Medicina
Introdução à Medicina II
Turma 6
6fmup0910@gmail.com
06-04-2010
Professor
Doutor Altamiro Pereira
ARTIFICIAL INTELLIGENCE FORCRITICAL CARE MONITORING ANDDECISION SUPPORT
Third Presentation
IMPACT ON PATIENT OUTCOMES
Does the usage of monitoring or clinicaldecision support systems that include AItechnology improve the quality of patientcare?
Professor
Doutor Altamiro Pereira
06-04-2010
Research question
Turma 6
6fmup0910@gmail.com
To review the usefulness of AI monitoring andCDS systems when applied to patients in theICU by the interpretation of the patientoutcomes
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Aim
•To study the benefits and drawbacks ofartificial intelligence monitoring and CDSsystems for critical care when compared tonon AI-methods.
•To find out the impact of the usage of AIsystems in the different patient outcomes inICU's.
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Specific Objectives
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Methods
Study design:
Systematic review
1-An exhaustive search, in electronic databases, and inclusion of primarystudies.
2-Quality assessment of included studies and data extraction (review, by twopersons, of the title and the abstract or the article. Same process for the fullarticle. A third opinion may be requested).
3-Synthesis of study results (SPSS and Review Manager).
4-Interpretation of results and report writing.
Articles which report AIapplications for monitoring(including warning (alert)),decision support or prescriptionsupport in the intensive careunit.
Data collection methods:
→ Search strategy
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Methods
Articles included byreviewer
Articles are searched in:
PubMed;
ISI Web of Knowledge;
SCOPUS.
The query is based in the following keywords: AI and
(critical care or ICU) and Trial
With no date restriction
Query terms:
Artificial intelligence: Computer reasoning, machine intelligence, machinelearning, computer vision system, knowledge acquisition, fuzzy logic, expert systems,knowledge bases, neural networks (computer), neural network model, perceptron, directsupport system, robotic, telerobotic;
Intensive care unit: Critical care (unit), surgical intensive care (unit), neonatalintensive care (unit), infant newborn intensive care (unit), pediatric intensive care (unit),ICU, PICU, NICU, CC, burn(s) unit, respiratory care unit, coronary care unit.
We also used the term “trial” in order to narrow down and specify our research.
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Methods
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Methods
Inclusion/ exclusion criteria:
Inclusion criteria
1.Study design (clinical trials, cohort or case-control);
2.Study participants of included articles are patients in the intensive careunit;
3.Studies that describe AI systems’ intervention on monitoring, warning(alert), decision support or prescription support;
4. Study outcomes include mortality, morbidity, quality of life, length of stayor other patient outcomes.
Exclusion criteria
Articles that use data from the ICU as secondary data for the demonstration ofAI systems based only on system's performance outcomes.
Data collection methods:
→ Validity assessment: Consort
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Methods
To assess the validity of our articles, we will use the Consort, whichstands for:
CONsolidated Standards of Reporting Trials
It also enables a greaterlevel of understanding by thereaders of the RCT’sanalysed by this method.
This statement helps authorsin designing RCT’s (byfollowing a group ofguidelines and a flowchart).
Study variables:
•Characteristics of the articles (year, author and country ofpublishing, etc…)
•Type of study (number of participants, duration, etc…)
•Domain of application (neurological, respiratory,cardiovascular, etc…)
•Area of application (monitoring, clinical decision support.)
•The patients'outcomes described in each article (mortalityrate, length of stay, quality of stay, morbidity, quality of life,cost of stay or other patient outcomes).
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Methods
Statistical analysis:
•Analysis of the study variables using the appropriatefrequency measures;
•Possible associative analysis between factors andoutcomes in the cases in which such aspects are included,resorting to adequate association measures.
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Methods
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Search Results
Search wasconducted onthe 7th ofMarch, 2010
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Abstracts' review
28 articles: Included
(5 articles were excluded due to absence ofabstract)
Number of third revisions (number ofdisagreements between reviewers): 27
222 articles: excluded
Professor
Doutor Altamiro Pereira
Turma 6
6fmup0910@gmail.com
06-04-2010
Reviewer Heterogeinity
To evaluate if there is anassociation between theapproval/rejection of acertain article and a certainreviewer, we proceeded todo a Chi-squared test.
The result shows that there is anassociation.
(Chi-squared test p=0,038).
However, analysing the data, we also concluded that the reviewersagreed over 89% of the cases, even though the value is lower (60.7%)for included articles.
Possible causes:
•Misunderstand of the criteria
•Certain reviewers being more inclined to reject/approve articles
