Clinical Performance of Decision Support Systems in Anesthesia, Intensive Care, and Emergency Medicine: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Medical technology is expanding at an alarming rate, with its integration into health care often reflected by the constant evolution of best practices. This rapid expansion of available treatment modalities, when coupled with progressively increasing amounts of consequential data for health care professionals to manage, creates an environment where complex and timely decision-making without the aid of technology is inconceivable. Decision support systems (DSSs) were, therefore, developed as a means of supporting the clinical duties of health care professionals through immediate point-of-care referencing. The integration of DSS can be especially useful in critical care medicine, where the combination of complex pathologies, the multitude of parameters, and the general state of patients require swift informed decision-making. The systematic review and meta-analysis were performed to evaluate DSS outcomes compared to the standard of care (SOC) in critical care medicine. METHODS: This systematic review and subsequent meta-analysis were performed after the EQUATOR networks Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA). We systematically explored PubMed, Ovid, Central, and Scopus for randomized controlled trials (RCTs) from January 2000 to December 2021. The primary outcome of this study was to evaluate whether DSS is more effective than SOC practice in critical care medicine within the following disciplines: anesthesia, emergency department (ED), and intensive care unit (ICU). A random-effects model was used to estimate the effect of DSS performance, with 95% confidence intervals (CIs) in both continuous and dichotomous results. Outcome-based, department-specific, and study-design subgroup analyses were performed. RESULTS: A total of 34 RCTs were included for analysis. In total, 68,102 participants received DSS intervention, while 111,515 received SOC. Analysis of the continuous (standardized mean difference [SMD], -0.66; 95% CI [-1.01 to -0.30]; P < .01) and binary outcomes (odds ratio [OR], 0.64; 95% CI, [0.44-0.91]; P < .01) was statistically significant and suggests that health interventions are marginally improved with DSS integration in comparison to SOC in critical care medicine. Subgroup analysis in anesthesia (SMD, -0.89; 95% CI, [-1.71 to -0.07]; P < .01) and ICU (SMD, -0.63; 95% CI [-1.14 to -0.12]; P < .01) were deemed statistically supportive of DSS in improving outcome, with evidence being indeterminate in the field of emergency medicine (SMD, -0.24; 95% CI, [-0.71 to 0.23]; P < .01). CONCLUSIONS: DSSs were associated with a beneficial impact in critical care medicine on a continuous and binary scale; however, the ED subgroup was found to be inconclusive. Additional RCTs are required to determine the effectiveness of DSS in critical care medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.095 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.056 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".