RESUMO
Background: Artificial intelligence (AI) is reforming healthcare, particularly in respiratory medicine and critical care, by utilizing big and synthetic data to improve diagnostic accuracy and therapeutic benefits. This survey aimed to evaluate the knowledge, perceptions, and practices of respiratory therapists (RTs) regarding AI to effectively incorporate these technologies into the clinical practice. Methods: The study approved by the institutional review board, aimed at the RTs working in the Kingdom of Saudi Arabia. The validated questionnaire collected reflective insights from 448 RTs in Saudi Arabia. Descriptive statistics, thematic analysis, Fisher's exact test, and chi-square test were used to evaluate the significance of the data. Results: The survey revealed a nearly equal distribution of genders (51% female, 49% male). Most respondents were in the 20-25 age group (54%), held bachelor's degrees (69%), and had 0-5 years of experience (73%). While 28% had some knowledge of AI, only 8.5% had practical experience. Significant gender disparities in AI knowledge were noted (p < 0.001). Key findings included 59% advocating for basics of AI in the curriculum, 51% believing AI would play a vital role in respiratory care, and 41% calling for specialized AI personnel. Major challenges identified included knowledge deficiencies (23%), skill enhancement (23%), and limited access to training (17%). Conclusion: In conclusion, this study highlights differences in the levels of knowledge and perceptions regarding AI among respiratory care professionals, underlining its recognized significance and futuristic awareness in the field. Tailored education and strategic planning are crucial for enhancing the quality of respiratory care, with the integration of AI. Addressing these gaps is essential for utilizing the full potential of AI in advancing respiratory care practices.
RESUMO
BACKGROUND: Patient-ventilator asynchrony (PVA) is a prevalent and often underrecognized problem in mechanically ventilated patients. Ventilator waveform analysis is a noninvasive and reliable means of detecting PVAs, but the use of this tool has not been broadly studied. METHODS: Our observational analysis leveraged a validated evaluation tool to assess the ability of critical care practitioners (CCPs) to detect different PVA types as presented in three videos. This tool consisted of three videos of common PVAs (i.e., double-triggering, auto-triggering, and ineffective triggering). Data were collected via an evaluation sheet distributed to 39 hospitals among the various CCPs, including respiratory therapists (RTs), nurses, and physicians. RESULTS: A total of 411 CCPs were assessed; of these, only 41 (10.2%) correctly identified the three PVA types, while 92 (22.4%) correctly detected two types and 174 (42.3%) correctly detected one; 25.3% did not recognize any PVA. There were statistically significant differences between trained and untrained CCPs in terms of recognition (three PVAs, p < 0.001; two PVAs, p = 0.001). The majority of CCPs who identified one or zero PVAs were untrained, and such differences among groups were statistically significant (one PVA, p = 0.001; zero PVAs, p = 0.004). Female gender and prior training on ventilator waveforms were found to increase the odds of identifying more than two PVAs correctly, with odds ratios (ORs) (95% confidence intervals [CIs]) of 1.93 (1.07-3.49) and 5.41 (3.26-8.98), respectively. Profession, experience, and hospital characteristics were not found to correlate with increased odds of detecting PVAs; this association generally held after applying a regression model on the RT profession, with the ORs (95% CIs) of prior training (2.89 [1.28-6.51]) and female gender (2.49 [1.15-5.39]) showing the increased odds of detecting two or more PVAs. CONCLUSION: Common PVAs detection were found low in critical care settings, with about 25% of PVA going undetected by CCPs. Female gender and prior training on ventilator graphics were the only significant predictive factors among CCPs and RTs in correctly identifying PVAs. There is an urgent need to establish teaching and training programs, policies, and guidelines vis-à-vis the early detection and management of PVAs in mechanically ventilated patients, so as to improve their outcomes.