Abstract
Background: We hypothesized an electronic platform adapted to a hand-held tablet receiving real time ventilatory parameters and containing an efficient hand-off checklist (RT Assistant ) could assist registered respiratory therapists (RRTs) in maintaining established lung protective ventilation (LPV) parameters (plateau pressure [Pplt] < 30 cm H2O and tidal volume [VT] 4 to 8 mL/kg predicated body weight [PBW]) and improve the quality of shift reports for ventilator-dependent patients.
Methods: Eight experienced RRTs managed four simulated patients with respiratory failure scenarios from various etiologies during a four-hour shift without RT Assistant (Control Group [CG]) and later with RT Assistant (Experimental Group [EG]). To reduce learning bias, the EG scenarios were similar in the care required but disguised with different demographics and interventions. We assessed the compliance to LPV standards and the quality of shift reports (comprehensiveness). RRTs completed questionnaires on the use (efficiency and satisfaction) of RT Assistant. Wilcoxon Signed Rank Tests were used; alpha was set at 0.05.
Results: In the experimental group, RTs controlled the patients better (less time outside of the LPV guidelines) and provided improved shift reports (more information transmitted and higher quality). On average, usability of RT Assistant was rated 1.5 on a Likert scale from 1 (excellent) to 5 (worst). RRTs compliance with LPV guidelines was significantly improved in the EG.
Conclusions: Real time LPV situational awareness was significantly improved by using RT assistant than without it, leading to better compliance with LPV standards. Furthermore, for the same shift report time (including preparation and presentation time), the use of RT Assistant resulted in a significantly greater quality of shift reports.
Footnotes
Commercial Relationships: The software used during this study was developed at Convergent Engineering. Dr. Euliano is the President and Founder of Convergent Engineering.
Support: This study was funded by an NIH SBIR grant 1R43HL146012-01A1
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