OBJECTIVE: Authors developed a picture-graphics display for pulmonary function to present typical respiratory data used in perioperative and intensive care environments. The display utilizes color, shape and emergent alerting to highlight abnormal pulmonary physiology. The display serves as an adjunct to traditional operating room displays and monitors. DESIGN: To evaluate the prototype, nineteen clinician volunteers each managed four adverse respiratory events and one normal event using a high-resolution patient simulator which included the new displays (intervention subjects) and traditional displays (control subjects). Between-group comparisons included (i) time to diagnosis and treatment for each adverse respiratory event; (ii) the number of unnecessary treatments during the normal scenario; and (iii) self-reported workload estimates while managing study events. MEASUREMENTS: Two expert anesthesiologists reviewed video-taped transcriptions of the volunteers to determine time to treat and time to diagnosis. Time values were then compared between groups using a Mann-Whitney-U Test. Estimated workload for both groups was assessed using the NASA-TLX and compared between groups using an ANOVA. P-values < 0.05 were considered significant. RESULTS: Clinician volunteers detected and treated obstructed endotracheal tubes and intrinsic PEEP problems faster with graphical rather than conventional displays (p < 0.05). During the normal scenario simulation, 3 clinicians using the graphical display, and 5 clinicians using the conventional display gave unnecessary treatments. Clinician-volunteers reported significantly lower subjective workloads using the graphical display for the obstructed endotracheal tube scenario (p < 0.001) and the intrinsic PEEP scenario (p < 0.03). CONCLUSION: Authors conclude that the graphical pulmonary display may serve as a useful adjunct to traditional displays in identifying adverse respiratory events.
OBJECTIVE: Authors developed a picture-graphics display for pulmonary function to present typical respiratory data used in perioperative and intensive care environments. The display utilizes color, shape and emergent alerting to highlight abnormal pulmonary physiology. The display serves as an adjunct to traditional operating room displays and monitors. DESIGN: To evaluate the prototype, nineteen clinician volunteers each managed four adverse respiratory events and one normal event using a high-resolution patient simulator which included the new displays (intervention subjects) and traditional displays (control subjects). Between-group comparisons included (i) time to diagnosis and treatment for each adverse respiratory event; (ii) the number of unnecessary treatments during the normal scenario; and (iii) self-reported workload estimates while managing study events. MEASUREMENTS: Two expert anesthesiologists reviewed video-taped transcriptions of the volunteers to determine time to treat and time to diagnosis. Time values were then compared between groups using a Mann-Whitney-U Test. Estimated workload for both groups was assessed using the NASA-TLX and compared between groups using an ANOVA. P-values < 0.05 were considered significant. RESULTS: Clinician volunteers detected and treated obstructed endotracheal tubes and intrinsic PEEP problems faster with graphical rather than conventional displays (p < 0.05). During the normal scenario simulation, 3 clinicians using the graphical display, and 5 clinicians using the conventional display gave unnecessary treatments. Clinician-volunteers reported significantly lower subjective workloads using the graphical display for the obstructed endotracheal tube scenario (p < 0.001) and the intrinsic PEEP scenario (p < 0.03). CONCLUSION: Authors conclude that the graphical pulmonary display may serve as a useful adjunct to traditional displays in identifying adverse respiratory events.
Authors: S Blake Wachter; Jim Agutter; Noah Syroid; Frank Drews; Matthew B Weinger; Dwayne Westenskow Journal: J Am Med Inform Assoc Date: 2003-03-28 Impact factor: 4.497
Authors: Thomas J Reese; Guilherme Del Fiol; Joseph E Tonna; Kensaku Kawamoto; Noa Segall; Charlene Weir; Brekk C Macpherson; Polina Kukhareva; Melanie C Wright Journal: J Am Med Inform Assoc Date: 2020-08-01 Impact factor: 4.497
Authors: Lukasz M Mazur; Prithima R Mosaly; Carlton Moore; Elizabeth Comitz; Fei Yu; Aaron D Falchook; Michael J Eblan; Lesley M Hoyle; Gregg Tracton; Bhishamjit S Chera; Lawrence B Marks Journal: J Am Med Inform Assoc Date: 2016-03-28 Impact factor: 4.497
Authors: Jason J Saleem; Emily S Patterson; Laura Militello; Shilo Anders; Mercedes Falciglia; Jennifer A Wissman; Emilie M Roth; Steven M Asch Journal: J Am Med Inform Assoc Date: 2007-06-28 Impact factor: 4.497