Robot-assisted versus laparoscopic gastric bypass: comparison of short-term outcomes.

BACKGROUND: Roux-en-Y gastric bypass is an effective treatment for severe obesity and obesity-related comorbidities. Presently, gastric bypass is performed most often laparoscopically, although a robotic-assisted procedure is the preferred approach for an increasing number of bariatric surgeons. METHODS: This retrospective study compared the results of 100 Roux-en-Y gastric bypass operations using the da Vinci robot and 100 laparoscopic Roux-en-Y gastric bypasses performed laparoscopically. Short-term outcomes were determined by evaluating mortality, length of stay, length of operation, return to the operating room within 90 days of operation, conversions to open procedure, leaks, strictures, transfusions, and hospital readmissions. RESULTS: There was no mortality, pulmonary embolus, or conversion to open procedure in either group. Both the laparoscopic and robotic operative times decreased progressively, although the robotic operation time was longer (mean, 144 versus 87 min, P < 0.001). The length of stay was shorter for the robotic-assisted group (37 versus 52 h, P < 0.001), and 60% of these patients were discharged after one night's stay (P < 0.001). There were fewer transfusions (P = 0.005) and readmissions (P = .560) in the robotic group. The stricture rate was higher in the first 50 robotic procedures (17 mm gastrotomy) but resolved in the second 50 procedures (21 mm gastrotomy). There was no difference in the rate of leak and return to the operating room between groups (both P > 0.05). CONCLUSIONS: These results indicate that Roux-en-Y gastric bypass can be performed safely with robotic assistance, even during the first 100 cases.

Supporting trust calibration and the effective use of decision aids by presenting dynamic system confidence information.

OBJECTIVE: To examine whether continually updated information about a system's confidence in its ability to perform assigned tasks improves operators' trust calibration in, and use of, an automated decision support system (DSS). BACKGROUND: The introduction of decision aids often leads to performance breakdowns that are related to automation bias and trust miscalibration. This can be explained, in part, by the fact that operators are informed about overall system reliability only, which makes it impossible for them to decide on a case-by-case basis whether to follow the system's advice. METHOD: The application for this research was a neural net-based decision aid that assists pilots with detecting and handling in-flight icing encounters. A multifactorial experiment was carried out with two groups of 15 instructor pilots each flying a series of 28 approaches in a motion-base simulator. One group was informed about the system's overall reliability only, whereas the other group received updated system confidence information. RESULTS: Pilots in the updated group experienced significantly fewer icing-related stalls and were more likely to reverse their initial response to an icing condition when it did not produce desired results. Their estimate of the system's accuracy was more accurate than that of the fixed group. CONCLUSION: The presentation of continually updated system confidence information can improve trust calibration and thus lead to better performance of the human-machine team. APPLICATION: The findings from this research can inform the design of decision support systems in a variety of event-driven high-tempo domains.